diff --git a/Algorithms/.gitignore b/Algorithms/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..5f2c97937124264fdebf3602664c58bc39290ee6
--- /dev/null
+++ b/Algorithms/.gitignore
@@ -0,0 +1,30 @@
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+.idea/
+
+# Generated files
+.idea/**/contentModel.xml
+
+.DS_Store
+__pycache__/
+
+
+# Extra
+./build
+./obj
+./Logs
+./Library
+./Temp
+./ProjectSettings
+
+
+# algorithms
+*.tour
+*.par
+*.tsp
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvChannel.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..89909eccb160d3bebb15087e72541ddbdd4ce2fd
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvChannel.py
@@ -0,0 +1,45 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel
+"""
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.EnvData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.EnvData.matrix = environment_info["matrixLayout"]
+ self.EnvData.rotation = environment_info["rotationRobot"]
+ self.EnvData.action_size = environment_info["sizeOfAction"]
+ self.EnvData.observation_size = environment_info["sizeOfObservation"]
+ self.EnvData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.EnvData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.EnvData.can_go_backwards = environment_info["backwards"]
+ self.EnvData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.EnvData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.EnvData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.EnvData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.EnvData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvironmentData.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..5156f445b07ce2351c4b0dcd5ea506e3135bf990
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/EnvironmentData.py
@@ -0,0 +1,92 @@
+# stores data on environment
+# initialized when initial data is being sent through EnvChannel
+import json
+import numpy as np
+
+from Node import Node
+
+import ta_task
+from ta_state import Location
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.action_size = None
+ self.observation_size = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+ self.task_side_channel = None
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # set_initial_nodes_dictionary
+ # set_tasks_to_be_assigned
+ # set_assigned_tasks
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Location(tasks[i]["PickUp"]["Column"],
+ tasks[i]["PickUp"]["Row"], 0)
+ delivery = Location(
+ tasks[i]["Delivery"]["Column"], tasks[i]["Delivery"]["Row"], 0)
+ releaseTime = tasks[i]["ReleaseTime"]
+ new_task = ta_task.Task(ta_task.CURRENT_TASK_ID, releaseTime,
+ pickup, delivery, None)
+ ta_task.CURRENT_TASK_ID += 1
+ self.tasks_to_be_assigned.append(new_task)
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ # info has to be sent to unity via the correct channel
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ self.tasks_to_be_assigned.remove(task)
+ taskDict = {"Task": {
+ "PickUp": {"Column": str(task.pickup_endpoint.col), "Row": str(task.pickup_endpoint.row)},
+ "Delivery": {"Column": str(task.delivery_endpoint.col), "Row": str(task.delivery_endpoint.row)},
+ "ReleaseTime": str(task.release_time)},
+ "InstanceID": int(robot_instanceID)}
+
+ self.task_side_channel.send_string(json.dumps(taskDict))
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH-2_USER_GUIDE.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH-2_USER_GUIDE.pdf
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH-3_REPORT.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH-3_REPORT.pdf
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH_Genetic.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/LKH_Genetic.pdf
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/POPMUSIC_REPORT.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/POPMUSIC_REPORT.pdf
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/TSPLIB_DOC.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/DOC/TSPLIB_DOC.pdf
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKH-3.exe b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKH-3.exe
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKHWin-3.0.6-WinChanges.zip b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKHWin-3.0.6-WinChanges.zip
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKH_linux b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKH_linux
new file mode 100644
index 0000000000000000000000000000000000000000..8a9ab0d5394332e166a8332d956afb089a077bfa
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/Makefile b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..4849d972907664b17c5bd9310455084e203789cd
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/Makefile
@@ -0,0 +1,4 @@
+all:
+ make -C SRC
+clean:
+ make -C SRC clean
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/README.txt b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/README.txt
new file mode 100644
index 0000000000000000000000000000000000000000..30c5f622fa2b23276bac3aa5d21169249fc738d5
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/README.txt
@@ -0,0 +1,146 @@
+LKH is an implementation of the Lin-Kernighan traveling salesman heuristic.
+
+The code is distributed for research use. The author reserves all rights to
+the code.
+
+
+INSTRUCTIONS FOR INSTALLATION: (Version 3.0.3 - July 2018)
+-----------------------------
+
+The software is available in gzipped tar format:
+
+ LKH-3.0.3.tgz (approximately 2.3 MB)
+
+Download the software and execute the following UNIX commands:
+
+ tar xvfz LKH-3.0.3.tgz
+ cd LKH-3.0.3
+ make
+
+An executable file called LKH will now be available in the directory LKH-3.0.3.
+
+To test the installation run the program by typing ./LKH pr2392.par.
+Then press return. The program should now solve a TSP instance with 2392 nodes.
+
+For testing the installation on an mTSP problem, type ./LKH whizzkids96.par.
+Then press return.
+
+A two-level tree is used as the default tour representation.
+A three-level tree representation may be used instead by compiling the
+source code with the compiler option
+
+ -DTHREE_LEVEL_TREE
+
+Just edit the first line in SRC/Makefile and execute the commands
+
+ make clean
+ make
+
+CHANGES IN VERSION 3.0.3:
+-------------------------
+
+Candidate sets may now be created by means of POPMUSIC by giving the following
+specification in the parameter file for LKH:
+
+ CANDIDATE_SET_TYPE = POPMUSIC
+
+The value of the parameter MAX_CANDIDATES is used to trim the candidate set.
+There are, however, some other POPMUSIC related parameters. If not specified,
+they will take their default values. These parameters are:
+
+ POPMUSIC_SAMPLE_SIZE = <int> Â
+ Sample size.
+ Default: 10.
+
+ POPMUSIC_SOLUTIONS = <int>Â
+ Number of solutions to generate.
+ Default: 50.
+
+ POPMUSIC_MAX_NEIGHBORS = <int>
+ Maximum number of nearest neighbors used as candidates in iterated 3-opt for
+ POPMUSIC.
+ Default: 5.
+
+ POPMUSIC_TRIALS = <int>
+ Number of trials used in iterated 3-opt for POPMUSIC.
+ If the value is zero, the number of trials is the size of the subpath
+ to be optimized.
+ Default: 1.
+
+ POPMUSIC_INITIAL_TOUR = { YES | NO }
+ Specifies whether the first generated POPMUSIC tour is used as
+ initial tour for Lin-Kernighan.
+ Default: NO.
+
+CHANGES IN VERSION 3.0.2:
+-------------------------
+
+Tours may now be recombined by GPX2 (Generalized Partition Crossover 2)
+instead of IPT (Iterative Partial Transcription).
+
+GPX2 is used by giving the following specification in the parameter file:
+
+ RECOMBINATION = GPX2
+
+The possible settings are:
+
+ RECOMBINATION = { IPT | GPX2 }
+
+IPT is default.
+
+
+CHANGES IN VERSION 3.0.1:
+-------------------------
+
+ New problem type: TSPDL (traveling salesman with draft limits)
+
+NEW IN VERSION 3.0:
+-------------------
+
+New parameter keywords:
+
+ BWTSP = <integer> <integer> [ <integer> ]
+ DEPOT = <integer>
+ MAKESPAN = { YES | NO } MTSP_MIN_SIZE = <integer>
+ MTSP_MAX_SIZE = <integer>
+ MTSP_OBJECTIVE = [ MINMAX | MINMAX_SIZE | MINSUM ]
+ MTSP_RESULT_FILE = <string>
+ SINTEF_RESULT_FILE = <string>
+ SALESMEN = <integer>
+ SCALE = <integer>
+ VEHICLES : <integer>
+
+ New initial tour algorithms:
+ CVRPR
+ MTSP
+ SOP
+
+New TSPLIB format keywords:
+
+The specification part:
+
+ CAPACITY : <integer>
+ DEMAND_DIMENSION : <integer>
+ DISTANCE : <real>
+ GRID_SIZE : <real>
+ RISK_THRESHOLD : <integer>
+ SALESMEN : <integer>
+ SCALE : <integer>
+ SERVICE_TIME_SECTION
+ VEHICLES : <integer>
+
+ New edge weight types:
+ EXACT_2D
+ EXACT_3D
+ FLOOR_2D
+ FLOOR_3D
+ TOR_2D
+ TOR_3D
+
+The data part:
+
+ BACKHAUL_SECTION
+ DEMAND_SECTION
+ DEPOT_SECTION
+ PICKUP_AND_DELIVERY_SECTION
+ TIME_WINDOW_SECTION
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/SRC.zip b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/SRC.zip
new file mode 100644
index 0000000000000000000000000000000000000000..4dd16c6a5d327c5bac3f7b15a7a7888013e22a2e
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Node.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2bb07c8fdf941ab68a8e8c87e8478b6d2e79aba
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Node.py
@@ -0,0 +1,39 @@
+# Class Node represent nodes in the graph. Every node contains a
+# position (x,y) in the grid
+# and a rotation in degree where
+# 0 = North
+# 90 = East
+# 280 = South
+# 270 = West
+# all degrees must be between 0 and 360
+
+# attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/PickupAndDeliveryChannel.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/PickupAndDeliveryChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7b40e6db4ea21d3a9eb72b29558fe4b9260ddfb
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/PickupAndDeliveryChannel.py
@@ -0,0 +1,36 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+"""
+class that processes information from SideChannel EnvChannel
+"""
+
+NEW_TASKS = []
+
+
+# Create the StringLogChannel class
+class PaDChannel(SideChannel):
+
+ def __init__(self) -> None:
+ # must coincide with ChannelId set in c#
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f8"))
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ print(f"Received info from side channel: {data}")
+ args = data.split(" ")
+ NEW_TASKS.append(args[1:])
+ print(f"New Task({args[1]}, {args[2]}, {args[3]})")
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..9b2afe2db3b43b974e3fc2244ddb1076119118dc
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile
@@ -0,0 +1,15 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+numpy = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile.lock b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile.lock
new file mode 100644
index 0000000000000000000000000000000000000000..b182fb88a1831f2730b6e59774e840f5498a278a
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Pipfile.lock
@@ -0,0 +1,528 @@
+{
+ "_meta": {
+ "hash": {
+ "sha256": "7d5bfdeb402549418fd529d32756528fb1c7c6815d55687f16e3784120edf6c8"
+ },
+ "pipfile-spec": 6,
+ "requires": {
+ "python_version": "3.6"
+ },
+ "sources": [
+ {
+ "name": "pypi",
+ "url": "https://pypi.org/simple",
+ "verify_ssl": true
+ }
+ ]
+ },
+ "default": {
+ "absl-py": {
+ "hashes": [
+ "sha256:62bd4e248ddb19d81aec8f9446b407ff37c8175c2ba88266a7afa9b4ce4a333b",
+ "sha256:6953272383486044699fd0e9f00aad167a27e08ce19aae66c6c4b10e7e767793"
+ ],
+ "version": "==0.13.0"
+ },
+ "attrs": {
+ "hashes": [
+ "sha256:149e90d6d8ac20db7a955ad60cf0e6881a3f20d37096140088356da6c716b0b1",
+ "sha256:ef6aaac3ca6cd92904cdd0d83f629a15f18053ec84e6432106f7a4d04ae4f5fb"
+ ],
+ "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+ "version": "==21.2.0"
+ },
+ "cached-property": {
+ "hashes": [
+ "sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130",
+ "sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0"
+ ],
+ "markers": "python_version < '3.8'",
+ "version": "==1.5.2"
+ },
+ "cachetools": {
+ "hashes": [
+ "sha256:2cc0b89715337ab6dbba85b5b50effe2b0c74e035d83ee8ed637cf52f12ae001",
+ "sha256:61b5ed1e22a0924aed1d23b478f37e8d52549ff8a961de2909c69bf950020cff"
+ ],
+ "markers": "python_version ~= '3.5'",
+ "version": "==4.2.2"
+ },
+ "cattrs": {
+ "hashes": [
+ "sha256:616972ae3dfa6e623a40ad3cb845420e64942989152774ab055e5c2b2f89f997",
+ "sha256:b7ab5cf8ad127c42eefd01410c1c6e28569a45a255ea80ed968511873c433c7a"
+ ],
+ "version": "==1.0.0"
+ },
+ "certifi": {
+ "hashes": [
+ "sha256:2bbf76fd432960138b3ef6dda3dde0544f27cbf8546c458e60baf371917ba9ee",
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diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Readme.md b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..27b0b6a9b6d66f7bb0a3362c55c262f091326e68
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/Readme.md
@@ -0,0 +1,94 @@
+# Task-Allocation and Prioritized pathplanning (TA-Prioritized)
+
+The Task-Allocation and Prioritized pathplanning (TA-Prioritized) algorithm is a centralized algorithm for the offline variant of the MAPD problem.
+
+In the offline variant of the MAPD problem the following is supported:
+- Using tasks from a taskslist with releaseTimes
+- Tested from 1 agent up untill 50 agents (significant lag for larger amount of agents)
+
+Not supported:
+- Randomly generated tasks (randomTasks = True)
+- Tasklists without releaseTimes
+
+## Running the algorithm
+
+1. Install the virtual environment
+ - 'pipenv install'
+2. Setup the LKH-3 TSP solver (see "LKH-3 TSP Solver" section)
+3. Setup lkh.config (see below)
+4. Run the "centralized_decisionmaker_shortest_path.py" file
+4. Start the game inside the Unity editor
+
+## Working of the algorithm
+
+This implementation is a modified version of the implementation available at: https://github.com/Pieter-Cawood/M-TA-Prioritized-MAPD
+
+This implementation is based on the "Task and Path Planning for Multi-Agent Pickup and Delivery" paper (http://idm-lab.org/bib/abstracts/papers/aamas19a.pdf).
+An explanation of the algorithm is available at:
+ - Section 3 and 4 of the "Task and Path Planning for Multi-Agent Pickup and Delivery" paper
+ - Section 2.2.2 of the paper provided in the "paper"
+
+General working of the algorithm:
+ - The algorithm uses the LKH-3 TSP Solver to generate task sequences for the agents:
+ * Each agent gets an ordered list of tasks (or no tasks at all)
+ * The agent needs to execute these tasks in the provided order
+ - The algorithm plans 1 path for each agent to execute its whole task set:
+ * Each agent gets one path that allows it to execute all its tasks in the correct order
+ => In order to minimize the amount of timesteps needed to solve the problem the algorithm uses the Prioritized Pathplanning method to always plan longer paths first
+ => This is done to avoid making the longer paths even longer by waiting to avoid collisions (see papers for a more detailed explanation)
+ - Once all the agents have their path they start following that path
+ * In each timestep each agent moves one step following its path
+
+## (IMPORTANT) LKH-3 TSP Solver
+
+- This algorithm uses a modified version of the original LKH-3 TSP solver (original: http://akira.ruc.dk/~keld/research/LKH-3/) to generate task sequences. The source code (and an executable for Linux and Windows) can be found in the LKH3 directory. A visual studio project for Windows is added as LKHwin-3.0.6-WinChanges.
+- By default the algorithm computes new task sequences using the LKH-3 solver every time the algorithm is ran
+- The algorithm supports using pre-computed task sequences (as this process can be slow for larger task sets) (see "Generating pre-computed task sequences" section)
+
+### Setup of LKH-3 TSP Solver
+
+- The source code (and an executable for Linux and Windows) are provided in the LKH-3 directory
+- If the provided executable is not supported by your machine (error when trying to run the LKH-3 executable) you can compile a new executable on a Unix/Linux machine using the following commands:
+ * cd LKH3
+ * make
+- On Windows machines you need to create an .exe file using the visual studio project provided in the LKH3/SRC directory
+
+### Running algorithm without using pre-computed task sequences
+- Set the lkh.conf file as follows:
+ * tourFile = None
+ * path = specify path to LKH3 solver (can found in directory LKH3, Linux: LKH_linux, Windows: LKH-3.exe)
+ * lkhMaxTime = The maximum time (in seconds) that should be used by the LKH-3 TSP solver to compute task sequences (default: 500)
+- Run the main file centralized_decisionmaker_shortest_path.py directly.
+
+### Running algorithm using pre-computed task sequences
+- Set the lkh.conf file as follows:
+ * tourFile = None.
+ * path = specify path to LKH3 solver (can found in directory LKH3, Linux: LKH_linux, Windows: LKH-3.exe)
+ * lkhMaxTime = The maximum time (in seconds) that should be used by the LKH-3 TSP solver to compute task sequences (default: 500)
+- Compute tour file using generate_tour_file.py
+ * Add the path to the problem file (config file warehouse) and the file containing the tasks in generate_tour_file.py.
+ * run generate_tour_file.py, a tour file will have been generated and added in LKH3/TOUR. Each tour file created will have a unique name.
+- Set the lkh.conf file as follows:
+ * tourFile = path to tour file computed in previous step.
+ * path = path to LKH3 solver (Linux: LKH_linux, Windows: LKH-3.exe)
+ * lkhMaxTime = The maximum time (in seconds) that should be used by the LKH-3 TSP solver to compute task sequences (default: 500)
+- Run the main file centralized_decisionmaker_shortest_path.py
+
+## Overview of the provided files
+
+Main file:
+- centralized_decisionmaker_shortest_path.py: Main file that runs the algorithm
+
+Task Allocation and Prioritized Pathplanning:
+- ta_agent.py: Agent that are executing tasks
+- ta_system.py: System allocates tasks and plans paths for agents
+- ta_task.py: Pickup and delivery tasks
+- ta_world.py: Class that reads and stores the warehouse environment
+
+A-star:
+- ta_astar.py: A-Star algorithm
+- ta_state.py: Contains Location and state classes used for the path planning
+
+LKH-3:
+- lkh.py: Class that computes task sequences using the LKH-3 TSP solver
+- lkh.conf: Configuration file for the LKH-3 TSP solver
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/TaskSideChannel.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e86f1a0f43763e5ee775f1063422926c5a64297
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/TaskSideChannel.py
@@ -0,0 +1,41 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+import ta_task
+from ta_state import Location
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ data = msg.read_string()
+ new_task = json.loads(data)
+ new_task = new_task["task"]
+ task_to_add = ta_task.Task(ta_task.CURRENT_TASK_ID,
+ Location(new_task["PickUp"]["Column"],
+ new_task["PickUp"]["Row"], 0),
+ Location(new_task["Delivery"]["Column"], new_task["Delivery"]["Row"], 0),
+ new_task["ReleaseTime"],
+ None)
+ ta_task.CURRENT_TASK_ID += 1
+ # add task to task to be assigned
+ self.environment_data.add_task_to_tasks_to_be_assigned(task_to_add)
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/centralized_decisionmaker_shortest_path.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/centralized_decisionmaker_shortest_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cc18a3d799b06749b833e0bd27ddaf0a71dda98
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/centralized_decisionmaker_shortest_path.py
@@ -0,0 +1,209 @@
+from mlagents_envs.environment import ActionTuple, UnityEnvironment
+import EnvChannel
+import TaskSideChannel
+import PickupAndDeliveryChannel as PADEC
+import numpy as np
+import os
+import time
+
+from Node import Node
+
+import configparser
+
+from lkh import LKH
+
+# from pickup_and_delivery.task import Task
+from ta_system import System as TA_System
+from ta_state import Location
+from ta_agent import Agent
+
+# as long as simulation_going is true, new actions will be generated
+simulation_going = True
+
+# Load the side channels and Unity environment.
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+# Open unity and press play to execute
+env = UnityEnvironment(file_name=None, side_channels=[
+ envChannel, taskChannel])
+env.reset() # reset the environment
+
+# Specify the used behaviour name for steering the agent
+behavior_names = list(env.behavior_specs.keys())
+behavior_name = behavior_names[0]
+
+# info obtained from environment
+environment_info = envChannel.EnvData
+# set up links between task side channel and environment info
+environment_info.task_side_channel = taskChannel
+taskChannel.environment_data = environment_info
+
+##--------##
+## Agents ##
+##--------##
+
+# Create all the agents
+agentsList = []
+for robot_id, start_node in environment_info.current_nodes_dictionary.items():
+ # Convert the Node that represents the agent's starting position to a Location
+ robot_position = Location(start_node.y, start_node.x, start_node.g)
+ # Create agent
+ agentsList.append(Agent(robot_id, robot_position))
+
+##-------##
+## Tasks ##
+##-------##
+
+# Retrieve all tasks
+tasksList = environment_info.tasks_to_be_assigned
+
+##-----------------------------##
+## LKH3-Solver: Task sequences ##
+##-----------------------------##
+
+## Create the LKH class so that the problem (and parameter) files for the LKH3 solver can be generated
+lkh = LKH(environment_info.matrix, tasksList, agentsList)
+# File that contains all the task sequences: can either be pre-computed or can be computed on the fly
+lkhFile = lkh.get_task_sequences()
+
+##--------##
+## System ##
+##--------##
+
+# Initialize system
+system = TA_System(environment_info.matrix, environment_info.rotation, agentsList, tasksList, lkhFile)
+
+##---------------##
+## Path Planning ##
+##---------------##
+
+# Retrieve all agents from the system
+agents_list = system.world.agents
+# Retrieve all paths from the system
+agent_paths = system.get_paths()
+
+##-------------##
+## Follow Path ##
+##-------------##
+
+# Keep track of current timestep
+actions_requested_in_timestep = 0
+timestep = 0
+
+# Keep track of tasks that need to be assigned in Unity
+to_assign_tasks = []
+
+# Assign all agents to their first task in Unity
+for a_id, agent in agents_list.items():
+ # Agent has at least one task
+ if len(agent.taskSet) > 0:
+ # Get the agent's first task
+ next_task = agent.taskSet[0]
+ # Assign the first task to the agent in Unity
+ environment_info.assign_task(
+ a_id, system.tasks[next_task.id])
+
+while simulation_going:
+ # Get information on the agents that request an action
+ decision_steps, terminal_steps = env.get_steps(behavior_name)
+ agents = decision_steps.agent_id
+ observation = decision_steps.obs[0][0]
+
+ # Actions that will be sent to Unity
+ actions = []
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # Assign tasks in Unity for agents that are waiting for a new task assignment
+ for agent_tuple in to_assign_tasks:
+ # Extract the needed information from the tupple
+ agent_id = agent_tuple[0]
+ task = agent_tuple[1]
+ # Assign the task to the agent in Unity
+ environment_info.assign_task(agent_id, task)
+ # Remove the task from the tupple list to avoid assigning the same task to the same agent more than once
+ to_assign_tasks.remove(agent_tuple)
+
+ ##--------------##
+ ## Observations ##
+ ##--------------##
+
+ # get observations per robot and store in dictionary observations[instance_id] -> observation
+ observations = {}
+ for i in range(environment_info.number_of_robots):
+ observation_i = observation[i * environment_info.observation_size:
+ i * environment_info.observation_size + environment_info.observation_size]
+ observations[observation_i[0]] = observation_i[1:]
+
+ # Create an action for all agents that request an action
+ for robot_id_unity in observations:
+
+ ##--------------##
+ ## OBSERVATIONS ##
+ ##--------------##
+
+ # robot_id_unity = list(agents_list.keys())[i]
+ # Collect agent's observations
+ observation = observations[robot_id_unity]
+
+ # Extract observations
+ # robot_id_unity = observation[0] # robot id in unity used to get correct precomputed path
+ robot_pos_x = observation[1]
+ robot_pos_y = observation[0]
+ robot_rot = observation[2]
+ # Convert coordinates to position
+ robot_position = Location(robot_pos_x, robot_pos_y, robot_rot)
+
+ ##-------------##
+ ## FOLLOW PATH ##
+ ##-------------##
+
+ # Get the pre-computed path of the agent
+ path = agent_paths[robot_id_unity]
+
+ next_pos = None
+
+ # Check if agent still needs to move
+ if timestep <= agents_list[robot_id_unity].final_action:
+ # Agent has not yet reached the end of its path: get the next position of the agent
+ next_pos = path[timestep]
+ else:
+ # Agent has reached the end of its path: stay parked at its parking location
+ next_pos = Location(robot_pos_x, robot_pos_y, robot_rot)
+
+ # Convert Position to Node
+ next_action = Node(next_pos.row, next_pos.col, next_pos.rotation)
+
+ # Check whether agent needs to pickup/deliver
+ pickup_action = 0
+ delivery_action = 0
+ if next_pos.to_pickup:
+ # Agent has to pick up his task
+ pickup_action = 1
+ elif next_pos.to_deliver:
+ # Agent has to deliver his task
+ delivery_action = 1
+ # Remove the task that was delivered from the taskSet
+ agents_list[robot_id_unity].taskSet = agents_list[robot_id_unity].taskSet[1:]
+ # Get the task set of the agent
+ tasks = agents_list[robot_id_unity].taskSet
+ # If there are remaining tasks assign the next one to the agent:
+ if (len(tasks) > 0):
+ # Get the agent's next task
+ next_task = tasks[0]
+ # Do not assign the task directly as the agent has not yet delivered it's task, add it to a list so that it will be assigned in the next timestep
+ to_assign_tasks.append((robot_id_unity, system.tasks[next_task.id]))
+
+ # Send action to agent in Unity
+ actions.extend([robot_id_unity, next_action.x, next_action.y, next_action.g, pickup_action, delivery_action])
+
+ timestep += 1
+
+ # Send actions to Unity
+ actions = np.array(actions)
+ actions.resize(1, environment_info.number_of_robots * environment_info.action_size)
+ action_tuple.add_discrete(actions)
+
+ env.set_actions(behavior_name, action_tuple)
+ env.step()
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/generate_tour_file.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/generate_tour_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0ea098ee7a2035f446ce5d87daab0b52cf063b
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/generate_tour_file.py
@@ -0,0 +1,65 @@
+import os
+import json
+import numpy as np
+
+from lkh import Agent, LKH
+from ta_world import Location
+from ta_task import Task
+
+#problemFile = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt"
+#tasksFile = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt"
+
+problemFile ="../../../PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt"
+tasksFile = "../../../PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt"
+
+
+print(os.path.isfile(problemFile))
+print(os.path.isfile(tasksFile))
+
+environment = None
+agentsList = []
+tasksList = []
+
+with open(problemFile) as envFile:
+ envData = json.load(envFile)
+
+ environment = np.array(envData["Matrix"])
+
+ agents = envData["InitialNodes"]
+
+ for a in agents:
+ col = a["GridPosition"]["Column"]
+ row = a["GridPosition"]["Row"]
+ rot = a["Degrees"]
+
+ loc = Location(col, row, rot)
+
+ agentsList.append(Agent(loc))
+
+with open(tasksFile) as tasksFile:
+
+ tasksData = json.load(tasksFile)
+
+ taskID = 1
+
+ for task in tasksData:
+
+ pickup = Location(task['PickUp']["Column"],
+ task['PickUp']["Row"],
+ 0)
+
+ delivery = Location(task['Delivery']["Column"],
+ task['Delivery']["Row"],
+ 0)
+
+ releaseTime = task['ReleaseTime']
+
+ tasksList.append(Task(taskID, releaseTime, pickup, delivery, None))
+
+ taskID += 1
+
+lkh = LKH(environment, tasksList, agentsList)
+
+lkhFile = lkh.get_task_sequences()
+
+print(f"Computed task sequences saved: {lkhFile}")
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.conf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.conf
new file mode 100644
index 0000000000000000000000000000000000000000..0fc64fae722c355ffd40ecd05ec99f5f2c091b3e
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.conf
@@ -0,0 +1,6 @@
+[lkh]
+#tourFile=None
+path=C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/LKH-3.exe
+lkhMaxTime=500
+
+tourFile=LKH3/TOUR/LKH_2021_08_12-12_29_43.tour
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.py
new file mode 100644
index 0000000000000000000000000000000000000000..33bc318348a49021335998965a55aff962976cd1
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/lkh.py
@@ -0,0 +1,262 @@
+import numpy as np
+import configparser
+import os
+import time
+import sys
+import json
+
+from ta_task import Task
+# from position import Position, dist
+from ta_world import Location
+
+def dist(loc1, loc2):
+ return abs(loc1.col - loc2.col) + abs(loc1.row - loc2.row)
+
+##-----------##
+## CONSTANTS ##
+##-----------##
+
+# Dummy agents only used to calculate the distances in the graph
+class Agent:
+ def __init__(self, pos):
+ # Dummy agents only have a position
+ self.parking_location = pos
+
+##--------##
+## System ##
+##--------##
+
+class LKH:
+ def __init__(self, environment, tasks, agentsList):
+ # Keep track of the agents and the amount of agents
+ self.agents = agentsList
+
+ # Keep track of all endpoints in the environment
+ self.task_endpoints = dict()
+ # Initialize environment
+ self.init_env(environment)
+
+ # Keep track of tasks in the task set
+ self.tasks = tasks
+ # Initialize tasks
+ self.load_tasks()
+
+ def init_env(self, env):
+ # Keep track of task endpoints
+ n_endpoints = 0
+ for y in range(len(env)):
+ for x in range(len(env[0])):
+ if env[y][x] == 3:
+ self.task_endpoints[(x, y)] = n_endpoints
+ n_endpoints += 1
+
+ ##-------##
+ ## TASKS ##
+ ##-------##
+
+ def load_tasks(self):
+ for task in self.tasks:
+ pickup_id = self.task_endpoints[(
+ task.pickup_endpoint.col, task.pickup_endpoint.row)]
+ task.pickup_id = pickup_id
+ delivery_id = self.task_endpoints[(
+ task.delivery_endpoint.col, task.delivery_endpoint.row)]
+ task.delivery_id = delivery_id
+
+ ##-------##
+ ## LKH-3 ##
+ ##-------##
+
+ def get_task_sequences(self):
+
+ ##----------------##
+ ## Initialization ##
+ ##----------------##
+
+ print(f"Current directory: {os.getcwd()}")
+
+ # Create a configParser to read the config file
+ config = configparser.ConfigParser()
+ # Read the config file
+ config.read("lkh.conf")
+
+ # Extract information needed from config file
+ # Path to pre-computed .tour file OR None (default: None)
+ tour_file_path = config["lkh"]["tourFile"]
+ lkh_path = config["lkh"]["path"] # Path to the LKH solver (default: /LKH3/LKH)
+ # Maximum time (in seconds) that is going to be used by the LKH3 solver to compute task sequences (default: 500)
+ lkh_time = config["lkh"]["lkhMaxTime"]
+
+ if tour_file_path != "None":
+ # Use a pre-computed .tour file (contains pre-computed task sequences)
+ return tour_file_path
+
+ # First compute the task sequences using the LKH3 solver, then return the created .tour file
+
+ ## Generate a unique filename for the generated problem, parameter and .tour files
+ timestr = time.strftime("%Y_%m_%d-%H_%M_%S")
+ lkh_filename = "LKH_" + timestr
+
+ ## Create problem and parameter files
+ self.generate_problem_files(lkh_filename)
+
+ ##------------##
+ ## Run solver ##
+ ##------------##
+
+ ## Run the LKH-3 algorithm to generate .tour file
+ print("\n\n[LKH3] Generating task sequences:\n")
+ os.system(lkh_path + " " + "LKH3/PROBLEM/" + lkh_filename + ".par")
+ print("\n\n[LKH3] Task sequences generated")
+
+ ##---------------##
+ ## Return result ##
+ ##---------------##
+
+ # Save the path of the newly created .tour file
+ lkhFile = "LKH3/TOUR/" + lkh_filename + ".tour"
+ return lkhFile
+
+
+
+ def generate_problem_files(self, lkhFileName):
+
+ ##-------------------------------------##
+ ## Create graph to calculate sequences ##
+ ##-------------------------------------##
+
+ n_tasks = len(self.tasks)
+ n_agents = len(self.agents)
+ n_nodes = n_tasks + n_agents
+ edge_weights = np.zeros((n_nodes, n_nodes))
+
+ # Edge
+ for k in range(n_agents):
+ for i in range(n_tasks):
+ task = self.tasks[i]
+ agent = self.agents[k]
+ # Edges from tasks to agents
+ edge_weights[n_agents +
+ i][k] = dist(task.pickup_endpoint, task.delivery_endpoint)
+ # Edges from agents to tasks
+ edge_weights[k][n_agents+i] = max(
+ dist(agent.parking_location, task.pickup_endpoint), task.release_time)
+
+ for i in range(n_tasks):
+ for j in range(n_tasks):
+ if (i != j):
+ taski = self.tasks[i]
+ taskj = self.tasks[j]
+ # Edges between tasks
+ d = dist(taski.pickup_endpoint, taski.delivery_endpoint) + \
+ dist(taski.delivery_endpoint, taskj.pickup_endpoint)
+ edge_weights[n_agents+i][n_agents+j] = d
+
+ #---------------------#
+ # Create problem file #
+ #---------------------#
+
+ problemFileName = "LKH3/PROBLEM/" + lkhFileName + ".tsp"
+ problemFile = open(problemFileName, "w")
+ problemFile.write("NAME: MAPD\n")
+ problemFile.write("TYPE: TSPTW\n")
+ problemFile.write(f"DIMENSION: {n_nodes}\n")
+ problemFile.write("EDGE_WEIGHT_TYPE: EXPLICIT\n")
+ problemFile.write("DISPLAY_DATA_TYPE: NO_DISPLAY\n")
+ problemFile.write("EDGE_WEIGHT_FORMAT: FULL_MATRIX\n")
+ problemFile.write("EDGE_WEIGHT_SECTION\n")
+ for i in range(n_nodes):
+ for j in range(n_nodes):
+ problemFile.write(f"{str(int(edge_weights[i][j]))} ")
+ problemFile.write("\n")
+ problemFile.write("TIME_WINDOW_SECTION\n")
+ for i in range(n_agents):
+ problemFile.write(f"{i+1} -1 {10000} 0 0 0\n")
+ for i in range(n_tasks):
+ task = self.tasks[i]
+ problemFile.write(
+ f"{i+1+n_agents} {task.release_time} {10000} {dist(task.pickup_endpoint, task.delivery_endpoint)} {task.pickup_id} {task.delivery_id}\n")
+
+ #-----------------------#
+ # Create parameter file #
+ #-----------------------#
+
+ parameterFileName = "LKH3/PROBLEM/" + lkhFileName + ".par"
+ parFile = open(parameterFileName, "w")
+ parFile.write("SPECIAL\n")
+ parFile.write("PROBLEM_FILE = " + problemFileName + "\n")
+ parFile.write("MAX_TRIALS = 100000\nTIME_LIMIT = 1000\n")
+ parFile.write("RUNS = 1\n")
+ parFile.write("TRACE_LEVEL = 1\n")
+ parFile.write("TOUR_FILE = LKH3/TOUR/" + lkhFileName + ".tour\n")
+ parFile.write("MAKESPAN = YES\n")
+
+# environmentFile = None
+# tasksFile = None
+
+#TODO: remove commented code
+
+# if __name__ == '__main__':
+#
+# if (len(sys.argv) != 3):
+# print("Incorrect number of arguments: 'python lkh.py <problemFile> <tasksFile>'")
+# problemFile = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt"
+# tasksFile = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt"
+# else:
+# problemFile = str(sys.argv[1])
+# tasksFile = str(sys.argv[2])
+#
+# print(f"Problem file: {problemFile}")
+# print(f"Tasks file: {tasksFile}")
+#
+# environment = None
+# agentsList = []
+# tasksList = []
+#
+# with open(problemFile) as envFile:
+# envData = json.load(envFile)
+#
+# environment = np.array(envData["Matrix"])
+#
+# agents = envData["InitialNodes"]
+#
+# for a in agents:
+# col = a["GridPosition"]["Column"]
+# row = a["GridPosition"]["Row"]
+# rot = a["Degrees"]
+#
+# loc = Location(col, row, rot)
+#
+# agentsList.append(Agent(loc))
+#
+# with open(tasksFile) as tasksFile:
+#
+# tasksData = json.load(tasksFile)
+#
+# taskID = 1
+#
+# for task in tasksData:
+#
+# pickup = Location(task['PickUp']["Column"],
+# task['PickUp']["Row"],
+# 0)
+#
+# delivery = Location(task['Delivery']["Column"],
+# task['Delivery']["Row"],
+# 0)
+#
+# releaseTime = task['ReleaseTime']
+#
+# tasksList.append(Task(taskID, releaseTime, pickup, delivery, None))
+#
+# taskID += 1
+#
+# lkh = LKH(environment, tasksList, agentsList)
+#
+# lkhFile = lkh.get_task_sequences()
+#
+# print(f"Computed task sequences saved: {lkhFile}")
+
+
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/paper/Bachelorthesis.pdf b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/paper/Bachelorthesis.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf4fce9fccb3903a7f502782e4292f8c59e23d
Binary files /dev/null and b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/paper/Bachelorthesis.pdf differ
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_agent.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..2203d79e507a49fa2b88204e085a6ee591d4a4a2
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_agent.py
@@ -0,0 +1,27 @@
+"""
+Created on Sat Apr 18 11:45:41 2020
+
+@author: Pieter Cawood
+
+"""
+
+GLOBAL_MAX_AGENT_TIME = 2500
+
+class Agent(object):
+ def __init__(self, _id, parking_location):
+ self.id = _id
+ self.parking_location = parking_location
+ self.path = {}
+ self.max_time = GLOBAL_MAX_AGENT_TIME
+ self.pos = parking_location
+ self.next_endpoint = None
+ self.time_at_goal = 0
+
+ self.final_action = 0
+
+ self.taskSet = []
+
+ self.tasksPlanning = []
+
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_astar.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_astar.py
new file mode 100644
index 0000000000000000000000000000000000000000..c15b674e09b720dae7cb101a40699b6b8b0bf74c
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_astar.py
@@ -0,0 +1,154 @@
+"""
+Created on Wed Apr 22 07:43:03 2020
+
+@author: Pieter Cawood
+
+"""
+from heapq import *
+from ta_state import *
+from ta_world import MAPNODETYPES
+
+def rotation_steps(rotation, start_rotation, end_rotation):
+ n_rotations = int(360/rotation)
+ return int(abs(end_rotation-start_rotation)/rotation)
+
+
+
+def get_heuristic(start_location, goal_location, rotation):
+ # Admissible heuristic - manhattan distance
+ return abs(start_location.col - goal_location.col) + abs(start_location.row - goal_location.row) + rotation_steps(rotation, start_location.rotation, goal_location.rotation)
+
+def get_old_heuristic(start_location, goal_location):
+ # Admissible heuristic - manhattan distance
+ return abs(start_location.col - goal_location.col) + abs(start_location.row - goal_location.row)
+
+
+class AStar:
+ def __init__(self, world, _rotation):
+ self.dim_x = world.width
+ self.dim_y = world.height
+ self.obstacles = {}
+ self.other_agent_paths = []
+
+ self.rotation = _rotation
+
+ def state_possible(self, current_state, new_state):
+ # Check not parking or wall
+ not_obstacle = False
+ if (new_state.location.col, new_state.location.row) not in self.obstacles:
+ not_obstacle = True
+ return 0 <= new_state.location.col < self.dim_x and 0 <= new_state.location.row < self.dim_y \
+ and not_obstacle and not self.is_collision(current_state, new_state)
+
+ def is_collision(self, current_state, new_state):
+ for other_agent_path in self.other_agent_paths:
+ # Vertex collision
+ if new_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == new_state.location.row and other_agent_path[new_state.time].col == new_state.location.col:
+ return True
+ # Edge collision
+ if new_state.time in other_agent_path and current_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == current_state.location.row and \
+ other_agent_path[new_state.time].col == current_state.location.col and \
+ other_agent_path[current_state.time].row == new_state.location.row and \
+ other_agent_path[current_state.time].col == new_state.location.col:
+ return True
+ return False
+
+ def get_neighbours(self, current_state):
+ neighbours = []
+ neighbour_time_step = current_state.time + 1
+ # Wait in state
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+
+ # Add rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation+self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ # Remove rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation-self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+
+ ## Move in specific directions
+ if current_state.location.rotation == 0:
+ # Move down
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row - 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 90:
+ # Move right
+ new_state = State(neighbour_time_step, Location(current_state.location.col + 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 180:
+ # Move up
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row + 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 270:
+ new_state = State(neighbour_time_step, Location(current_state.location.col - 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+
+ return neighbours
+
+ def create_obstacles(self, world, other_agent_paths, remaining_parking, timestep):
+ # Map blocked cells
+ for element in world:
+ if world[element] == MAPNODETYPES.WALL:
+ self.obstacles[(element[0], element[1])] = True
+
+ # Other agents' parking locations
+ for location in remaining_parking:
+ self.obstacles[(location.col, location.row)] = True
+
+ short_paths = []
+ t = timestep
+ for path in other_agent_paths:
+ new_path = dict()
+ t = timestep
+ while t in path:
+ new_path[t] = path[t]
+ t += 1
+ short_paths.append(new_path)
+ self.other_agent_paths = short_paths
+
+ def search(self, time_step, start_location, goal_location, other_agent_paths, world, parking_locations, agent,
+ release_time=0, find_step = 0):
+ self.create_obstacles(world, other_agent_paths, parking_locations, time_step)
+ initial_state = State(time_step, start_location)
+ came_from = {}
+ open_list = [initial_state]
+ closed_set = set()
+ initial_state.g_score = 0
+ # initial_state.f_score = get_heuristic(start_location, goal_location, self.rotation)
+ initial_state.f_score = get_old_heuristic(start_location, goal_location)
+ while open_list:
+ current = heappop(open_list)
+ # If agent is at the goal location
+ if current.location == goal_location:
+ # Agent cannot move during pickup/delivery, needs at least 2 timesteps at the goal location
+ if current in came_from and came_from[current].location == goal_location:
+ # If not yet release time hold and run search
+ if not current.time < release_time:
+ new_path = [current]
+ while current in came_from:
+ current = came_from[current]
+ new_path.append(current)
+ return new_path[::-1]
+ # Continue looking for the goal location
+ closed_set |= {current}
+ neighbours = self.get_neighbours(current)
+ neighbour: State
+ for neighbour in neighbours:
+ # Only explore new states
+ if (neighbour not in closed_set) and (neighbour not in open_list):
+ came_from[neighbour] = current
+ neighbour.g = current.g + 1
+ # neighbour.f = neighbour.g + get_heuristic(neighbour.location, goal_location, self.rotation)
+ neighbour.f = neighbour.g + get_old_heuristic(neighbour.location, goal_location)
+ heappush(open_list, neighbour)
+ return False
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_state.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ad98faed2d5c8a149f7671e2ce84702c0db5604
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_state.py
@@ -0,0 +1,56 @@
+"""
+Created on Sat Apr 18 10:45:11 2020
+
+@author: Pieter Cawood
+
+"""
+
+def h(pos1, pos2):
+ return abs(pos1.col - pos2.col) + abs(pos1.row - pos2.row)
+
+class Location(object):
+ def __init__(self, col, row, rotation):
+ self.col = col
+ self.row = row
+ self.rotation = rotation
+
+ # Relevant information for pickup and delivery actions
+ self.task_id = None
+ self.to_pickup = False
+ self.to_deliver = False
+
+ def __eq__(self, other):
+ return self.col == other.col and self.row == other.row and self.rotation == other.rotation
+
+ def __hash__(self):
+ return hash(str(self.col) + "_" + str(self.row) + "_" + str(self.rotation))
+
+ def __str__(self):
+ return str((self.col, self.row, self.rotation))
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"Location({self.col}, {self.row}, {self.rotation})"
+
+
+class State(object):
+ def __init__(self, time, location):
+ self.time = time
+ self.location = location
+ self.g = 0
+ self.f = 0
+
+ def __eq__(self, other):
+ return self.time == other.time and self.location == other.location
+
+ def __hash__(self):
+ return hash(str(self.time) + str(self.location.col) + str(self.location.row))
+
+ def __str__(self):
+ return str((self.time, self.location.col, self.location.row))
+
+ def __lt__(self, other):
+ if self.f < other.f:
+ return True
+ else:
+ return False
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_system.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..e36ec78cd4c83668bdbb577597cc9509120962e0
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_system.py
@@ -0,0 +1,261 @@
+"""
+Created on Sat Apr 18 11:11:10 2020
+
+@author: Pieter Cawood
+"""
+import time
+from ta_world import *
+from ta_task import *
+from ta_astar import AStar
+from queue import Queue
+from itertools import combinations
+import concurrent.futures
+
+GLOBAL_MAX_AGENT_TIME = 2500
+
+def plan_path(world, rotation, agent, time_step, release_time=0, find_step=0):
+ a_star = AStar(world, rotation)
+ other_agent_paths = []
+ parking_locations = []
+
+ # Add other agent paths
+ for agent_id in world.agents:
+ if agent_id != agent.id:
+ other_agent_paths.append(world.agents[agent_id].path)
+ parking_locations.append(world.agents[agent_id].parking_location)
+ start_location = agent.path[time_step]
+ goal_location = agent.next_endpoint
+
+ # Non dummy path, add release time to hold pickup
+ new_path = a_star.search(time_step, start_location, goal_location, other_agent_paths, world, parking_locations,
+ agent, release_time, find_step)
+ # Could not find path
+ if not new_path:
+ return False
+
+ # Update agent path
+ for state in new_path:
+ world.agents[agent.id].path[state.time] = state.location
+
+ world.agents[agent.id].time_at_goal = time_step + len(new_path) - 1
+ return world.agents[agent.id].time_at_goal
+
+def copy_queue(queue):
+ result = Queue()
+ for entry in queue.queue:
+ result.put(entry)
+ return result
+
+def calculate_execution_time(world, rotation, agent_id, tsp_seqs):
+ # print(f"Calculating execution time of agent: {agent_id}")
+ time_step = 0
+ new_start_step = -1
+ while True:
+ new_start_step += 1
+ time_step = new_start_step
+ if agent_id not in tsp_seqs:
+ tsp_seqs[agent_id] = Queue()
+ seq = copy_queue(tsp_seqs[agent_id])
+ path_failed = False
+ for seq_task in range(seq.qsize()):
+ task = seq.get()
+ # To pickup position
+ world.agents[agent_id].next_endpoint = task.pickup_endpoint
+ time_step = plan_path(world, rotation, world.agents[agent_id], time_step, task.release_time, 1)
+ if not time_step:
+ path_failed = True
+ break
+ # To delivery position
+ world.agents[agent_id].next_endpoint = task.delivery_endpoint
+ time_step = plan_path(world, rotation, world.agents[agent_id], time_step, 0, 2)
+ if not time_step:
+ path_failed = True
+ break
+ if not path_failed:
+ break
+ # Move agent path back to parking position
+ for t_step in range(GLOBAL_MAX_AGENT_TIME):
+ world.agents[agent_id].path[t_step] = world.agents[agent_id].parking_location
+ return time_step
+
+class System:
+ def __init__(self, env, rotation, agentsList, taskList, sequence_file_path):
+ self.env = env
+ self.rotation = rotation
+ self.agentsList = agentsList
+ self.taskList = taskList
+ self.sequence_file_path = sequence_file_path
+
+ self.world = None
+ self.tasks = None
+
+ self.paths = dict()
+
+ self.makespan = 0
+
+ self.main()
+
+
+ def load_files(self):
+ world = World(self.env, self.agentsList)
+ self.world = world
+ tasks = TaskDict(self.taskList, world)
+ self.tasks = tasks
+ agent_tour_dict = TourDict(
+ self.sequence_file_path, self.agentsList, tasks)
+ return world, tasks, agent_tour_dict
+
+
+ def find_agent_path(self, world, rotation, agent, time_step, release_time=0, find_step=0):
+ a_star = AStar(world, rotation)
+ other_agent_paths = []
+ parking_locations = []
+
+ # Add other agent paths
+ for agent_id in world.agents:
+ if agent_id != agent.id:
+ other_agent_paths.append(world.agents[agent_id].path)
+ parking_locations.append(world.agents[agent_id].parking_location)
+ start_location = agent.path[time_step]
+ goal_location = agent.next_endpoint
+
+ # Non dummy path, add release time to hold pickup
+ new_path = a_star.search(time_step, start_location, goal_location, other_agent_paths, world, parking_locations,
+ agent, release_time, find_step)
+ # Could not find path
+ if not new_path:
+ return False
+
+ # Update agent path
+ for state in new_path:
+ world.agents[agent.id].path[state.time] = state.location
+
+ world.agents[agent.id].time_at_goal = time_step + len(new_path) - 1
+ return world.agents[agent.id].time_at_goal
+
+
+ def test_paths(self, world):
+ for agent_1, agent_2 in combinations(world.agents, 2):
+ for time_step in world.agents[agent_1].path:
+ if time_step > max(world.agents[agent_2].path):
+ break
+ # Vertex collision between any two agents
+ if world.agents[agent_1].path[time_step] == world.agents[agent_2].path[time_step]:
+ print("Agent #" + str(agent_1) + ",#" + str(agent_2) + " vertex collision at " +
+ str(world.agents[agent_1].path[time_step]) + " t:" + str(time_step))
+ return False
+ # Edge collision between any two agents
+ elif (time_step + 1 in world.agents[agent_1].path) and (time_step + 1 in world.agents[agent_2].path):
+ if (world.agents[agent_1].path[time_step] == world.agents[agent_2].path[time_step + 1]) and \
+ (world.agents[agent_2].path[time_step] == world.agents[agent_1].path[time_step + 1]):
+ print("Agent #" + str(agent_1) + ",#" + str(agent_2) + " edge collision at " +
+ str(world.agents[agent_1].path[time_step]) + "-" +
+ str(world.agents[agent_2].path[time_step]) + " t:" + str(time_step))
+ return False
+ return True
+
+
+
+ def main(self):
+ world, tasks, tsp_seqs = self.load_files()
+ make_span = 0
+ last_sim_step = 0
+ closed_list = {}
+ time_start = time.perf_counter()
+ for next_agent in world.agents:
+ time_agent_start = time.perf_counter()
+ max_len = 0
+ priority_id = -1
+ print(f"Determining execution times for remaining agents: ", end='')
+ '''
+ Calculate agents execution time for their paths
+ Initialise path to parking position once complete
+ '''
+ estimated_times = []
+ threads = []
+ remainingAgents = []
+
+ for agent_id in world.agents:
+ if agent_id not in closed_list:
+ remainingAgents.append(agent_id)
+
+ if len(remainingAgents) > 1:
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ for agent_id in remainingAgents:
+ print(agent_id, end=',')
+ future = executor.submit(calculate_execution_time, world, self.rotation, agent_id, tsp_seqs)
+ threads.append((future, agent_id))
+ print("")
+
+ for threadTuple in threads:
+ timestep = threadTuple[0].result()
+ estimated_times.append((timestep, threadTuple[1]))
+ largest_exec = max(estimated_times, key=lambda t: t[0])
+ max_len = largest_exec[0]
+ priority_id = largest_exec[1]
+ else:
+ priority_id = remainingAgents[0]
+ # print(f"Calculating actual for {priority_id}")
+ # Expand _id: highest execution time
+ closed_list[priority_id] = True
+ # print(f"Agent {priority_id} is now in closed list")
+ '''
+ Calculate actual prioritized agent path
+ '''
+ new_start_step = -1
+ while True:
+ new_start_step += 1
+ time_step = new_start_step
+ seq = copy_queue(tsp_seqs[priority_id])
+ path_failed = False
+ for next_task in range(tsp_seqs[priority_id].qsize()):
+ task = seq.get()
+ # To pickup position
+ world.agents[priority_id].next_endpoint = task.pickup_endpoint
+ time_step = self.find_agent_path(world, self.rotation, world.agents[priority_id], time_step, task.release_time, 3)
+ world.agents[priority_id].path[time_step].task_id = task.id
+ world.agents[priority_id].path[time_step].to_pickup = True
+ if not time_step:
+ path_failed = True
+ break
+ # To delivery position
+ world.agents[priority_id].next_endpoint = task.delivery_endpoint
+ time_step = self.find_agent_path(world, self.rotation, world.agents[priority_id], time_step, 0, 4)
+ world.agents[priority_id].path[time_step].task_id = task.id
+ world.agents[priority_id].path[time_step].to_deliver = True
+ if not time_step:
+ path_failed = True
+ break
+ task.delivered = time_step
+ world.agents[priority_id].taskSet.append(task)
+ if not path_failed:
+ break
+ # Record make_span before parking travel
+ make_span = max(make_span, world.agents[priority_id].time_at_goal)
+ time_agent_stop = time.perf_counter()
+ print("\t* Agent #" + str(priority_id) + " execution time: " + str(
+ world.agents[priority_id].time_at_goal) + " (calc :" + str(
+ round(((time_agent_stop - time_agent_start) / 60), 1)) + " mins)")
+ # End - Travel back to parking
+ world.agents[priority_id].next_endpoint = world.agents[priority_id].parking_location
+ self.find_agent_path(world, self.rotation, world.agents[priority_id], time_step)
+ last_sim_step = max(last_sim_step, world.agents[priority_id].time_at_goal)
+ world.agents[priority_id].final_action = last_sim_step
+
+ time_stop = time.perf_counter()
+ print("Make_span of :" + str(make_span))
+ self.makespan = make_span
+ print("Runtime of :" + str(round(((time_stop - time_start) / 60), 1)) + " minutes")
+ for a_id, a in world.agents.items():
+ self.paths[a_id] = a.path
+
+ def get_paths(self):
+ return self.paths
+
+ def get_parking_locations(self):
+ agents = self.world.agents
+ parking_locations = dict()
+ for a_id, agent in agents.items():
+ parking_pos = agent.parking_location
+ parking_locations[parking_pos] = a_id
+ return parking_locations
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_task.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..87abad263c228fe56cb021979dbe4bf4d2d39226
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_task.py
@@ -0,0 +1,100 @@
+"""
+Created on Sat Apr 18 12:51:32 2020
+
+@author: Pieter Cawood
+"""
+from queue import Queue
+import os
+
+CURRENT_TASK_ID = 0
+
+
+class Task(object):
+ def __init__(self, task_id, release_time, endpoint_pickup, endpoint_delivery, world):
+ self.id = task_id
+ self.assigned_agent = None
+ self.release_time = release_time
+ self.pickup_endpoint = endpoint_pickup
+ self.delivery_endpoint = endpoint_delivery
+
+ self.delivered = None
+
+ self.pickup_id = None
+ self.delivery_id = None
+
+ def service_time(self):
+ if self.delivered is not None:
+ return (self.delivered - self.release_time)
+ else:
+ return None
+
+
+# With key of task_id to pair with TaskSequence
+class TaskDict(dict):
+ def __init__(self, taskList, world):
+ self.load_tasks(taskList, world)
+
+ def load_tasks(self, taskList, world):
+
+ for t in taskList:
+ self[t.id] = t
+
+
+ # task_id = 0
+ # for t in taskList:
+ # task_releaseTime = t[0]
+ # task_pickup = t[1]
+ # task_delivery = t[2]
+
+ # self[task_id] = Task(task_id, task_releaseTime, task_pickup, task_delivery, world)
+ # task_id += 1
+ print(f"(2/3) Tasks loaded")
+
+
+# With key of seq id (max is agent count) to load all tasks
+# Seq_id is initially agent listed in tour file
+class TourDict(dict):
+ def __init__(self, tour_file_path, agent_count, tasks_dict):
+ self.load_file(tour_file_path, agent_count, tasks_dict)
+
+ def load_file(self, tour_file_path, agentsList, tasks_dict):
+ agent_count = len(agentsList)
+ file = open(tour_file_path, "r")
+# file = open("C:/Users/Marjon/Documents/Toolbox_MAS/mvpwarehouse/Algorithm_TA_Prioritized/LKH3/TOUR/LKH_2021_07_08-15_00_44.tour", "r")
+ if not file.mode == 'r':
+ print("Could not open " + tour_file_path)
+ else:
+ tour_data = file.readlines()
+ tour_section = False
+ agent_sequence = None
+ agent_number = 1
+ ez = 0
+ for line in tour_data:
+ if "-1" in line: # end of data
+ if agent_sequence.qsize():
+ agent = agentsList[agent_number-1]
+ a_id = agent.id
+ self[a_id] = agent_sequence
+ tour_section = False
+ if tour_section:
+ line_val = int(line)
+ if line_val <= agent_count: # line listed an agent number
+ # print(f"Reading sequence of agent: {ez} / {agent_count}")
+ if agent_sequence.qsize(): # Store previous agent queue
+ # print(f"Saved sequence for agent {agent_number} with size {agent_sequence.qsize()}")
+ agent = agentsList[agent_number-1]
+ a_id = agent.id
+ self[a_id] = agent_sequence
+ agent_sequence = Queue() # Create new agent queue
+ agent_number = line_val
+ ez += 1
+ else:
+ # Line listed as task number, subtract agent count
+ task_id = line_val - agent_count - 1
+ agent_sequence.put(tasks_dict[task_id])
+ # Set initial agent to tsp seq
+ tasks_dict[task_id].seq_id = agent_number
+ if "TOUR_SECTION" in line: # start of data next line
+ tour_section = True
+ agent_sequence = Queue()
+ print(f"(3/3) Sequences loaded")
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_world.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_world.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb39f9cc548bfacb46aefb85c9b4e91cf5ee52a4
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/ta_world.py
@@ -0,0 +1,67 @@
+"""
+Created on Sat Apr 18 10:49:17 2020
+
+@author: Pieter Cawood
+
+"""
+from enum import Enum
+from ta_agent import *
+from ta_state import *
+
+
+class MAPNODETYPES(Enum):
+ PARKING = 0
+ PATH = 1
+ WALL = 2
+ TASK_ENDPOINT = 3
+
+
+# Loads map. parking and endpoints are numbered in sequence loaded.
+# Task endpoints numbered from agent_number + 1
+class World(dict):
+ def __init__(self, env, agentsList):
+ self.agents = dict()
+ self.endpoints = dict()
+ self.parking_locations = dict()
+ self.width = 0 # Map width
+ self.height = 0 # Map height
+ self.load_map(env)
+ self.load_agents(env, agentsList)
+
+ def load_agents(self, env, agentsList):
+ for agent in agentsList:
+ agent_col = agent.parking_location.col
+ agent_row = agent.parking_location.row
+ if env[agent_row][agent_col] != 2:
+ raise Exception('Agent {} has an invalid parking location: {}'.format(agent.id, agent.parking_location))
+ self.agents[agent.id] = agent
+ agent.path[0] = agent.parking_location
+ self.parking_locations[agent.id] = agent.parking_location
+
+ def load_map(self, env):
+ parking_count = 1
+ endpoint_count = 0
+ for row in range(len(env)):
+ for col in range(len(env[0])):
+ node = None
+ if env[row][col] == 0:
+ # Node is a path
+ node = MAPNODETYPES.PATH
+ elif env[row][col] == 2:
+ # Node is a parking location (non-task endpoint)
+ node = MAPNODETYPES.PARKING
+ elif env[row][col] == 1:
+ # Node is a wall
+ node = MAPNODETYPES.WALL
+ elif env[row][col] == 3:
+ # Node is a pickup/delivery location (task endpoint)
+ node = MAPNODETYPES.TASK_ENDPOINT
+ self.endpoints[endpoint_count] = Location(col, row, 0)
+ endpoint_count += 1
+ if node is not None:
+ # Add node to this collection
+ self[col, row] = node
+ self.width = len(env[0])
+ self.height = len(env)
+ print(f"(1/3) Map loaded")
+ print(f"Created {parking_count} agents")
diff --git a/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/test.py b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..5afca52772df1cee7078faa5fac96a39cd7a9421
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/test.py
@@ -0,0 +1,15 @@
+import os
+
+#total = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized/LKH3/TOUR/LKH_2021_08_11-16_23_20.tour"
+#start = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized"
+
+total = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt"
+start = "C:/Users/Marjon/Documents/Toolbox_MAS/pick-up-and-delivery/Algorithms/Offline_Centralized/Algorithm_TA_Prioritized"
+
+#print(os.path.relpath(total, start))
+
+test = "../../../PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt"
+
+print(os.path.isfile(test))
+
+print(os.path.isfile("LKH3/LKH-3.exe"))
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvChannel.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..64ab52f3d9eba835313576996a986084d4dc6233
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvChannel.py
@@ -0,0 +1,61 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel: stores all incoming information
+in an instance of class EnvironmentData
+"""
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.envData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.envData.matrix = environment_info["matrixLayout"]
+ self.envData.rotation = environment_info["rotationRobot"]
+ self.envData.size_of_action = environment_info["sizeOfAction"]
+ self.envData.size_of_observation = environment_info["sizeOfObservation"]
+ self.envData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.envData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.envData.can_go_backwards = environment_info["backwards"]
+ self.envData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.envData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.envData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.envData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.envData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentData.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e8e71e36be0c96182a7480803290e2edbaccb2f
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentData.py
@@ -0,0 +1,99 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+stores data on environment
+instance methods that initialize environment (used by EnvChannel)
+- set_initial_nodes_dictionary(initial_nodes): initializes current_nodes_dictionary
+containing current(next) nodes for each of the robots
+- set_tasks_to_be_assigned(tasks): initialize tasks_to_be_assigned: tasks that have to be assigned
+- set_assigned_tasks(): initialize assigned_tasks_dictionary containing
+ for every robot the current assigned task
+
+instance methods that update environment info
+- assign_task(robot_instanceID, task): assign task to a given robot
+- add_task_to_tasks_to_be_assigned(task): append a new task to tasks_to_be_assigned
+- task_done(instance_id): remove task from robot that was assigned this task
+"""
+
+
+from Node import Node
+from Task import Position, Task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.size_of_action = None
+ self.size_of_observation = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Position(tasks[i]["PickUp"]["Row"], tasks[i]["PickUp"]["Column"])
+ delivery = Position(tasks[i]["Delivery"]["Row"], tasks[i]["Delivery"]["Column"])
+ release = tasks[i]["ReleaseTime"]
+ self.tasks_to_be_assigned.append(Task(pickup, delivery, release))
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ self.tasks_to_be_assigned.remove(task)
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentManager.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..d830bee18b18943dfe19075de04c1083c021f727
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/EnvironmentManager.py
@@ -0,0 +1,203 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""'
+Class that contains and manages the information on the environment.
+- Initializes and holds information on the environment (EnvData).
+- Contains a reference to the sidechannel (EnvChannel).
+- Contains a reference to the sidechannel (TaskChannel).
+- Contains a reference to the unity environment (UnityEnv).
+Has instance methods
+- reset() which resets the environment.
+- step() which assigns tasks, computes a new action based on observation from the unity environment
+ and sends actions back to unity, updates info environment when a task has finished
+- close() which closes the environment/
+
+Has also some auxilary instance methods:
+- assign_new_task() which finds a new task and a robot that is free, then calls
+assign_task_to_robot(robot_instanceID, task)
+- compute_path(current_node, task) which computes paths starting in current_node completing a certain task
+- initialize_shortest_paths() which initializes a dictionary containing the paths robots are following
+- assign_task_to_robot(robot_instanceID, task) which assigns task to a robot with given instance ID,
+updates EnvData and sends information on assignment back to Unity
+
+Note that this a toy example. No checks are done on collisions.
+"""
+
+
+import json
+
+from mlagents_envs.base_env import ActionTuple
+
+from Graph import Graph
+from Node import Node
+
+import numpy as np
+
+
+class EnvironmentManager:
+
+ def __init__(self, envData, unityEnv, envChannel, taskChannel):
+ self.envData = envData
+ self.unityEnv = unityEnv
+ self.envChannel = envChannel
+ self.taskChannel = taskChannel
+
+ # IMPORTANT: sends info through envChannel, to be called before setting up connection envData an taskChannel
+ self.reset()
+
+ # taskChannel needs info on envData
+ self.taskChannel.environment_data = envData
+
+ # behavior name for agent
+ self.behavior_name = list(unityEnv.behavior_specs.keys())[0]
+
+ # graph that is used for computing paths
+ self.graph = Graph(envData.matrix, envData.rotation, envData.can_go_backwards)
+
+ # for every robot with instanceID id shortest_paths[id] gives the shortest path to
+ # its currently assigned task
+ self.shortest_paths = {}
+
+ self.initialize_shortest_paths()
+
+ # step in the environment
+ # 1) checks if new tasks can be assigned
+ # 2) collect observations
+ # 3) compute new actions
+ # 4) send actions back to Unity
+ def step(self):
+ self.assign_new_task()
+
+ # get information from the agent that requests an action
+ decision_steps, terminal_steps = self.unityEnv.get_steps(self.behavior_name)
+
+ # decision_steps.obs is a list of numpy arrays, first dimension corresponds to the number of agents that has requested
+ # a decision (here 1), the observation of the agent is the first element
+ observation = decision_steps.obs[0][0]
+
+ print("----------------------------------------")
+
+
+ actions = [] # long list of all actions
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # get observations per robot and store in dictionary observations[instance_id] -> observation
+ observations = {}
+ for i in range(self.envData.number_of_robots):
+ observation_i = observation[i * self.envData.size_of_observation:
+ i * self.envData.size_of_observation + self.envData.size_of_observation]
+ observations[observation_i[0]] = observation_i[1:]
+
+ # create an action for all robots and send at once to unity
+ for robot_id_unity in observations:
+
+ obs = observations[robot_id_unity]
+ print("robot " + str(robot_id_unity) + " at node [(" + str(obs[0]) + "," + str(obs[1]) + ")," + str(
+ obs[2]) + "]")
+
+ path = self.shortest_paths[robot_id_unity]
+ # if path is empty, then robot should stay at same node
+ if len(path) == 0:
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 0, 0])
+ # if path is not empty we get next action from shortest path
+ else:
+ next_action = path[0] # is node or "pickup" or "deliver"
+ if next_action == "pickup":
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 1, 0])
+ elif next_action == "deliver":
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 0, 1])
+ self.envData.assigned_tasks_dictionary[robot_id_unity] = None
+ else:
+ actions.extend([robot_id_unity, next_action.x, next_action.y, next_action.g, 0, 0])
+ # update current node
+ self.envData.current_nodes_dictionary[robot_id_unity] = Node(next_action.x, next_action.y,
+ next_action.g)
+ # update path
+ self.shortest_paths[robot_id_unity] = path[1:]
+
+ # add action to action_tuple and set the actions
+ actions = np.array(actions)
+ actions.resize(1, self.envData.size_of_action * self.envData.number_of_robots)
+ action_tuple.add_discrete(actions)
+
+ self.unityEnv.set_actions(self.behavior_name, action_tuple)
+ self.unityEnv.step() # send actions to unity and wait until a new decision is requested
+
+
+
+
+ # reset the environment
+ def reset(self):
+ self.unityEnv.reset()
+
+ # close the environment
+ def close(self):
+ self.close()
+
+ # assigns the next available task to an idle robot, if possible
+ def assign_new_task(self):
+ # check if there are tasks that have to be assigned
+ # if this is the case, get first task and assign to available robot
+ if len(self.envData.tasks_to_be_assigned) != 0:
+ task = self.envData.tasks_to_be_assigned[0]
+ # get available robot
+ for robot_id in self.envData.instance_ids:
+ if self.envData.assigned_tasks_dictionary[robot_id] is None:
+ # robot is free: assign task to robot and break out of for loop
+ self.assign_task_to_robot(robot_id, task)
+ break
+
+
+ # compute path from current_node that finishes given task
+ # for test purposes pick up and delivery need to have rotation 0
+ # at pick up delivery robot stays at same node
+ # returns a sequence of nodes and pick up/delivery actions
+ def compute_path(self, current_node, task):
+ path1 = self.graph.computeShortestPath(current_node,
+ Node(task.PickUp.Row, task.PickUp.Column, 0))
+ path1 = path1[1:]
+ path2 = self.graph.computeShortestPath(Node(task.PickUp.Row, task.PickUp.Column, 0),
+ Node(task.Delivery.Row, task.Delivery.Column, 0))
+ path2 = path2[1:]
+
+ return path1 + ["pickup"] + path2 + ["deliver"]
+
+
+ # for every robot initially there is an empty path to be followed
+ def initialize_shortest_paths(self):
+ for robot_id in self.envData.instance_ids:
+ self.shortest_paths[robot_id] = []
+
+ def assign_task_to_robot(self, robot_instanceID, task):
+ # update envData
+ self.envData.assign_task(robot_instanceID, task)
+ # compute path for robot
+ self.shortest_paths[robot_instanceID] = \
+ self.compute_path(self.envData.current_nodes_dictionary[robot_instanceID],task)
+ # send info to unity
+ taskDict = {"Task": {
+ "PickUp": {"Row": task.PickUp.Row, "Column": task.PickUp.Column},
+ "Delivery": {"Row": task.Delivery.Row, "Column": task.Delivery.Column},
+ "ReleaseTime": task.ReleaseTime},
+ "InstanceID": robot_instanceID}
+ self.taskChannel.send_string(json.dumps(taskDict))
+
+
+
+
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Graph.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e03b997172dae6855dca7f06890606239e7063
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Graph.py
@@ -0,0 +1,126 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from networkx import DiGraph, shortest_path
+from Node import Node
+
+"""
+Class Graph creates a graph based on matrix, deviation (unit of rotation) and whether robots can move backwards).
+Its nodes contain position in the grid and orientation wrt north (see class <c>Node</c>).
+Networkx package as underlying data structure.
+"""
+
+class Graph:
+ def __init__(self, matrix, deviation, cangobackwards):
+ self.matrix = matrix
+ self.number_of_rows = len(matrix)
+ self.number_of_columns = len(matrix[0])
+ self.deviation = deviation
+ self.cangobackwards = cangobackwards
+ self.graph = DiGraph()
+
+ for i in range(self.number_of_rows):
+ for j in range(self.number_of_columns):
+ # we have to add nodes to the graph when we encounter a number different than 1
+ if matrix[i][j] != 1:
+ self.addEdgesByRotation(i, j)
+ self.addEdgesByMovingForward(i,j)
+
+ def addEdgesByRotation(self, row, column):
+ degree = 0
+ while degree < 360:
+ newnode = Node(row, column, degree)
+
+ # add all edges from newnode staying at the same position (row, number), i.e. rotation
+
+ # rotate left
+ rotateLeftNode = Node(row, column, get_degree_in_range_0_360(degree - self.deviation))
+ self.graph.add_edge(newnode, rotateLeftNode)
+
+ # rotate right
+ rotateRightNode = Node(row, column, get_degree_in_range_0_360(degree + self.deviation))
+ self.graph.add_edge(newnode, rotateRightNode)
+
+ degree += self.deviation
+
+ def addEdgesByMovingForward(self, row, column):
+ # check left
+ if column > 0 and self.matrix[row][column-1] != 1:
+ # we can move forward if we have degree 270
+ currentnode = Node(row, column, 270)
+ neighbourleft = Node(row, column-1, 270)
+ self.graph.add_edge(currentnode, neighbourleft)
+ # we can move backward if we have degree 90
+ if self.cangobackwards:
+ currentnode = Node(row, column, 90)
+ neighbourleft = Node(row, column-1, 90)
+ self.graph.add_edge(currentnode, neighbourleft)
+
+ # check right
+ if column < self.number_of_columns -1 and self.matrix[row][column+1] != 1:
+ # we can move forward if we have degree 90
+ currentnode = Node(row, column, 90)
+ neighbourright = Node(row, column+1, 90)
+ self.graph.add_edge(currentnode, neighbourright)
+ # we can move backward if we have degree 270
+ if self.cangobackwards:
+ currentnode = Node(row, column, 270)
+ neighbourright = Node(row, column+1, 270)
+ self.graph.add_edge(currentnode, neighbourright)
+
+ # check up
+ if row > 0 and self.matrix[row-1][column] != 1:
+ # we can move forward if we have degree 0
+ currentnode = Node(row, column, 0)
+ neighbourup = Node(row-1, column, 0)
+ self.graph.add_edge(currentnode, neighbourup)
+ # we can move backward if we have degree 180
+ if self.cangobackwards:
+ currentnode = Node(row, column, 180)
+ neighbourup = Node(row-1, column, 180)
+ self.graph.add_edge(currentnode, neighbourup)
+
+ # check down
+ if row < self.number_of_rows-1 and self.matrix[row+1][column] != 1:
+ # we can move forward if we have degree 180
+ currentnode = Node(row, column, 180)
+ neighbourdown = Node(row+1, column, 180)
+ self.graph.add_edge(currentnode, neighbourdown)
+ # we can move backward if we have degree 0
+ if self.cangobackwards:
+ currentnode = Node(row, column, 0)
+ neighbourdown = Node(row+1, column, 0)
+ self.graph.add_edge(currentnode, neighbourdown)
+
+ def computeShortestPath(self, start_node, end_node):
+ #compute shortest path from start_node to end_node: return a list
+ return shortest_path(self.graph, start_node, end_node)
+
+def get_degree_in_range_0_360(degree):
+ rest = degree % 360
+ if rest < 0:
+ return rest + 360
+ return rest
+
+
+
+
+
+
+
+
+
+
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Node.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..126684f885f63b51edcd2b6684d26d7a847c51af
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Node.py
@@ -0,0 +1,57 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Node represent nodes in the graph. Every node contains a
+position (x,y) in the grid
+and a rotation in degree where
+0 = North
+90 = East
+280 = South
+270 = West
+all degrees must be between 0 and 360
+
+attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+"""
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Pipfile b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Task.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Task.py
new file mode 100644
index 0000000000000000000000000000000000000000..23aa3d5b09f76858f524061f0050f39d9e914fe0
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/Task.py
@@ -0,0 +1,51 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Task represents tasks which consist of 2 positions: a pickup and a delivery position and a release time.
+"""
+class Task:
+ def __init__(self, pos1, pos2, release):
+ self.PickUp = pos1
+ self.Delivery = pos2
+ self.ReleaseTime = release
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.PickUp == other.PickUp and\
+ self.Delivery == other.Delivery and self.ReleaseTime == other.ReleaseTime
+
+ def __hash__(self):
+ hash_code = hash(self.PickUp)
+ hash_code = (hash_code * 397) ** hash(self.Delivery)
+ hash_code = (hash_code * 397) ** hash(self.ReleaseTime)
+ return hash_code
+
+ def __str__(self):
+ return "[" + str(self.PickUp) + ", " + str(self.Delivery) + "]" + str(self.ReleaseTime)
+
+class Position:
+ def __init__(self, x, y):
+ self.Row = x
+ self.Column = y
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.Row == other.Row and self.Column == other.Column
+
+ def __hash__(self):
+ return ((self.Row * 397) ** self.Column) * 397
+
+ def __str__(self):
+ return "(" + str(self.Row) + ", " + str(self.Column) + ")"
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/TaskSideChannel.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b5d1fba25f4def24a809c0552ee719bd7bef553
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/TaskSideChannel.py
@@ -0,0 +1,50 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from Task import Task, Position
+
+"""
+Class that processes info from the TaskSideChannel.
+- sends assignment of tasks to robots
+"""
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ print(msg.read_string())
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/main.py b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf0312bb53a914f0169c5b6fb6d78efb87298b3c
--- /dev/null
+++ b/Algorithms/Offline_Centralized/Algorithm_test_toyexample_offline_centralized/main.py
@@ -0,0 +1,44 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Main file that controls and decision making
+This is a toy example!
+Uses EnvironmentManager who assigns tasks in order to available agents
+and then computes shortest paths, no check on collisions.
+"""
+
+from mlagents_envs.environment import UnityEnvironment
+import EnvChannel, TaskSideChannel
+import EnvironmentManager
+
+# as long as simulation_going is true, new actions will be generated
+
+simulation_going = True
+
+# setup side channels and unity environment
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel])
+
+# set up environment manager
+envManager = EnvironmentManager.EnvironmentManager(envChannel.envData, env, envChannel, taskChannel)
+
+
+while simulation_going:
+ envManager.step()
+
+envManager.close()
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvChannel.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6d62add37419b9cfe1ddb9b07a70b150a07a6d7
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvChannel.py
@@ -0,0 +1,63 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel: stores all incoming information
+in an instance of class EnvironmentData
+"""
+
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.envData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.envData.matrix = environment_info["matrixLayout"]
+ self.envData.rotation = environment_info["rotationRobot"]
+ self.envData.size_of_action = environment_info["sizeOfAction"]
+ self.envData.size_of_observation = environment_info["sizeOfObservation"]
+ self.envData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.envData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.envData.can_go_backwards = environment_info["backwards"]
+ self.envData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.envData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.envData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.envData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.envData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentData.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..be6a9c1e66b598ea49fd5fba407962fc81bc9581
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentData.py
@@ -0,0 +1,102 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+stores data on environment
+instance methods that initialize environment (used by EnvChannel)
+- set_initial_nodes_dictionary(initial_nodes): initializes current_nodes_dictionary
+containing current(next) nodes for each of the robots
+- set_tasks_to_be_assigned(tasks): initialize tasks_to_be_assigned: tasks that have to be assigned
+- set_assigned_tasks(): initialize assigned_tasks_dictionary containing
+ for every robot the current assigned task
+
+instance methods that update environment info
+- assign_task(robot_instanceID, task): assign task to a given robot
+- add_task_to_tasks_to_be_assigned(task): append a new task to tasks_to_be_assigned
+- task_done(instance_id): remove task from robot that was assigned this task
+"""
+
+
+import json
+
+from Node import Node
+from Task import Position, Task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.size_of_action = None
+ self.size_of_observation = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Position(tasks[i]["PickUp"]["Row"], tasks[i]["PickUp"]["Column"])
+ delivery = Position(tasks[i]["Delivery"]["Row"], tasks[i]["Delivery"]["Column"])
+ release = tasks[i]["ReleaseTime"]
+ self.tasks_to_be_assigned.append(Task(pickup, delivery, release))
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ # info has to be sent to unity via the correct channel
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ self.tasks_to_be_assigned.remove(task)
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentManager.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..910a3108999159afa39573e29a13b95d94465168
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/EnvironmentManager.py
@@ -0,0 +1,196 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""'
+Class that contains and manages the information on the environment.
+- Initializes and holds information on the environment (EnvData).
+- Contains a reference to the sidechannel (EnvChannel).
+- Contains a reference to the sidechannel (TaskChannel).
+- Contains a reference to the unity environment (UnityEnv).
+Has instance methods
+- reset() which resets the environment.
+- step() which assigns tasks, computes a new action based on observation from the unity environment
+ and sends actions back to unity, updates info environment when a task has finished
+- close() which closes the environment/
+
+Has also some auxiliary instance methods:
+- assign_new_task() which finds a new task and a robot that is free, then calls
+assign_task_to_robot(robot_instanceID, task)
+- compute_path(current_node, task) which computes paths starting in current_node completing a certain task
+- initialize_shortest_paths() which initializes a dictionary containing the paths robots are following
+- assign_task_to_robot(robot_instanceID, task) which assigns task to a robot with given instance ID,
+updates EnvData and sends information on assignment back to Unity
+
+Note that this a toy example. No checks are done on collisions.
+"""
+
+import json
+
+from mlagents_envs.base_env import ActionTuple
+
+from Graph import Graph
+from Node import Node
+
+import numpy as np
+
+
+class EnvironmentManager:
+
+ def __init__(self, envData, unityEnv, envChannel, taskChannel):
+ self.envData = envData
+ self.unityEnv = unityEnv
+ self.envChannel = envChannel
+ self.taskChannel = taskChannel
+
+ # IMPORTANT: sends info through envChannel, to be called before setting up connection envData an taskChannel
+ self.reset()
+
+ # taskChannel needs info on envData
+ self.taskChannel.environment_data = envData
+
+ # behavior name for agent
+ self.behavior_name = list(unityEnv.behavior_specs.keys())[0]
+
+ # graph that is used for computing paths
+ self.graph = Graph(envData.matrix, envData.rotation, envData.can_go_backwards)
+
+ # for every robot with instanceID id shortest_paths[id] gives the shortest path to
+ # its currently assigned task
+ self.shortest_paths = {}
+
+ self.initialize_shortest_paths()
+
+ # step in the environment
+ # 1) checks if new tasks can be assigned
+ # 2) collect observations
+ # 3) compute new actions
+ # 4) send actions back to Unity
+ def step(self):
+ self.assign_new_task()
+
+ # get information from the agent that requests an action
+ decision_steps, terminal_steps = self.unityEnv.get_steps(self.behavior_name)
+
+ print("request is being made by")
+ agents = decision_steps.agent_id
+ print(str(len(agents)) + " agents")
+
+ # decision_steps.obs is a list of numpy arrays, first dimension corresponds to the number of agents that has requested
+ # a decision (here len(agents))
+ observations = decision_steps.obs
+
+ print("----------------------------------------")
+
+
+ actions = [] # long list of all actions
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # create an action for all agents that request an action
+ for i in range(len(agents)):
+ observation = observations[0][i] # observation of current agent
+ robot_id_unity = observation[0]
+ row = observation[1]
+ column = observation[2]
+ degrees = observation[3]
+
+ print("robot " + str(robot_id_unity) + " at node [(" + str(row) + "," + str(column) + ")," + str(
+ degrees) + "]")
+
+ path = self.shortest_paths[robot_id_unity]
+
+ # if path is empty, then robot should stay at same node
+ if len(path) == 0:
+ actions.extend([row, column, degrees, 0, 0])
+ # if path is not empty we get next action from shortest path
+ else:
+ next_action = path[0] # is a node or "pickup" or "deliver"
+ if next_action == "pickup":
+ actions.extend([row, column, degrees, 1, 0])
+ elif next_action == "deliver":
+ actions.extend([row, column, degrees, 0, 1])
+ self.envData.assigned_tasks_dictionary[robot_id_unity] = None
+ else:
+ actions.extend([next_action.x, next_action.y, next_action.g, 0, 0])
+ # update current node
+ self.envData.current_nodes_dictionary[robot_id_unity] = Node(next_action.x, next_action.y,
+ next_action.g)
+ # update path
+ self.shortest_paths[robot_id_unity] = path[1:]
+
+ # add action to action_tuple and set the actions
+ actions = np.array(actions)
+ actions.resize(len(agents), self.envData.size_of_action)
+ action_tuple.add_discrete(actions)
+
+ self.unityEnv.set_actions(self.behavior_name, action_tuple)
+ self.unityEnv.step() # send actions to unity and wait until a new decision is requested
+
+ # reset the environment
+ def reset(self):
+ self.unityEnv.reset()
+
+ # close the environment
+ def close(self):
+ self.close()
+
+ # assigns the next available task to an idle robot, if possible
+ def assign_new_task(self):
+ # check if there are tasks that have to be assigned
+ # if this is the case, get first task and assign to available robot
+ if len(self.envData.tasks_to_be_assigned) != 0:
+ task = self.envData.tasks_to_be_assigned[0]
+ # get available robot
+ for robot_id in self.envData.instance_ids:
+ if self.envData.assigned_tasks_dictionary[robot_id] is None:
+ # robot is free: assign task to robot and break out of for loop
+ self.assign_task_to_robot(robot_id, task)
+ break
+
+
+ # compute path from current_node that finishes given task
+ # for test purposes pick up and delivery need to have rotation 0
+ # at pick up delivery robot stays at same node
+ # returns a sequence of nodes and pick up/delivery actions
+ def compute_path(self, current_node, task):
+ path1 = self.graph.computeShortestPath(current_node,
+ Node(task.PickUp.Row, task.PickUp.Column, 0))
+ path1 = path1[1:]
+ path2 = self.graph.computeShortestPath(Node(task.PickUp.Row, task.PickUp.Column, 0),
+ Node(task.Delivery.Row, task.Delivery.Column, 0))
+ path2 = path2[1:]
+
+ return path1 + ["pickup"] + path2 + ["deliver"]
+
+
+ # for every robot initially there is an empty path to be followed
+ def initialize_shortest_paths(self):
+ for robot_id in self.envData.instance_ids:
+ self.shortest_paths[robot_id] = []
+
+ def assign_task_to_robot(self, robot_instanceID, task):
+ # update envData
+ self.envData.assign_task(robot_instanceID, task)
+ # compute path for robot
+ self.shortest_paths[robot_instanceID] = \
+ self.compute_path(self.envData.current_nodes_dictionary[robot_instanceID],task)
+ # send info to unity
+ taskDict = {"Task": {
+ "PickUp": {"Row": task.PickUp.Row, "Column": task.PickUp.Column},
+ "Delivery": {"Row": task.Delivery.Row, "Column": task.Delivery.Column},
+ "ReleaseTime": task.ReleaseTime},
+ "InstanceID": robot_instanceID}
+ self.taskChannel.send_string(json.dumps(taskDict))
+
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Graph.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e03b997172dae6855dca7f06890606239e7063
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Graph.py
@@ -0,0 +1,126 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from networkx import DiGraph, shortest_path
+from Node import Node
+
+"""
+Class Graph creates a graph based on matrix, deviation (unit of rotation) and whether robots can move backwards).
+Its nodes contain position in the grid and orientation wrt north (see class <c>Node</c>).
+Networkx package as underlying data structure.
+"""
+
+class Graph:
+ def __init__(self, matrix, deviation, cangobackwards):
+ self.matrix = matrix
+ self.number_of_rows = len(matrix)
+ self.number_of_columns = len(matrix[0])
+ self.deviation = deviation
+ self.cangobackwards = cangobackwards
+ self.graph = DiGraph()
+
+ for i in range(self.number_of_rows):
+ for j in range(self.number_of_columns):
+ # we have to add nodes to the graph when we encounter a number different than 1
+ if matrix[i][j] != 1:
+ self.addEdgesByRotation(i, j)
+ self.addEdgesByMovingForward(i,j)
+
+ def addEdgesByRotation(self, row, column):
+ degree = 0
+ while degree < 360:
+ newnode = Node(row, column, degree)
+
+ # add all edges from newnode staying at the same position (row, number), i.e. rotation
+
+ # rotate left
+ rotateLeftNode = Node(row, column, get_degree_in_range_0_360(degree - self.deviation))
+ self.graph.add_edge(newnode, rotateLeftNode)
+
+ # rotate right
+ rotateRightNode = Node(row, column, get_degree_in_range_0_360(degree + self.deviation))
+ self.graph.add_edge(newnode, rotateRightNode)
+
+ degree += self.deviation
+
+ def addEdgesByMovingForward(self, row, column):
+ # check left
+ if column > 0 and self.matrix[row][column-1] != 1:
+ # we can move forward if we have degree 270
+ currentnode = Node(row, column, 270)
+ neighbourleft = Node(row, column-1, 270)
+ self.graph.add_edge(currentnode, neighbourleft)
+ # we can move backward if we have degree 90
+ if self.cangobackwards:
+ currentnode = Node(row, column, 90)
+ neighbourleft = Node(row, column-1, 90)
+ self.graph.add_edge(currentnode, neighbourleft)
+
+ # check right
+ if column < self.number_of_columns -1 and self.matrix[row][column+1] != 1:
+ # we can move forward if we have degree 90
+ currentnode = Node(row, column, 90)
+ neighbourright = Node(row, column+1, 90)
+ self.graph.add_edge(currentnode, neighbourright)
+ # we can move backward if we have degree 270
+ if self.cangobackwards:
+ currentnode = Node(row, column, 270)
+ neighbourright = Node(row, column+1, 270)
+ self.graph.add_edge(currentnode, neighbourright)
+
+ # check up
+ if row > 0 and self.matrix[row-1][column] != 1:
+ # we can move forward if we have degree 0
+ currentnode = Node(row, column, 0)
+ neighbourup = Node(row-1, column, 0)
+ self.graph.add_edge(currentnode, neighbourup)
+ # we can move backward if we have degree 180
+ if self.cangobackwards:
+ currentnode = Node(row, column, 180)
+ neighbourup = Node(row-1, column, 180)
+ self.graph.add_edge(currentnode, neighbourup)
+
+ # check down
+ if row < self.number_of_rows-1 and self.matrix[row+1][column] != 1:
+ # we can move forward if we have degree 180
+ currentnode = Node(row, column, 180)
+ neighbourdown = Node(row+1, column, 180)
+ self.graph.add_edge(currentnode, neighbourdown)
+ # we can move backward if we have degree 0
+ if self.cangobackwards:
+ currentnode = Node(row, column, 0)
+ neighbourdown = Node(row+1, column, 0)
+ self.graph.add_edge(currentnode, neighbourdown)
+
+ def computeShortestPath(self, start_node, end_node):
+ #compute shortest path from start_node to end_node: return a list
+ return shortest_path(self.graph, start_node, end_node)
+
+def get_degree_in_range_0_360(degree):
+ rest = degree % 360
+ if rest < 0:
+ return rest + 360
+ return rest
+
+
+
+
+
+
+
+
+
+
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Node.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..126684f885f63b51edcd2b6684d26d7a847c51af
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Node.py
@@ -0,0 +1,57 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Node represent nodes in the graph. Every node contains a
+position (x,y) in the grid
+and a rotation in degree where
+0 = North
+90 = East
+280 = South
+270 = West
+all degrees must be between 0 and 360
+
+attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+"""
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Pipfile b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Task.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Task.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ed7c37501e17a97a2a21349c34d6220720ad380
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/Task.py
@@ -0,0 +1,53 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Task represents tasks which consist of 2 positions: a pickup and a delivery position and a release time.
+"""
+
+class Task:
+ def __init__(self, pos1, pos2, release):
+ self.PickUp = pos1
+ self.Delivery = pos2
+ self.ReleaseTime = release
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.PickUp == other.PickUp and\
+ self.Delivery == other.Delivery and self.ReleaseTime == other.ReleaseTime
+
+ def __hash__(self):
+ hash_code = hash(self.PickUp)
+ hash_code = (hash_code * 397) ** hash(self.Delivery)
+ hash_code = (hash_code * 397) ** hash(self.ReleaseTime)
+ return hash_code
+
+ def __str__(self):
+ return "[" + str(self.PickUp) + ", " + str(self.Delivery) + "]" + str(self.ReleaseTime)
+
+
+class Position:
+ def __init__(self, x, y):
+ self.Row = x
+ self.Column = y
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.Row == other.Row and self.Column == other.Column
+
+ def __hash__(self):
+ return ((self.Row * 397) ** self.Column) * 397
+
+ def __str__(self):
+ return "(" + str(self.Row) + ", " + str(self.Column) + ")"
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/TaskSideChannel.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f75664d7780ae5804ed869bebeac4a7815903e5
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/TaskSideChannel.py
@@ -0,0 +1,48 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+
+"""
+Class that processes info from the TaskSideChannel.
+- sends assignment of tasks to robots
+"""
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ print(msg.read_string())
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/main.py b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ac383f4edcaad078ae265e9a4a795e8d9f0810b
--- /dev/null
+++ b/Algorithms/Offline_Decentralized/Algorithm_test_toyexample_offline_decentralized/main.py
@@ -0,0 +1,44 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Main file that controls decision making for the Offline Decentralized Pick Up and Delivery problem.
+This is a toy example!
+Uses EnvironmentManager who assigns tasks in order to available agents
+and then computes shortest paths, no check on collisions.
+"""
+
+from mlagents_envs.environment import UnityEnvironment
+import EnvChannel, TaskSideChannel
+import EnvironmentManager
+
+# as long as simulation_going is true, new actions will be generated
+
+simulation_going = True
+
+# setup side channels and unity environment
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel])
+
+# set up environment manager
+envManager = EnvironmentManager.EnvironmentManager(envChannel.envData, env, envChannel, taskChannel)
+
+
+while simulation_going:
+ envManager.step()
+
+envManager.close()
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvChannel.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..142cf2c27d8f9fa7d090c79496901190bb8bf2f3
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvChannel.py
@@ -0,0 +1,46 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel: stores all incoming information
+in an instance of class EnvironmentData
+"""
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.envData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.envData.matrix = environment_info["matrixLayout"]
+ self.envData.rotation = environment_info["rotationRobot"]
+ self.envData.size_of_action = environment_info["sizeOfAction"]
+ self.envData.size_of_observation = environment_info["sizeOfObservation"]
+ self.envData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.envData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.envData.can_go_backwards = environment_info["backwards"]
+ self.envData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.envData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.envData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.envData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.envData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentData.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..bac66852ffcfdfe8f738b1e24f91b339772fb042
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentData.py
@@ -0,0 +1,82 @@
+"""
+stores data on environment
+instance methods that initialize environment (used by EnvChannel)
+- set_initial_nodes_dictionary(initial_nodes): initializes current_nodes_dictionary
+containing current(next) nodes for each of the robots
+- set_tasks_to_be_assigned(tasks): initialize tasks_to_be_assigned: tasks that have to be assigned
+- set_assigned_tasks(): initialize assigned_tasks_dictionary containing
+ for every robot the current assigned task
+
+instance methods that update environment info
+- assign_task(robot_instanceID, task): assign task to a given robot
+- add_task_to_tasks_to_be_assigned(task): append a new task to tasks_to_be_assigned
+- task_done(instance_id): remove task from robot that was assigned this task
+"""
+
+
+from Node import Node
+from Task import Position, Task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.size_of_action = None
+ self.size_of_observation = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Position(tasks[i]["PickUp"]["Row"], tasks[i]["PickUp"]["Column"])
+ delivery = Position(tasks[i]["Delivery"]["Row"], tasks[i]["Delivery"]["Column"])
+ self.tasks_to_be_assigned.append(Task(pickup, delivery))
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ self.tasks_to_be_assigned.remove(task)
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentManager.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..dab5fa9450b32f7f6029c56449f7c3641ab1ea56
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/EnvironmentManager.py
@@ -0,0 +1,186 @@
+"""'
+Class that contains and manages the information on the environment.
+- Initializes and holds information on the environment (EnvData).
+- Contains a reference to the sidechannel (EnvChannel).
+- Contains a reference to the sidechannel (TaskChannel).
+- Contains a reference to the unity environment (UnityEnv).
+Has instance methods
+- reset() which resets the environment.
+- step() which assigns tasks, computes a new action based on observation from the unity environment
+ and sends actions back to unity, updates info environment when a task has finished
+- close() which closes the environment/
+
+Has also some auxilary instance methods:
+- assign_new_task() which finds a new task and a robot that is free, then calls
+assign_task_to_robot(robot_instanceID, task)
+- compute_path(current_node, task) which computes paths starting in current_node completing a certain task
+- initialize_shortest_paths() which initializes a dictionary containing the paths robots are following
+- assign_task_to_robot(robot_instanceID, task) which assigns task to a robot with given instance ID,
+updates EnvData and sends information on assignment back to Unity
+
+Note that this a toy example. No checks are done on collisions.
+"""
+
+
+import json
+
+from mlagents_envs.base_env import ActionTuple
+
+from Graph import Graph
+from Node import Node
+
+import numpy as np
+
+
+class EnvironmentManager:
+
+ def __init__(self, envData, unityEnv, envChannel, taskChannel):
+ self.envData = envData
+ self.unityEnv = unityEnv
+ self.envChannel = envChannel
+ self.taskChannel = taskChannel
+
+ # IMPORTANT: sends info through envChannel, to be called before setting up connection envData an taskChannel
+ self.reset()
+
+ # taskChannel needs info on envData
+ self.taskChannel.environment_data = envData
+
+ # behavior name for agent
+ self.behavior_name = list(unityEnv.behavior_specs.keys())[0]
+
+ # graph that is used for computing paths
+ self.graph = Graph(envData.matrix, envData.rotation, envData.can_go_backwards)
+
+ # for every robot with instanceID id shortest_paths[id] gives the shortest path to
+ # its currently assigned task
+ self.shortest_paths = {}
+
+ self.initialize_shortest_paths()
+
+ # step in the environment
+ # 1) checks if new tasks can be assigned
+ # 2) collect observations
+ # 3) compute new actions
+ # 4) send actions back to Unity
+ def step(self):
+ self.assign_new_task()
+
+ # get information from the agent that requests an action
+ decision_steps, terminal_steps = self.unityEnv.get_steps(self.behavior_name)
+
+ # decision_steps.obs is a list of numpy arrays, first dimension corresponds to the number of agents that has requested
+ # a decision (here 1), the observation of the agent is the first element
+ observation = decision_steps.obs[0][0]
+
+ print("----------------------------------------")
+
+
+ actions = [] # long list of all actions
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # get observations per robot and store in dictionary observations[instance_id] -> observation
+ observations = {}
+ for i in range(self.envData.number_of_robots):
+ observation_i = observation[i * self.envData.size_of_observation:
+ i * self.envData.size_of_observation + self.envData.size_of_observation]
+ observations[observation_i[0]] = observation_i[1:]
+
+ # create an action for all robots and send at once to unity
+ for robot_id_unity in observations:
+
+ obs = observations[robot_id_unity]
+ print("robot " + str(robot_id_unity) + " at node [(" + str(obs[0]) + "," + str(obs[1]) + ")," + str(
+ obs[2]) + "]")
+
+ path = self.shortest_paths[robot_id_unity]
+ # if path is empty, then robot should stay at same node
+ if len(path) == 0:
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 0, 0])
+ # if path is not empty we get next action from shortest path
+ else:
+ next_action = path[0] # is node or "pickup" or "deliver"
+ if next_action == "pickup":
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 1, 0])
+ elif next_action == "deliver":
+ actions.extend([robot_id_unity, obs[0], obs[1], obs[2], 0, 1])
+ self.envData.assigned_tasks_dictionary[robot_id_unity] = None
+ else:
+ actions.extend([robot_id_unity, next_action.x, next_action.y, next_action.g, 0, 0])
+ # update current node
+ self.envData.current_nodes_dictionary[robot_id_unity] = Node(next_action.x, next_action.y,
+ next_action.g)
+ # update path
+ self.shortest_paths[robot_id_unity] = path[1:]
+
+ # add action to action_tuple and set the actions
+ actions = np.array(actions)
+ actions.resize(1, self.envData.size_of_action * self.envData.number_of_robots)
+ action_tuple.add_discrete(actions)
+
+ self.unityEnv.set_actions(self.behavior_name, action_tuple)
+ self.unityEnv.step() # send actions to unity and wait until a new decision is requested
+
+
+
+
+ # reset the environment
+ def reset(self):
+ self.unityEnv.reset()
+
+ # close the environment
+ def close(self):
+ self.close()
+
+ # assigns the next available task to an idle robot, if possible
+ def assign_new_task(self):
+ # check if there are tasks that have to be assigned
+ # if this is the case, get first task and assign to available robot
+ if len(self.envData.tasks_to_be_assigned) != 0:
+ task = self.envData.tasks_to_be_assigned[0]
+ # get available robot
+ for robot_id in self.envData.instance_ids:
+ if self.envData.assigned_tasks_dictionary[robot_id] is None:
+ # robot is free: assign task to robot and break out of for loop
+ self.assign_task_to_robot(robot_id, task)
+ break
+
+
+ # compute path from current_node that finishes given task
+ # for test purposes pick up and delivery need to have rotation 0
+ # at pick up delivery robot stays at same node
+ # returns a sequence of nodes and pick up/delivery actions
+ def compute_path(self, current_node, task):
+ path1 = self.graph.computeShortestPath(current_node,
+ Node(task.PickUp.Row, task.PickUp.Column, 0))
+ path1 = path1[1:]
+ path2 = self.graph.computeShortestPath(Node(task.PickUp.Row, task.PickUp.Column, 0),
+ Node(task.Delivery.Row, task.Delivery.Column, 0))
+ path2 = path2[1:]
+
+ return path1 + ["pickup"] + path2 + ["deliver"]
+
+
+ # for every robot initially there is an empty path to be followed
+ def initialize_shortest_paths(self):
+ for robot_id in self.envData.instance_ids:
+ self.shortest_paths[robot_id] = []
+
+ def assign_task_to_robot(self, robot_instanceID, task):
+ # update envData
+ self.envData.assign_task(robot_instanceID, task)
+ # compute path for robot
+ self.shortest_paths[robot_instanceID] = \
+ self.compute_path(self.envData.current_nodes_dictionary[robot_instanceID],task)
+ # send info to unity
+ taskDict = {"Task": {
+ "PickUp": {"Row": task.PickUp.Row, "Column": task.PickUp.Column},
+ "Delivery": {"Row": task.Delivery.Row, "Column": task.Delivery.Column}},
+ "InstanceID": robot_instanceID}
+ self.taskChannel.send_string(json.dumps(taskDict))
+
+
+
+
+
+
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Graph.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..c877def1fe99a2f5d82697167084c1a40dd0a30e
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Graph.py
@@ -0,0 +1,110 @@
+from networkx import DiGraph, shortest_path
+from Node import Node
+
+"""
+Class Graph creates a graph based on matrix, deviation (unit of rotation) and whether robots can move backwards).
+Its nodes contain position in the grid and orientation wrt north (see class <c>Node</c>).
+Networkx package as underlying data structure.
+"""
+
+class Graph:
+ def __init__(self, matrix, deviation, cangobackwards):
+ self.matrix = matrix
+ self.number_of_rows = len(matrix)
+ self.number_of_columns = len(matrix[0])
+ self.deviation = deviation
+ self.cangobackwards = cangobackwards
+ self.graph = DiGraph()
+
+ for i in range(self.number_of_rows):
+ for j in range(self.number_of_columns):
+ # we have to add nodes to the graph when we encounter a number different than 1
+ if matrix[i][j] != 1:
+ self.addEdgesByRotation(i, j)
+ self.addEdgesByMovingForward(i,j)
+
+ def addEdgesByRotation(self, row, column):
+ degree = 0
+ while degree < 360:
+ newnode = Node(row, column, degree)
+
+ # add all edges from newnode staying at the same position (row, number), i.e. rotation
+
+ # rotate left
+ rotateLeftNode = Node(row, column, get_degree_in_range_0_360(degree - self.deviation))
+ self.graph.add_edge(newnode, rotateLeftNode)
+
+ # rotate right
+ rotateRightNode = Node(row, column, get_degree_in_range_0_360(degree + self.deviation))
+ self.graph.add_edge(newnode, rotateRightNode)
+
+ degree += self.deviation
+
+ def addEdgesByMovingForward(self, row, column):
+ # check left
+ if column > 0 and self.matrix[row][column-1] != 1:
+ # we can move forward if we have degree 270
+ currentnode = Node(row, column, 270)
+ neighbourleft = Node(row, column-1, 270)
+ self.graph.add_edge(currentnode, neighbourleft)
+ # we can move backward if we have degree 90
+ if self.cangobackwards:
+ currentnode = Node(row, column, 90)
+ neighbourleft = Node(row, column-1, 90)
+ self.graph.add_edge(currentnode, neighbourleft)
+
+ # check right
+ if column < self.number_of_columns -1 and self.matrix[row][column+1] != 1:
+ # we can move forward if we have degree 90
+ currentnode = Node(row, column, 90)
+ neighbourright = Node(row, column+1, 90)
+ self.graph.add_edge(currentnode, neighbourright)
+ # we can move backward if we have degree 270
+ if self.cangobackwards:
+ currentnode = Node(row, column, 270)
+ neighbourright = Node(row, column+1, 270)
+ self.graph.add_edge(currentnode, neighbourright)
+
+ # check up
+ if row > 0 and self.matrix[row-1][column] != 1:
+ # we can move forward if we have degree 0
+ currentnode = Node(row, column, 0)
+ neighbourup = Node(row-1, column, 0)
+ self.graph.add_edge(currentnode, neighbourup)
+ # we can move backward if we have degree 180
+ if self.cangobackwards:
+ currentnode = Node(row, column, 180)
+ neighbourup = Node(row-1, column, 180)
+ self.graph.add_edge(currentnode, neighbourup)
+
+ # check down
+ if row < self.number_of_rows-1 and self.matrix[row+1][column] != 1:
+ # we can move forward if we have degree 180
+ currentnode = Node(row, column, 180)
+ neighbourdown = Node(row+1, column, 180)
+ self.graph.add_edge(currentnode, neighbourdown)
+ # we can move backward if we have degree 0
+ if self.cangobackwards:
+ currentnode = Node(row, column, 0)
+ neighbourdown = Node(row+1, column, 0)
+ self.graph.add_edge(currentnode, neighbourdown)
+
+ def computeShortestPath(self, start_node, end_node):
+ #compute shortest path from start_node to end_node: return a list
+ return shortest_path(self.graph, start_node, end_node)
+
+def get_degree_in_range_0_360(degree):
+ rest = degree % 360
+ if rest < 0:
+ return rest + 360
+ return rest
+
+
+
+
+
+
+
+
+
+
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Node.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7dc823be7e503837647d38290413521b04c1512
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Node.py
@@ -0,0 +1,41 @@
+"""
+Class Node represent nodes in the graph. Every node contains a
+position (x,y) in the grid
+and a rotation in degree where
+0 = North
+90 = East
+280 = South
+270 = West
+all degrees must be between 0 and 360
+
+attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+"""
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Pipfile b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Task.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Task.py
new file mode 100644
index 0000000000000000000000000000000000000000..286d0d8254452110b0f491296120de590b9a642c
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/Task.py
@@ -0,0 +1,31 @@
+"""
+Class Task represents tasks which consist of 2 positions: a pickup and a delivery position.
+"""
+class Task:
+ def __init__(self, pos1, pos2):
+ self.PickUp = pos1
+ self.Delivery = pos2
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.PickUp == other.PickUp and self.Delivery == other.Delivery
+
+ def __hash__(self):
+ return ((hash(self.PickUp) * 397) ** hash(self.Delivery)) * 397
+
+ def __str__(self):
+ return "[" + str(self.PickUp) + ", " + str(self.Delivery) + "]"
+
+
+class Position:
+ def __init__(self, x, y):
+ self.Row = x
+ self.Column = y
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.Row == other.Row and self.Column == other.Column
+
+ def __hash__(self):
+ return ((self.Row * 397) ** self.Column) * 397
+
+ def __str__(self):
+ return "(" + str(self.Row) + ", " + str(self.Column) + ")"
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/TaskSideChannel.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..a541d459d56aec589f8e20b09cd7f5485b92dcd4
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/TaskSideChannel.py
@@ -0,0 +1,41 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from Task import Task, Position
+
+"""
+Class that processes info from the TaskSideChannel.
+- receives new tasks that have to assigned to robots
+- sends assignment of tasks to robots
+"""
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ data = msg.read_string()
+ new_task = json.loads(data)
+ new_task = new_task["task"]
+ task_to_add = Task(Position(new_task["PickUp"]["Row"], new_task["PickUp"]["Column"]),
+ Position(new_task["Delivery"]["Row"], new_task["Delivery"]["Column"]))
+ # add task to tasks to be assigned
+ self.environment_data.add_task_to_tasks_to_be_assigned(task_to_add)
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/main.py b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..3768c19ce07c27f85c2ab1aad60ff2c707e0dfce
--- /dev/null
+++ b/Algorithms/Online_Centralized/Algorithm_test_toyexample_online_centralized/main.py
@@ -0,0 +1,28 @@
+"""
+Main file that controls and decision making
+This is a toy example!
+Uses EnvironmentManager who assigns tasks in order to available agents
+and then computes shortest paths, no check on collisions.
+"""
+
+from mlagents_envs.environment import UnityEnvironment
+import EnvChannel, TaskSideChannel
+import EnvironmentManager
+
+# as long as simulation_going is true, new actions will be generated
+
+simulation_going = True
+
+# setup side channels and unity environment
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel])
+
+# set up environment manager
+envManager = EnvironmentManager.EnvironmentManager(envChannel.envData, env, envChannel, taskChannel)
+
+
+while simulation_going:
+ envManager.step()
+
+envManager.close()
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvChannel.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..89909eccb160d3bebb15087e72541ddbdd4ce2fd
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvChannel.py
@@ -0,0 +1,45 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel
+"""
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.EnvData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.EnvData.matrix = environment_info["matrixLayout"]
+ self.EnvData.rotation = environment_info["rotationRobot"]
+ self.EnvData.action_size = environment_info["sizeOfAction"]
+ self.EnvData.observation_size = environment_info["sizeOfObservation"]
+ self.EnvData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.EnvData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.EnvData.can_go_backwards = environment_info["backwards"]
+ self.EnvData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.EnvData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.EnvData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.EnvData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.EnvData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvironmentData.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..82b6b1b7ee71726bd23a94aedbfea83657c7e988
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/EnvironmentData.py
@@ -0,0 +1,91 @@
+# stores data on environment
+# initialized when initial data is being sent through EnvChannel
+import json
+
+from Node import Node
+from ta_state import Location
+import tp_task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.action_size = None
+ self.observation_size = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+ self.task_side_channel = None
+
+ self.tasksCreated = 0
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # set_initial_nodes_dictionary
+ # set_tasks_to_be_assigned
+ # set_assigned_tasks
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+
+ for i in range(self.number_of_current_tasks):
+ pickup = Location(tasks[i]["PickUp"]["Column"], tasks[i]["PickUp"]["Row"], 0)
+ delivery = Location(tasks[i]["Delivery"]["Column"], tasks[i]["Delivery"]["Row"], 0)
+ releaseTime = tasks[i]["ReleaseTime"]
+ task = tp_task.Task(tp_task.CURRENT_TASK_ID, pickup, delivery, releaseTime=releaseTime)
+ self.tasks_to_be_assigned.append(task)
+
+ tp_task.CURRENT_TASK_ID += 1
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ # info has to be sent to unity via the correct channel
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ # self.tasks_to_be_assigned.remove(task)
+ taskDict = {"Task": {
+ "PickUp": {"Column": str(task.pickup.col), "Row": str(task.pickup.row)},
+ "Delivery": {"Column": str(task.delivery.col), "Row": str(task.delivery.row)},
+ "ReleaseTime": str(task.releaseTime)},
+ "InstanceID": str(robot_instanceID)}
+
+ self.task_side_channel.send_string(json.dumps(taskDict))
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Node.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2bb07c8fdf941ab68a8e8c87e8478b6d2e79aba
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Node.py
@@ -0,0 +1,39 @@
+# Class Node represent nodes in the graph. Every node contains a
+# position (x,y) in the grid
+# and a rotation in degree where
+# 0 = North
+# 90 = East
+# 280 = South
+# 270 = West
+# all degrees must be between 0 and 360
+
+# attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Pipfile b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Readme.md b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c6b638256050c47eaa46819580e4985de7064d6
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/Readme.md
@@ -0,0 +1,52 @@
+# Token Passing (TP)
+
+The Token-Passing (TP) algorithm is a decentralized algorithm for the online variant of the MAPD problem.
+
+In the online variant of the MAPD problem the following is supported:
+- Using randomly generated tasks (randomtasks = True)
+- Using tasks from a taskslist (bv. ../config/tasks.txt)
+ -> When using a taskslist with releaseTimes: releaseTimes are ignored (only used in offline variant)
+- Tested from 1 agent up untill 50 agents (significant lag for larger amount of agents)
+
+## Running the algorithm
+
+1. Install the virtual environment
+ - pipenv install
+2. Run the "decentralized_decisionmaker_shortest_path.py" file
+ - pipenv run python decentralized_decisionmaker_shortest_path.py
+3. Start the game inside the Unity editor
+
+## Working of the algorithm
+
+This implementation is based on the "Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks" paper (https://arxiv.org/pdf/1705.10868.pdf).
+An explanation of the algorithm is available at:
+ - Section 4.1 of the "Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks" paper
+ - Section 2.2.1 of the paper provided in the "paper" directory
+
+General overview of working:
+ - Token contains:
+ * Paths of all agents
+ * Taskset
+ * Assignments of tasks of all agents
+ - In each timestep each agent moves one step following its path in the token
+ - When an agent reaches the end of its path (= free agent) it looks for a task:
+ - Task found:
+ * Plan path to execute task and update token
+ * Remove the selected task from the task set
+ - No task found:
+ * Plan path to safe location and update token
+
+## Overview of the provided files
+
+Main file:
+- decentralized_decisionmaker_shortest_path.py: Main file that runs the algorithm
+
+Token Passing:
+- tp_agent.py: Agent that are executing tasks
+- tp_system: System manages the agents and tasks
+- tp_task: Pickup and delivery tasks
+- tp_token: Token that is used for communicating global information between the agents
+
+A-star:
+- ta_astar.py: A-Star algorithm
+- ta_state.py: Contains Location and state classes used for the path planning
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/TaskSideChannel.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f03f1fbc3e78a07d30314469a72777623e13e7f
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/TaskSideChannel.py
@@ -0,0 +1,39 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+import tp_task
+from ta_state import Location
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ data = msg.read_string()
+ new_task = json.loads(data)
+ new_task = new_task["task"]
+ task_to_add = tp_task.Task(tp_task.CURRENT_TASK_ID,
+ Location(new_task["PickUp"]["Column"], new_task["PickUp"]["Row"], 0),
+ Location(new_task["Delivery"]["Column"], new_task["Delivery"]["Row"], 0),
+ new_task["ReleaseTime"])
+ tp_task.CURRENT_TASK_ID += 1
+ # add task to task to be assigned
+ self.environment_data.add_task_to_tasks_to_be_assigned(task_to_add)
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/decentralized_decisionmaker_shortest_path.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/decentralized_decisionmaker_shortest_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc5f1b5513a0f597bd3a231eb5629d0944afe9df
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/decentralized_decisionmaker_shortest_path.py
@@ -0,0 +1,163 @@
+##------------------##
+## Python libraries ##
+##------------------##
+
+import sys
+import numpy as np
+
+##-----------##
+## ML-agents ##
+##-----------##
+
+from mlagents_envs.environment import ActionTuple, UnityEnvironment
+import EnvChannel
+import TaskSideChannel
+from Node import Node
+
+##----##
+## TP ##
+##----##
+
+from tp_system import System as TP_System
+from ta_state import Location
+from tp_task import Task
+
+
+
+simulation_going = True
+
+# Load the side channels and Unity environment.
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel]) # Open unity and press play to execute
+env.reset() # reset the environment and get info about environment through envChannel
+
+# Specify the used behaviour name for steering the agent
+behavior_names = list(env.behavior_specs.keys())
+behavior_name = behavior_names[0]
+
+# info obtained from environment
+environment_info = envChannel.EnvData
+# set up links between task side channel and environment info
+environment_info.task_side_channel = taskChannel
+taskChannel.environment_data = environment_info
+
+# Retrieve information from environment
+matrix = environment_info.matrix
+number_of_robots = environment_info.number_of_robots
+rotation = environment_info.rotation
+observation_size = environment_info.observation_size
+action_size = environment_info.action_size
+robot_can_go_backwards = environment_info.can_go_backwards
+
+# Initialize system
+system = TP_System(matrix, rotation)
+
+# Callback called when an agent assigns itself a task
+def on_task_assigned(agent_id, task_id):
+ environment_info.assign_task(agent_id, system.get_task(task_id))
+
+##----------------##
+## Robot Creation ##
+##----------------##
+
+# Number of agents in the configuration file
+n_agents = len(environment_info.instance_ids)
+
+# Create all the agents
+for robot_id, start_node in environment_info.current_nodes_dictionary.items():
+ # Convert the Node that represents the agent's location to a Location
+ robot_position = Location(start_node.y, start_node.x, start_node.g)
+ # Create agent
+ system.add_agent(robot_id, robot_position, on_task_assigned)
+
+# Each agent requests 1 action in each timestep
+# next timestep starts when all agents have requested an action in this timestep
+actions_requested_in_timestep = 0
+# Keep track of current timestep
+timestep = 0
+
+while simulation_going:
+ # If there are new tasks that need to be assigned add them to the task set
+ if len(environment_info.tasks_to_be_assigned) != 0:
+ for task in environment_info.tasks_to_be_assigned:
+ # Add task to task set
+ system.add_task(task)
+ # Task is already in task set, remove it from this list to avoid duplicates in task set
+ environment_info.tasks_to_be_assigned.remove(task)
+
+ # Get information on the agents that request an action
+ decision_steps, terminal_steps = env.get_steps(behavior_name)
+ agents = decision_steps.agent_id
+ observations = decision_steps.obs
+
+ # Actions that will be sent back to Unity
+ actions = []
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # Create an action for all agents that request an action
+ for i in range(len(agents)):
+
+ ##--------------##
+ ## OBSERVATIONS ##
+ ##--------------##
+
+ # Keep track of how many agents have requested an action in this timestep
+ actions_requested_in_timestep += 1
+
+ # Collect agent's observations
+ observation = observations[0][i]
+ # Extract observations
+ robot_id_unity = observation[0] # robot id in unity used to get correct precomputed path
+ robot_pos_x = observation[2]
+ robot_pos_y = observation[1]
+ robot_rot = observation[3]
+ # Convert coordinates to the agent's current position
+ robot_position = Location(robot_pos_x, robot_pos_y, robot_rot)
+
+ # Get Python Agent that represents robot
+ robot = system.get_agent(int(robot_id_unity))
+
+ ##-------------##
+ ## FOLLOW PATH ##
+ ##-------------##
+
+ # Follow previously computed path or plan new path when at the end of path
+ next_position = robot.move(system.token, timestep)
+ # Convert Location to Node
+ next_action = Node(next_position.row, next_position.col, next_position.rotation)
+ # Check if a task needs to be picked up / delivered
+ pickup_action = 0
+ delivery_action = 0
+ if next_position.to_pickup:
+ # Agent has to pick up his task
+ pickup_action = 1
+ elif next_position.to_deliver:
+ # Agent has to deliver his task
+ delivery_action = 1
+ # Agent has no task assigned anymore as the task is delivered
+ system.token.assignments[robot_id_unity] = None
+
+ # Send action to agent in Unity
+ actions.extend([next_action.x, next_action.y, next_action.g, pickup_action, delivery_action])
+
+ # When all agents have moved in this timestep update the timestep
+ if (actions_requested_in_timestep == n_agents):
+ # Update the timestep
+ timestep += 1
+ # Reset the number of agents that have requested a timestep in this timestep
+ actions_requested_in_timestep = 0
+
+ # Send actions to Unity
+ actions = np.array(actions)
+ actions.resize(len(agents), action_size)
+ action_tuple.add_discrete(actions)
+
+ env.set_actions(behavior_name, action_tuple)
+ env.step()
+
+
+
+
+
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/paper/Bachelorthesis.pdf b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/paper/Bachelorthesis.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf4fce9fccb3903a7f502782e4292f8c59e23d
Binary files /dev/null and b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/paper/Bachelorthesis.pdf differ
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_astar.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_astar.py
new file mode 100644
index 0000000000000000000000000000000000000000..975ff6a58ab1b48d69e3704354ce14919b0c315e
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_astar.py
@@ -0,0 +1,164 @@
+"""
+Created on Wed Apr 22 07:43:03 2020
+
+@author: Pieter Cawood
+
+"""
+from heapq import *
+from ta_state import *
+
+def get_heuristic(start_location, goal_location, rotation):
+ # Admissible heuristic - manhattan distance
+ return abs(start_location.col - goal_location.col) + abs(start_location.row - goal_location.row)
+
+class AStar:
+ def __init__(self, world, rotation):
+ self.rotation = rotation
+ self.world = world
+ self.dim_x = len(world[0])
+ self.dim_y = len(world)
+ self.obstacles = {}
+ self.path_ends = dict()
+ self.other_agent_paths = []
+
+ def state_possible(self, current_state, new_state):
+ # Check if wall
+ not_obstacle = False
+ if new_state.location not in self.obstacles:
+ not_obstacle = True
+ # Check if location is in dimensions of grid and does not collide with another location/wall
+ return 0 <= new_state.location.col < self.dim_x and 0 <= new_state.location.row < self.dim_y \
+ and not_obstacle and not self.is_collision(current_state, new_state)
+
+ def is_collision(self, current_state, new_state):
+ for other_agent_path in self.other_agent_paths:
+ # Vertex collision
+ if new_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == new_state.location.row and other_agent_path[new_state.time].col == new_state.location.col:
+ return True
+ # Edge collision
+ if new_state.time in other_agent_path and current_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == current_state.location.row and \
+ other_agent_path[new_state.time].col == current_state.location.col and \
+ other_agent_path[current_state.time].row == new_state.location.row and \
+ other_agent_path[current_state.time].col == new_state.location.col:
+ return True
+ # Last position of agent is his parking location
+ if new_state.location in self.path_ends:
+ busy_time = self.path_ends[new_state.location]
+ if new_state.time >= busy_time:
+ return True
+ return False
+
+ def get_neighbours(self, current_state):
+ neighbours = []
+ neighbour_time_step = current_state.time + 1
+ # Wait in state
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ # Add rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation+self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ # Remove rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation-self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ if current_state.location.rotation == 0:
+ # Move down
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row - 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 90:
+ # Move right
+ new_state = State(neighbour_time_step, Location(current_state.location.col + 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 180:
+ # Move up
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row + 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 270:
+ # Move left
+ new_state = State(neighbour_time_step, Location(current_state.location.col - 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ return neighbours
+
+ def create_obstacles(self, other_agent_paths):
+ n_rotations = int(360/self.rotation)
+ # Keep track of all walls in the environment
+ for y in range(self.dim_y):
+ for x in range(self.dim_x):
+ # 1 represent a wall in the environment
+ if self.world[y][x] == 1:
+ # A wall is always a collision, no matter the rotation
+ for i in range(n_rotations):
+ rot = i * self.rotation
+ self.obstacles[Location(x, y, rot)] = True
+ # Keep track of where the paths of the other agents end
+ self.path_ends = dict()
+ for path in other_agent_paths:
+ # Get the last timestep in the path of the agent
+ last_time = list(path)[-1]
+ # Get the last location in the path of the agent
+ last_pos = path[last_time]
+ # Two agents always collide when they share the same location, no matter the rotation
+ for i in range(n_rotations):
+ rot = i * self.rotation
+ self.path_ends[Location(last_pos.col, last_pos.row, rot)] = last_time
+ # Keep track of the other paths of agents to detect collisions
+ self.other_agent_paths = other_agent_paths
+
+ # Returns the latest timestep at which a specific location will be used
+ def find_lastest_use(self, location):
+ # Keep track of all the uses of that location
+ uses = []
+ # Check in the path of each agent if that agent uses that location, if yes, add it to the list
+ for agent_path in self.other_agent_paths:
+ for t, loc in agent_path.items():
+ if loc.col == location.col and loc.row == location.row:
+ uses.append(t)
+ # If the location is used at least once, return the maximum
+ if uses:
+ return max(uses)
+ # If the location is never used, return 0
+ else:
+ return 0
+
+
+ def search(self, time_step, start_location, goal_location, other_agent_paths,
+ release_time=0, find_step = 0):
+ self.create_obstacles(other_agent_paths)
+ initial_state = State(time_step, start_location)
+ # Find the lastest use of the goal location to avoid parking on a location that is still going to be used
+ lastest_use = self.find_lastest_use(goal_location)
+ came_from = {}
+ open_list = [initial_state]
+ closed_set = set()
+ initial_state.g_score = 0
+ initial_state.f_score = get_heuristic(start_location, goal_location, self.rotation)
+ while open_list:
+ current = heappop(open_list)
+ if current.location == goal_location:
+ if current in came_from and came_from[current].location == goal_location:
+ if current.time > lastest_use:
+ new_path = [current]
+ while current in came_from:
+ current = came_from[current]
+ new_path.append(current)
+ # Reverse path
+ return new_path[::-1]
+ closed_set |= {current}
+ neighbours = self.get_neighbours(current)
+ neighbour: State
+ for neighbour in neighbours:
+ # Only explore new states
+ if (neighbour not in closed_set) and (neighbour not in open_list):
+ came_from[neighbour] = current
+ neighbour.g = current.g + 1
+ neighbour.f = neighbour.g + get_heuristic(neighbour.location, goal_location, self.rotation)
+ heappush(open_list, neighbour)
+ return False
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_state.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6269547d945284d4fcff1503617d8a2e44b8ff4
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/ta_state.py
@@ -0,0 +1,60 @@
+"""
+Created on Sat Apr 18 10:45:11 2020
+
+@author: Pieter Cawood
+
+"""
+
+def h(pos1, pos2):
+ return abs(pos1.col - pos2.col) + abs(pos1.row - pos2.row)
+
+class Location(object):
+ def __init__(self, col, row, rotation):
+ self.col = col
+ self.row = row
+ self.rotation = rotation
+
+ # Relevant information for pickup and delivery actions
+ self.task_id = None
+ self.to_pickup = False
+ self.to_deliver = False
+
+ def __eq__(self, other):
+ return self.col == other.col and self.row == other.row and self.rotation == other.rotation
+
+ def __hash__(self):
+ return hash(str(self.col) + "_" + str(self.row) + "_" + str(self.rotation))
+
+ def __str__(self):
+ return str((self.col, self.row, self.rotation, self.to_pickup, self.to_deliver))
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"Location({self.col}, {self.row}, {self.rotation}, {self.to_pickup}, {self.to_deliver})"
+
+
+class State(object):
+ def __init__(self, time, location):
+ self.time = time
+ self.location = location
+ self.g = 0
+ self.f = 0
+
+ def __eq__(self, other):
+ return self.time == other.time and self.location == other.location
+
+ def __hash__(self):
+ return hash(str(self.time) + str(self.location.col) + str(self.location.row))
+
+ def __str__(self):
+ return str((self.time, self.location.col, self.location.row))
+
+ def __lt__(self, other):
+ if self.f < other.f:
+ return True
+ else:
+ return False
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"State(Time: {self.time}, Location(x: {self.location.col}, y: {self.location.row}, r: {self.location.rotation}))"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_agent.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..092eb890db6d8d7decf3c9f8bf046c5cc60dcc09
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_agent.py
@@ -0,0 +1,161 @@
+# Python modules
+import sys
+import numpy as np
+# Pathfinding modules
+from ta_astar import AStar
+from ta_state import Location, h
+
+
+class Agent:
+ def __init__(self, id, initialLocation, system, environment, rotation, on_task_assigned):
+ self.id = id
+ # Position
+ self.pos = initialLocation
+ # Plan paths
+ self.a_star = AStar(environment, rotation)
+ # Used to request token when looking for a task
+ self.system = system
+
+ self.on_task_assigned = on_task_assigned
+
+ def look_for_task(self, token, timestep):
+ availableTasks = [] # List of all the tasks that are available to the agent
+
+ ##-- Check if pickup/delivery is not end of path of any other agent --##
+ # Get last locations of all paths in token
+ pathEnds = token.get_pathends(self.id)
+ # If pickup/delivery is not in pathEnds it's an available task
+ for t in token.taskSet:
+ if t.pickup not in pathEnds and t.delivery not in pathEnds:
+ availableTasks.append(t)
+ # If there is no available task plan a path to a location where the agent can wait
+ if len(availableTasks) == 0:
+ self.path2(token, timestep)
+ # There is at least one task the agent can execute
+ else:
+ # Find the "best" task (best = minimum h-value)
+ h_scores = np.array(map(lambda t: h(self.pos, t.pickup), availableTasks))
+ min_task = availableTasks[np.argmin(h_scores)]
+ # Plan path for that task
+ self.path1(token, min_task, timestep)
+
+
+ def path1(self, token, task, timestep):
+ """[summary]
+ Args:
+ token (Token): The global token provided by the system
+ task (Task): The task for which a path needs to be planned
+ timestep (int): Current timestep
+ """
+
+ # All needed locations for the pathplanning
+ current_location = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ pickup_location = Location(task.pickup.col, task.pickup.row, task.pickup.rotation)
+ delivery_location = Location(task.delivery.col, task.delivery.row, task.delivery.rotation)
+
+ ##----------------##
+ ## Path to pickup ##
+ ##----------------##
+
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the current location of the agent to the pickup location of the task
+ pickup_path = self.a_star.search(timestep, current_location, pickup_location, other_agent_paths)
+ # There should always be a path if the problem is well formed
+ if not pickup_path:
+ raise ValueError("[TP] No path to pickup location - Problem not well formed")
+ # Add newly planned path to the agent's path in the token
+ for state in pickup_path:
+ token.paths[self.id][state.time] = state.location
+ old_timestep = timestep
+
+ # Update the timestep to the timestep at the end of the planned path
+ timestep = timestep + len(pickup_path) - 1
+
+ timestep_at_pickup = timestep
+
+ ##------------------##
+ ## Path to delivery ##
+ ##------------------##
+
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the pickup location of the task to the delivery location of the task
+ delivery_path = self.a_star.search(timestep, pickup_location, delivery_location, other_agent_paths)
+ # There should always be a path if the problem is well formed
+ if not delivery_path:
+ raise ValueError("[TP] No path to delivery location - Problem not well formed")
+ # Add newly planned path to the agent's path in the token
+ for state in delivery_path:
+ token.paths[self.id][state.time] = state.location
+
+ timestep = timestep + len(delivery_path) - 1
+
+ ##---------------------------##
+ ## Pickup / Delivery actions ##
+ ##---------------------------##
+
+ # Register in the last location of the path to the pickup location that the agent needs to pickup a task and register the task ID
+ token.paths[self.id][timestep_at_pickup].to_pickup = True
+ token.paths[self.id][timestep_at_pickup].task_id = task.id
+
+ # Register in the last location of the path to the delivery location that the agent needs to pickup a task and register the task ID
+ token.paths[self.id][timestep].to_deliver = True
+ token.paths[self.id][timestep].task_id = task.id
+
+ # Assign task to agent
+ token.assignments[self.id] = task.id
+ # Remove the task from the taskset
+ token.taskSet.remove(task)
+
+ def path2(self, token, timestep):
+ ##-- Check if current location is not delivery location of any task in taskSet --##
+ # Indicates whether agent needs to move
+ needToMove = False
+ for t in token.taskSet:
+ # If the agent is parked on the delivery location of a task he needs to move
+ if t.delivery == self.pos:
+ needToMove = True
+ if not needToMove:
+ # Agent doesn't need to move, update the path with a trivial path to current position
+ token.paths[self.id][timestep] = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ else:
+ ##-- Find a free endpoint to move to --##
+ endpoints = self.system.get_free_endpoints()
+ # Select the best endpoint (best = smallest h-score)
+ h_scores = list(map(lambda e: h(self.pos, e), endpoints))
+ chosen_endpoint = endpoints[np.argmin(h_scores)]
+
+ ##-- Plan path to free endpoint --##
+ # Make sure planned path doesn't collide with already planned paths
+ current_location = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ endpoint_location = Location(chosen_endpoint.col, chosen_endpoint.row, chosen_endpoint.rotation)
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the agent's current location to the chosen endpoint
+ path_to_endpoint = self.a_star.search(timestep, current_location, endpoint_location, other_agent_paths)
+ # There should always be a path if the problem is well formed
+ if not path_to_endpoint:
+ raise ValueError("[TP] No path to chosen endpoint - Problem not well formed")
+ # Add newly planned path to the agent's path in the token
+ for state in path_to_endpoint:
+ token.paths[self.id][state.time] = state.location
+
+ def move(self, token, timestep):
+ # Get the current path of the agent
+ path = token.paths[self.id]
+ # Check if the agent has already planned a move for this timestep
+ if timestep not in path:
+ # If agent has not yet planned a move for this timestep it requests the token and looks for a task
+ # Start looking for a new task
+ self.look_for_task(token, timestep)
+ # Check if the agent has assigned a task to itself and notify Unity if that is the case (using callback)
+ if token.assignments[self.id] is not None:
+ self.on_task_assigned(self.id, token.assignments[self.id])
+
+ # Select the move of the agent in this timestep
+ next_move = token.paths[self.id][timestep]
+ # Update the agent's position
+ self.pos = next_move
+ # Return the agent's next position
+ return next_move
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_system.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..121ce15369f3709c23c5101f0b56231b78fd3334
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_system.py
@@ -0,0 +1,104 @@
+# Python modules
+import sys
+import numpy as np
+# Pathfinding modules
+from ta_state import Location
+# Own modules
+from tp_agent import Agent
+from tp_token import Token
+
+# System manages the token
+class System:
+ def __init__(self, env, rotation):
+ # Keep track of the warehouse setup
+ self.environment = env
+ # Keep track of rotation
+ self.rotation = rotation
+
+ # Keep track of all agents in the system
+ self.agents = dict()
+ # Keep track of all tasks
+ self.tasks = dict()
+ # Token setup
+ self.token = Token()
+ # Keep track of all the non-task/task endpoints
+ self.task_endpoints = []
+ self.non_task_endpoints = []
+ # Get all the endpoints from the environment
+ self.init_endpoints()
+
+ # Find all the task and non-task endpoints in the environment
+ def init_endpoints(self):
+ # Get the dimensions of the environment
+ dim_y = len(self.environment)
+ dim_x = len(self.environment[0])
+ for y in range(dim_y):
+ for x in range(dim_x):
+ # Non-task endpoints (parking locations) are represented by 2
+ if self.environment[y][x] == 2:
+ self.non_task_endpoints.append(Location(x, y, 0))
+ # Task endpoints (pickup/delivery locations) are represented by 3
+ elif self.environment[y][x] == 3:
+ self.task_endpoints.append(Location(x, y, 0))
+
+ ##---------##
+ ## Getters ##
+ ##---------##
+
+ def get_agent(self, a_id):
+ return self.agents[a_id]
+
+ def get_task(self, task_id):
+ return self.tasks[task_id]
+
+ def get_endpoints(self):
+ return np.concatenate((self.non_task_endpoints, self.task_endpoints))
+
+ def get_non_task_endpoints(self):
+ return self.non_task_endpoints
+
+ def get_free_endpoints(self):
+ # List of all the endpoints
+ all_endpoints = self.get_endpoints()
+ free_endpoints = []
+ # Get a list of the last positions of each agent's path
+ endPaths = self.token.get_pathends()
+ # Generate a list of all the delivery locations of the tasks in the taskSet
+ deliveryLocations = []
+ for t in self.token.taskSet:
+ deliveryLocations.append(t.delivery)
+ for e in all_endpoints:
+ # If endpoint is not the end of the path of any agent
+ # or is not the delivery location of any task it's free
+ if e not in endPaths and e not in deliveryLocations:
+ free_endpoints.append(e)
+ return free_endpoints
+
+ ##---------------------##
+ ## Adding new elements ##
+ ##---------------------##
+
+ def add_task(self, task):
+ self.token.add_task(task)
+ self.tasks[task.id] = task
+ return True
+
+ def add_agent(self, agent_id, agent_pos, on_task_assigned):
+ agent = Agent(agent_id, agent_pos, self, self.environment, self.rotation, on_task_assigned)
+ self.agents[agent_id] = agent
+ self.token.add_agent(agent)
+
+ ##-------------------##
+ ## Utilitary methods ##
+ ##-------------------##
+
+ def request_token(self):
+ return self.token
+
+ def is_endpoint(self, pos):
+ if pos in self.non_task_endpoints or pos in self.task_endpoints:
+ return True
+ else:
+ return False
+
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_task.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..105c02eabdb1cc0c4828f5b811455118e4e3bd22
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_task.py
@@ -0,0 +1,43 @@
+CURRENT_TASK_ID = 0
+
+class Task:
+ """ Represents a task in the pickup & delivery problem
+
+ Attributes:
+ id: int
+ Identifier of the task
+ pickupLocation: Position
+ The location where the task needs to be picked up
+ deliveryLocation: Position
+ The location where the task needs to be delivered
+ """
+ def __init__(self, id, pickupLocation, deliveryLocation, releaseTime=0):
+ self.id = id
+
+ self.pickup = pickupLocation
+ self.delivery = deliveryLocation
+
+ self.releaseTime = releaseTime
+
+ # Keep track timestep when task enters the taskset and when it's executed => calculating service time
+ self.entered = None
+ self.delivered = None
+
+ def service_time(self):
+ if self.entered is not None and self.delivered is not None:
+ return self.delivered - self.entered
+ else:
+ return None
+
+ def __eq__(self, other):
+ return self.pickup == other.pickup and self.delivery == other.delivery
+
+ def __hash__(self):
+ return ((hash(self.pickup) * 397) ** hash(self.delivery)) * 397
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"Task({self.id},Pickup({self.pickup}), Delivery({self.delivery}))"
+
+ def __str__(self):
+ return f"Task({self.id},Pickup({self.pickup}), Delivery({self.delivery}))"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_token.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_token.py
new file mode 100644
index 0000000000000000000000000000000000000000..f804b8d9dbe0dfc553d22065e9d17d8681b9c32a
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing/tp_token.py
@@ -0,0 +1,63 @@
+class Token:
+ def __init__(self):
+ # Keep track of the paths of all the agents
+ self.paths = dict()
+ # Keep track of which agent is executing which task
+ self.assignments = dict()
+ # Keep track of the remaining tasks
+ self.taskSet = []
+
+ ##---------##
+ ## Getters ##
+ ##---------##
+
+ # Returns the last location on the path of each agent
+ def get_pathends(self, given_id=None):
+ pathEnds = []
+ for a_id, k in self.paths.items():
+ if given_id is None or a_id != given_id:
+ pathEnds.append(k[list(k)[-1]])
+ return pathEnds
+
+ # Get the paths of all agents except the path of agent with ID = own_id
+ def get_other_agents_paths(self, timestep, own_id):
+ other_agent_paths = []
+ for a_id, path in self.paths.items():
+ if a_id != own_id:
+ other_agent_paths.append(path)
+ return other_agent_paths
+
+ ##------------------##
+ ## Add new elements ##
+ ##------------------##
+
+ # Adds a new agent to the system
+ def add_agent(self, agent):
+ a_id = agent.id
+ self.paths[a_id] = dict()
+ self.paths[a_id][0] = agent.pos
+ self.assignments[a_id] = None
+
+ # Adds a new task to the system
+ def add_task(self, task):
+ self.taskSet.append(task)
+
+ ##-------------------##
+ ## Utilitary methods ##
+ ##-------------------##
+
+ # Restores the token with an old state of the token
+ def restore(self, old_token):
+ self.paths = old_token.paths
+ self.assignments = old_token.assignments
+ self.taskSet = old_token.taskSet
+
+ # Generates a copy of this token
+ def copy(self):
+ new_token = Token()
+ new_token.paths = self.paths
+ new_token.assignments = self.assignments
+ new_token.taskSet = self.taskSet
+ return new_token
+
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvChannel.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..89909eccb160d3bebb15087e72541ddbdd4ce2fd
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvChannel.py
@@ -0,0 +1,45 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel
+"""
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.EnvData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.EnvData.matrix = environment_info["matrixLayout"]
+ self.EnvData.rotation = environment_info["rotationRobot"]
+ self.EnvData.action_size = environment_info["sizeOfAction"]
+ self.EnvData.observation_size = environment_info["sizeOfObservation"]
+ self.EnvData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.EnvData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.EnvData.can_go_backwards = environment_info["backwards"]
+ self.EnvData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.EnvData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.EnvData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.EnvData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.EnvData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvironmentData.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea3e19ffeee3dfbe56ad9e5cddeab3af61bcbfaf
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/EnvironmentData.py
@@ -0,0 +1,86 @@
+# stores data on environment
+# initialized when initial data is being sent through EnvChannel
+import json
+
+from Node import Node
+from ta_state import Location
+import tpts_task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.action_size = None
+ self.observation_size = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+ self.task_side_channel = None
+
+ self.tasksCreated = 0
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # set_initial_nodes_dictionary
+ # set_tasks_to_be_assigned
+ # set_assigned_tasks
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Location(tasks[i]["PickUp"]["Column"], tasks[i]["PickUp"]["Row"], 0)
+ delivery = Location(tasks[i]["Delivery"]["Column"], tasks[i]["Delivery"]["Row"], 0)
+ self.tasks_to_be_assigned.append(tpts_task.Task(tpts_task.CURRENT_TASK_ID, pickup, delivery))
+ tpts_task.CURRENT_TASK_ID += 1
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ # info has to be sent to unity via the correct channel
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ # self.tasks_to_be_assigned.remove(task)
+ taskDict = {"Task": {
+ "PickUp": {"Column": str(task.pickup.col), "Row": str(task.pickup.row)},
+ "Delivery": {"Column": str(task.delivery.col), "Row": str(task.delivery.row)}},
+ "InstanceID": str(robot_instanceID)}
+
+ self.task_side_channel.send_string(json.dumps(taskDict))
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Node.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2bb07c8fdf941ab68a8e8c87e8478b6d2e79aba
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Node.py
@@ -0,0 +1,39 @@
+# Class Node represent nodes in the graph. Every node contains a
+# position (x,y) in the grid
+# and a rotation in degree where
+# 0 = North
+# 90 = East
+# 280 = South
+# 270 = West
+# all degrees must be between 0 and 360
+
+# attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Pipfile b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Readme.md b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..d354f1ceb2311e731e56be3a148b0fb0cc535666
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/Readme.md
@@ -0,0 +1,59 @@
+# Token Passing with Task Swaps (TPTS)
+
+The Token-Passing with Task Swaps (TPTS) algorithm is a decentralized algorithm for the online variant of the MAPD problem.
+
+In the online variant of the MAPD problem the following is supported:
+- Using randomly generated tasks (randomtasks = True)
+- Using tasks from a taskslist (bv. ../config/tasks.txt)
+ -> When using a taskslist with releaseTimes: releaseTimes are ignored (only used in offline variant)
+- Tested from 1 agent up untill 50 agents (significant lag for larger amount of agents)
+
+## Running the algorithm
+
+1. Install the virtual environment
+ - pipenv install
+2. Run the "decentralized_decisionmaker_shortest_path.py" file
+ - pipenv run python decentralized_decisionmaker_shortest_path.py
+3. Start the game inside the Unity editor
+
+## Working of the algorithm
+
+This implementation is based on the "Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks" paper (https://arxiv.org/pdf/1705.10868.pdf).
+An explanation of the algorithm is available at:
+ - Section 4.2 of the "Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks" paper
+ - Section 2.2.2 of the paper provided in the "paper" directory
+
+General overview of working:
+ - Token contains:
+ * Paths of all agents
+ * Taskset
+ * Assignments of tasks of all agents
+ - In each timestep each agent moves one step following its path in the token
+ - When an agent reaches the pickup location of its task:
+ * Remove the task from the task set
+ - When an agent reaches the end of its path (= free agent) it looks for a task:
+ - Task found:
+ * Plan path to execute task and update token
+ - No task found:
+ * Plan path to safe location and update token
+
+Differece with Token Passing (TP):
+ - Availability tasks in token
+ * In the TP algorithm tasks are removed from the taskset as soon as an agent assigns that task to itself
+ * In the TPTS algorithm tasks are only removed when an agent reaches the pickup location of the task
+ => This allows agents to select tasks that are already assigned to other agents if they can execute the task more efficiently (= task swapping)
+
+## Overview of the provided files
+
+Main file:
+- decentralized_decisionmaker_shortest_path.py: Main file that runs the algorithm
+
+Token Passing with Task Swaps:
+- tpts_agent.py: Agent that are executing tasks
+- tpts_system: System manages the agents and tasks
+- tpts_task: Pickup and delivery tasks
+- tpts_token: Token that is used for communicating global information between the agents
+
+A-star:
+- ta_astar.py: A-Star algorithm
+- ta_state.py: Contains Location and state classes used for the path planning
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/TaskSideChannel.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..5905a60a0e4d906da492398691ce59c60764791a
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/TaskSideChannel.py
@@ -0,0 +1,38 @@
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+import tpts_task
+from ta_state import Location
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ data = msg.read_string()
+ new_task = json.loads(data)
+ new_task = new_task["task"]
+ task_to_add = tpts_task.Task(tpts_task.CURRENT_TASK_ID,
+ Location(new_task["PickUp"]["Column"], new_task["PickUp"]["Row"], 0),
+ Location(new_task["Delivery"]["Column"], new_task["Delivery"]["Row"], 0))
+ tpts_task.CURRENT_TASK_ID += 1
+ # add task to task to be assigned
+ self.environment_data.add_task_to_tasks_to_be_assigned(task_to_add)
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/decentralized_decisionmaker_shortest_path.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/decentralized_decisionmaker_shortest_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dba440c4b19d672bd25c54c07d8c4579d97a82a
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/decentralized_decisionmaker_shortest_path.py
@@ -0,0 +1,165 @@
+##------------------##
+## Python libraries ##
+##------------------##
+
+import sys
+import numpy as np
+
+##-----------##
+## ML-agents ##
+##-----------##
+
+from mlagents_envs.environment import ActionTuple, UnityEnvironment
+import EnvChannel
+import TaskSideChannel
+from Node import Node
+
+##------##
+## TPTS ##
+##------##
+
+from tpts_system import System as TPTS_System
+from ta_state import Location
+import tpts_task
+
+simulation_going = True
+
+# Load the side channels and Unity environment.
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel]) # Open unity and press play to execute
+env.reset() # reset the environment and get info about environment through envChannel
+
+# Specify the used behaviour name for steering the agent
+behavior_names = list(env.behavior_specs.keys())
+behavior_name = behavior_names[0]
+
+# info obtained from environment
+environment_info = envChannel.EnvData
+# set up links between task side channel and environment info
+environment_info.task_side_channel = taskChannel
+taskChannel.environment_data = environment_info
+
+# Retrieve information from environment
+matrix = environment_info.matrix
+number_of_robots = environment_info.number_of_robots
+rotation = environment_info.rotation
+observation_size = environment_info.observation_size
+action_size = environment_info.action_size
+robot_can_go_backwards = environment_info.can_go_backwards
+
+# Initialize system
+system = TPTS_System(matrix, rotation)
+
+# Callback called when an agent assigns itself a task
+def on_task_assigned(agent_id, task_id):
+ environment_info.assign_task(agent_id, system.get_task(task_id))
+
+##----------------##
+## Robot Creation ##
+##----------------##
+
+# Number of agents in the configuration file
+n_agents = len(environment_info.instance_ids)
+
+# Create all the agents
+for robot_id, start_node in environment_info.current_nodes_dictionary.items():
+ # Convert the Node that represents the agent's location to a Location
+ robot_position = Location(start_node.y, start_node.x, start_node.g)
+ # Create agent
+ system.add_agent(robot_id, robot_position, on_task_assigned)
+
+# Each agent requests 1 action in each timestep
+# next timestep starts when all agents have requested an action in this timestep
+actions_requested_in_timestep = 0
+# Keep track of current timestep
+timestep = 0
+
+while simulation_going:
+ # If there are new tasks that need to be assigned add them to the task set
+ if len(environment_info.tasks_to_be_assigned) != 0:
+ for task in environment_info.tasks_to_be_assigned:
+ # Add task to task set
+ system.add_task(task)
+ # Task is already in task set, remove it from this list to avoid duplicates in task set
+ environment_info.tasks_to_be_assigned.remove(task)
+
+ # Get information on the agents that request an action
+ decision_steps, terminal_steps = env.get_steps(behavior_name)
+ agents = decision_steps.agent_id
+ observations = decision_steps.obs
+
+ # Actions that will be sent back to Unity
+ actions = []
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # Create an action for all agents that request an action
+ for i in range(len(agents)):
+
+ ##--------------##
+ ## OBSERVATIONS ##
+ ##--------------##
+
+ # Keep track of how many agents have requested an action in this timestep
+ actions_requested_in_timestep += 1
+
+ # Collect agent's observations
+ observation = observations[0][i]
+ # Extract observations
+ robot_id_unity = observation[0] # robot id in unity used to get correct precomputed path
+ robot_pos_x = observation[2]
+ robot_pos_y = observation[1]
+ robot_rot = observation[3]
+ # Convert coordinates to position
+ robot_position = Location(robot_pos_x, robot_pos_y, robot_rot)
+
+ # Get Python Agent that represents robot
+ robot = system.get_agent(int(robot_id_unity))
+
+ ##-------------##
+ ## FOLLOW PATH ##
+ ##-------------##
+
+ # Follow previously computed path or plan new path when at the end of path
+ next_position = robot.move(system.token, timestep)
+ # Convert Position to Node
+ next_action = Node(next_position.row, next_position.col, next_position.rotation)
+ # Check if a task needs to be picked up / delivered
+ pickup_action = 0
+ delivery_action = 0
+ if next_position.to_pickup:
+ # Agent has to pick up his task
+ pickup_action = 1
+ # Agent has picked up the task, no agent can swap from now on, remove it from the taskSet
+ to_remove = system.get_task(next_position.task_id)
+ system.token.taskSet.remove(to_remove)
+ elif next_position.to_deliver:
+ # Agent has to deliver his task
+ delivery_action = 1
+ # Agent has no task assigned anymore as the task is delivered
+ system.token.assignments[robot_id_unity] = None
+
+ # Send action to agent in Unity
+ actions.extend([next_action.x, next_action.y, next_action.g, pickup_action, delivery_action])
+
+
+ # When all agents have moved in this timestep update the timestep
+ if (actions_requested_in_timestep == n_agents):
+ # Update the timestep
+ timestep += 1
+ # Reset the number of agents that have requested a timestep in this timestep
+ actions_requested_in_timestep = 0
+
+ # Send actions to Unity
+ actions = np.array(actions)
+ actions.resize(len(agents), action_size)
+ action_tuple.add_discrete(actions)
+
+ env.set_actions(behavior_name, action_tuple)
+ env.step()
+
+
+
+
+
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/paper/Bachelorthesis.pdf b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/paper/Bachelorthesis.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..e9bf4fce9fccb3903a7f502782e4292f8c59e23d
Binary files /dev/null and b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/paper/Bachelorthesis.pdf differ
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_astar.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_astar.py
new file mode 100644
index 0000000000000000000000000000000000000000..c936ffdaffbb6872143e2fd42caaa7ab35656dc1
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_astar.py
@@ -0,0 +1,163 @@
+"""
+Created on Wed Apr 22 07:43:03 2020
+
+@author: Pieter Cawood
+
+"""
+from heapq import *
+from ta_state import *
+
+def get_heuristic(start_location, goal_location, rotation):
+ # Admissible heuristic - manhattan distance
+ return abs(start_location.col - goal_location.col) + abs(start_location.row - goal_location.row)
+
+class AStar:
+ def __init__(self, world, rotation):
+ self.rotation = rotation
+ self.world = world
+ self.dim_x = len(world[0])
+ self.dim_y = len(world)
+ self.obstacles = {}
+ self.path_ends = dict()
+ self.other_agent_paths = []
+
+ def state_possible(self, current_state, new_state):
+ # Check if wall
+ not_obstacle = False
+ if new_state.location not in self.obstacles:
+ not_obstacle = True
+ return 0 <= new_state.location.col < self.dim_x and 0 <= new_state.location.row < self.dim_y \
+ and not_obstacle and not self.is_collision(current_state, new_state)
+
+ def is_collision(self, current_state, new_state):
+ for other_agent_path in self.other_agent_paths:
+ # Vertex collision
+ if new_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == new_state.location.row and other_agent_path[new_state.time].col == new_state.location.col:
+ return True
+ # Edge collision
+ if new_state.time in other_agent_path and current_state.time in other_agent_path:
+ if other_agent_path[new_state.time].row == current_state.location.row and \
+ other_agent_path[new_state.time].col == current_state.location.col and \
+ other_agent_path[current_state.time].row == new_state.location.row and \
+ other_agent_path[current_state.time].col == new_state.location.col:
+ return True
+ # Last position of agent is his parking location
+ if new_state.location in self.path_ends:
+ busy_time = self.path_ends[new_state.location]
+ if new_state.time >= busy_time:
+ return True
+ return False
+
+ def get_neighbours(self, current_state):
+ neighbours = []
+ neighbour_time_step = current_state.time + 1
+ # Wait in state
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ # Add rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation+self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ # Remove rotation
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row, (current_state.location.rotation-self.rotation) % 360))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ if current_state.location.rotation == 0:
+ # Move down
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row - 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 90:
+ # Move right
+ new_state = State(neighbour_time_step, Location(current_state.location.col + 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 180:
+ # Move up
+ new_state = State(neighbour_time_step, Location(current_state.location.col, current_state.location.row + 1, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ elif current_state.location.rotation == 270:
+ # Move left
+ new_state = State(neighbour_time_step, Location(current_state.location.col - 1, current_state.location.row, current_state.location.rotation))
+ if self.state_possible(current_state, new_state):
+ neighbours.append(new_state)
+ return neighbours
+
+ def create_obstacles(self, other_agent_paths):
+ n_rotations = int(360/self.rotation)
+ # Keep track of all walls in the environment
+ for y in range(self.dim_y):
+ for x in range(self.dim_x):
+ # 1 represent a wall in the environment
+ if self.world[y][x] == 1:
+ # A wall is always a collision, no matter the rotation
+ for i in range(n_rotations):
+ rot = i * self.rotation
+ self.obstacles[Location(x, y, rot)] = True
+ # Keep track of where the paths of the other agents end
+ self.path_ends = dict()
+ for path in other_agent_paths:
+ # Get the last timestep in the path of the agent
+ last_time = list(path)[-1]
+ # Get the last location in the path of the agent
+ last_pos = path[last_time]
+ # Two agents always collide when they share the same location, no matter the rotation
+ for i in range(n_rotations):
+ rot = i * self.rotation
+ self.path_ends[Location(last_pos.col, last_pos.row, rot)] = last_time
+ # Keep track of the other paths of agents to detect collisions
+ self.other_agent_paths = other_agent_paths
+
+ # Returns the latest timestep at which a specific location will be used
+ def find_lastest_use(self, location):
+ # Keep track of all the uses of that location
+ uses = []
+ # Check in the path of each agent if that agent uses that location, if yes, add it to the list
+ for agent_path in self.other_agent_paths:
+ for t, loc in agent_path.items():
+ if loc.col == location.col and loc.row == location.row:
+ uses.append(t)
+ # If the location is used at least once, return the maximum
+ if uses:
+ return max(uses)
+ # If the location is never used, return 0
+ else:
+ return 0
+
+
+ def search(self, time_step, start_location, goal_location, other_agent_paths,
+ release_time=0, find_step = 0):
+ self.create_obstacles(other_agent_paths)
+ initial_state = State(time_step, start_location)
+ # Find the lastest use of the goal location to avoid parking on a location that is still going to be used
+ lastest_use = self.find_lastest_use(goal_location)
+ came_from = {}
+ open_list = [initial_state]
+ closed_set = set()
+ initial_state.g_score = 0
+ initial_state.f_score = get_heuristic(start_location, goal_location, self.rotation)
+ while open_list:
+ current = heappop(open_list)
+ if current.location == goal_location:
+ if current in came_from and came_from[current].location == goal_location:
+ if current.time > lastest_use:
+ new_path = [current]
+ while current in came_from:
+ current = came_from[current]
+ new_path.append(current)
+ # Reverse path
+ return new_path[::-1]
+ closed_set |= {current}
+ neighbours = self.get_neighbours(current)
+ neighbour: State
+ for neighbour in neighbours:
+ # Only explore new states
+ if (neighbour not in closed_set) and (neighbour not in open_list):
+ came_from[neighbour] = current
+ neighbour.g = current.g + 1
+ neighbour.f = neighbour.g + get_heuristic(neighbour.location, goal_location, self.rotation)
+ heappush(open_list, neighbour)
+ return False
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_state.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6269547d945284d4fcff1503617d8a2e44b8ff4
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/ta_state.py
@@ -0,0 +1,60 @@
+"""
+Created on Sat Apr 18 10:45:11 2020
+
+@author: Pieter Cawood
+
+"""
+
+def h(pos1, pos2):
+ return abs(pos1.col - pos2.col) + abs(pos1.row - pos2.row)
+
+class Location(object):
+ def __init__(self, col, row, rotation):
+ self.col = col
+ self.row = row
+ self.rotation = rotation
+
+ # Relevant information for pickup and delivery actions
+ self.task_id = None
+ self.to_pickup = False
+ self.to_deliver = False
+
+ def __eq__(self, other):
+ return self.col == other.col and self.row == other.row and self.rotation == other.rotation
+
+ def __hash__(self):
+ return hash(str(self.col) + "_" + str(self.row) + "_" + str(self.rotation))
+
+ def __str__(self):
+ return str((self.col, self.row, self.rotation, self.to_pickup, self.to_deliver))
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"Location({self.col}, {self.row}, {self.rotation}, {self.to_pickup}, {self.to_deliver})"
+
+
+class State(object):
+ def __init__(self, time, location):
+ self.time = time
+ self.location = location
+ self.g = 0
+ self.f = 0
+
+ def __eq__(self, other):
+ return self.time == other.time and self.location == other.location
+
+ def __hash__(self):
+ return hash(str(self.time) + str(self.location.col) + str(self.location.row))
+
+ def __str__(self):
+ return str((self.time, self.location.col, self.location.row))
+
+ def __lt__(self, other):
+ if self.f < other.f:
+ return True
+ else:
+ return False
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"State(Time: {self.time}, Location(x: {self.location.col}, y: {self.location.row}, r: {self.location.rotation}))"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_agent.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..c274323e921b25304f7b3c82e81deef100d88206
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_agent.py
@@ -0,0 +1,248 @@
+# Python modules
+import sys
+import numpy as np
+# Pathfinding modules
+from ta_astar import AStar
+from ta_state import Location, h
+# Own modules
+
+class Agent:
+ def __init__(self, id, initial_position, system, environment, rotation, on_task_assigned):
+ # Identifier used to identify this agent
+ self.id = id
+ # Position of the agent
+ self.pos = initial_position
+ # Keep track of the parking location of the agent
+ self.parkingLocation = initial_position
+ # AStar planner used to compute path
+ self.a_star = AStar(environment, rotation)
+ # System is used to request token to look for tasks
+ self.system = system
+ # Indicates whether agent is currently carying a package
+ self.carrying = False
+ # Callback called when agent assigns a task to itself
+ self.on_task_assigned = on_task_assigned
+
+ # Delete path of an agent from the token
+ def delete_path(self, token, agent, start_timestep):
+ del token.paths[agent.id]
+
+ # Method that the agent calls to look for a new task and plan a path
+ def look_for_task(self, token, timestep):
+ # When look_for_task is called by another agent that tries to swap tasks agent might not be on an endpoint
+ if not self.system.is_endpoint(self.pos):
+ # Agent is not on an endpoint so planning a path to an endpoint might fail
+ found = self.path2(token, timestep)
+ if found:
+ # Agent can safely move to an endpoint -> Allow swapping
+ return True
+ else:
+ # Agent can not safely move to an endpoint -> Do not allow swapping
+ return False
+
+ availableTasks = [] # List of all the tasks that are available to the agent
+
+ ##-- Check if pickup/delivery is not end of path of any other agent --##
+ # Get last locations of all paths in token
+ pathEndsWithID = token.get_pathends(self.id)
+ # Only keep the locations, ID's are not needed here
+ pathEnds = list(map(lambda x: x[0], pathEndsWithID))
+ # If pickup/delivery is not in pathEnds it's an available task
+ for t in token.taskSet:
+ # Task is available if it's delivery location is not the end of the path of any agent
+ if t.pickup not in pathEnds:
+ availableTasks.append(t)
+
+ # While there is still at least one task
+ while len(availableTasks) > 0:
+ # Find the "best" task (best = minimum h-value)
+ h_scores = np.array(map(lambda t: h(self.pos, t.pickup), availableTasks))
+ min_task = availableTasks[np.argmin(h_scores)]
+ # Remove task from available tasks
+ availableTasks.remove(min_task)
+
+ # Check if task has already been assigned
+ other_id = None
+ for a_id, t_id in token.assignments.items():
+ # If another agent has already assigned this task to itself, keep track of the agent
+ if t_id == min_task.id:
+ other_id = a_id
+ if other_id is None:
+ # No other agent has already reserved this task
+
+ # Check if the path of any agent ends in the delivery location of the task: means that it can rest there forever, look for another task
+ if min_task.delivery in pathEnds:
+ continue
+ # No agent is assigned to task
+ # Assign the task to this agent
+ token.assignments[self.id] = min_task.id
+ # Plan a path to execute the task
+ self.path1(min_task, token, timestep)
+ # Call the callback to notify Unity that the agent has a new task assignment
+ self.on_task_assigned(self.id, min_task.id)
+ # Agent was able to find a task and plan a path, return True
+ return True
+ else:
+ # Task is already assigned to another agent: check if this agent can reach the pickup location more efficiently
+ other_agent = self.system.get_agent(other_id)
+ # Check if the path of any other agent is the delivery location of the task: means that it can rest there forever, look for another task
+ if min_task.delivery in pathEnds and (min_task.delivery, other_id) not in pathEndsWithID:
+ continue
+ # Check if other agent is already at the pickup location of the task: if yes, look for another task
+ if other_agent.pos == min_task.pickup:
+ continue
+ # Make a copy of the token to restore it in case swapping is not allowed
+ token_copy = token.copy()
+ # Unassign the task from the other agent
+ token.assignments[other_id] = None
+ # Assign this task to this agent
+ token.assignments[self.id] = min_task.id
+ # Delete the path of the other agent from the token
+ self.delete_path(token, other_agent, timestep)
+ # Plan a path to execute this task
+ self.path1(min_task, token, timestep)
+ # Calculate how many steps this agent needs to reach the pickup location
+ selfReached = timestep
+ while(token.paths[self.id][selfReached] != min_task.pickup):
+ selfReached += 1
+ # Calculate how many steps the other agent needs to reach the pickup location
+ otherReached = timestep
+ while(token_copy.paths[other_id][otherReached] != min_task.pickup):
+ otherReached += 1
+ # Check which agent reaches the pickup location of the task more efficiently
+ if (selfReached < otherReached):
+ # This agent can reach the pickup location more efficiently, check if other agent can find another task or find a path to a safe location
+ success = other_agent.look_for_task(token, timestep)
+ if success:
+ # Other agent was able to plan a path to another task or to a safe location -> Notify Unity that this agent has a new task assigned
+ self.on_task_assigned(self.id, min_task.id)
+ # Agent was able to find a task and plan a path, return True
+ return True
+ # Task Swapping is not allowed or this agent can not reach the pickup location more efficiently, restore the token
+ token.restore(token_copy)
+ # Agent was not able to find a task, plan a path to a safe location
+
+ # Keep track of all the delivery locations of the tasks in the taskSet
+ delivery_locations = []
+ for task in token.taskSet:
+ delivery_locations.append(task.delivery)
+ # If the current position of the agent is not a delivery location the agent can rest at it's current position
+ if Location(self.pos.col, self.pos.row, 0) not in delivery_locations:
+ # Check if agent already has a path in the token (might have been deleted by other agent): create a path if there is none
+ if self.id not in token.paths:
+ token.paths[self.id] = dict()
+ # Register the trivial path to the agent's current location in its path: will try to look for a path in the next timestep
+ token.paths[self.id][timestep] = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ else:
+ # Agent has to move as it's current location is a delivery location, plan a collision-free path to a safe location
+ self.path2(token, timestep)
+ # Agent was not able to find a task, but was able to plan a collision-free path to a safe location
+ return True
+
+
+ def path1(self, task, token, timestep):
+ # Plans a path from the current location of the agent to the pickup location and a path from pickup location to delivery location
+
+ # All needed locations for the pathplanning
+ current_location = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ pickup_location = Location(task.pickup.col, task.pickup.row, task.pickup.rotation)
+ delivery_location = Location(task.delivery.col, task.delivery.row, task.delivery.rotation)
+
+ ##----------------##
+ ## Path to pickup ##
+ ##----------------##
+
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the current location of the agent to the pickup location of the task
+ pickup_path = self.a_star.search(timestep, current_location, pickup_location, other_agent_paths)
+ # There should always be a path if the problem is well formed
+ if not pickup_path:
+ raise ValueError("[TPTS] No path to pickup location - Problem not well formed")
+
+ # Add newly planned path to the agent's path in the token
+ for state in pickup_path:
+ # Check if agent already has a path in the token (might have been deleted by other agent): create a path if there is none
+ if self.id not in token.paths:
+ token.paths[self.id] = dict()
+ token.paths[self.id][state.time] = state.location
+ # Update the timestep to the timestep at the end of the planned path
+ timestep = timestep + len(pickup_path) - 1
+
+ # Keep track of the timestep when the agent reaches the pickup location
+ timestep_at_pickup = timestep
+
+ ##------------------##
+ ## Path to delivery ##
+ ##------------------##
+
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the pickup location of the task to the delivery location of the task
+ delivery_path = self.a_star.search(timestep, pickup_location, delivery_location, other_agent_paths)
+ # There should always be a path if the problem is well formed
+ if not delivery_path:
+ raise ValueError("[TP] No path to delivery location - Problem not well formed")
+ # Add newly planned path to the agent's path in the token
+ for state in delivery_path:
+ token.paths[self.id][state.time] = state.location
+
+ # Keep track of the timestep at which the agent reaches the delivery location
+ timestep = timestep + len(delivery_path) - 1
+
+ ##---------------------------##
+ ## Pickup / Delivery actions ##
+ ##---------------------------##
+
+ # Register in the last location of the path to the pickup location that the agent needs to pickup a task and register the task ID
+ token.paths[self.id][timestep_at_pickup].to_pickup = True
+ token.paths[self.id][timestep_at_pickup].task_id = task.id
+
+ # Register in the last location of the path to the delivery location that the agent needs to pickup a task and register the task ID
+ token.paths[self.id][timestep].to_deliver = True
+ token.paths[self.id][timestep].task_id = task.id
+
+ """ Path2 plans a path from the agent's current location
+ to the closest free endpoint
+ """
+ def path2(self, token, timestep):
+ # Agent is not at an endpoint, path planning might fail
+ ##-- Find free endpoints to move to --##
+ endpoints = self.system.get_free_endpoints()
+ # Select the best endpoint (best = smallest h-score)
+ h_scores = list(map(lambda e: h(self.pos, e), endpoints))
+ chosen_endpoint = endpoints[np.argmin(h_scores)]
+
+ ##-- Plan path to free endpoint --##
+ # Make sure planned path doesn't collide with already planned paths
+ current_location = Location(self.pos.col, self.pos.row, self.pos.rotation)
+ endpoint_location = Location(chosen_endpoint.col, chosen_endpoint.row, chosen_endpoint.rotation)
+ # Get the paths of all the other agents
+ other_agent_paths = token.get_other_agents_paths(timestep, self.id)
+ # Plan a path from the agent's current location to the chosen endpoint
+ path_to_endpoint = self.a_star.search(timestep, current_location, endpoint_location, other_agent_paths)
+ # If agent is not currently at an endpoint, there might be no possible path, return False
+ if not path_to_endpoint:
+ return False
+ # Add newly planned path to the agent's path in the token
+ for state in path_to_endpoint:
+ # Check if agent already has a path in the token (might have been deleted by other agent): create a path if there is none
+ if self.id not in token.paths:
+ token.paths[self.id] = dict()
+ token.paths[self.id][state.time] = state.location
+ return True
+
+ def move(self, token, timestep):
+ # Get the current path of the agent
+ path = token.paths[self.id]
+ # Check if the agent has already planned a move for this timestep
+ if timestep not in path:
+ # If agent has not yet planned a move for this timestep it requests the token and looks for a task
+ token = self.system.request_token()
+ self.look_for_task(token, timestep)
+ # Select the move of the agent in this timestep
+ next_move = token.paths[self.id][timestep]
+ # Update the agent's position
+ self.pos = next_move
+ # Return the agent's next position
+ return next_move
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_system.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5c22f40302601435063882364577e7bd16da96c
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_system.py
@@ -0,0 +1,104 @@
+# Python modules
+import sys
+import numpy as np
+# Pathfinding modules
+from ta_state import Location
+# Own modules
+from tpts_token import Token
+from tpts_agent import Agent
+
+class System:
+ def __init__(self, environment, rotation):
+ # Keep track of the warehouse setup
+ self.environment = environment
+ # Keep track of rotation
+ self.rotation = rotation
+
+ # Keep track of all agents in the system
+ self.agents = dict()
+ # Keep track of all tasks
+ self.tasks = dict()
+ # Token setup
+ self.token = Token()
+
+ # Keep track of all the non-task/task endpoints
+ self.task_endpoints = []
+ self.non_task_endpoints = []
+ # Get all the endpoints from the environment
+ self.init_endpoints()
+
+ # Find all the task and non-task endpoints in the environment
+ def init_endpoints(self):
+ # Get the dimensions of the environment
+ dim_y = len(self.environment)
+ dim_x = len(self.environment[0])
+ for y in range(dim_y):
+ for x in range(dim_x):
+ # Non-task endpoints (parking locations) are represented by 2
+ if self.environment[y][x] == 2:
+ self.non_task_endpoints.append(Location(x, y, 0))
+ # Task endpoints (pickup/delivery locations) are represented by 3
+ elif self.environment[y][x] == 3:
+ self.task_endpoints.append(Location(x, y, 0))
+
+ ##---------##
+ ## Getters ##
+ ##---------##
+
+ def get_agent(self, agent_id):
+ return self.agents[agent_id]
+
+ def get_task(self, task_id):
+ return self.tasks[task_id]
+
+ def get_endpoints(self):
+ return np.concatenate((self.non_task_endpoints, self.task_endpoints))
+
+ def get_free_endpoints(self):
+ # List of all the endpoints
+ all_endpoints = self.get_endpoints()
+ free_endpoints = []
+ # Get a list of the last positions of each agent's path
+ endPathsWithID = self.token.get_pathends()
+ endPaths = list(map(lambda x: x[0], endPathsWithID))
+ # Generate a list of all the delivery locations of the tasks in the taskSet
+ deliveryLocations = []
+ for t in self.token.taskSet:
+ deliveryLocations.append(t.delivery)
+ for e in all_endpoints:
+ # If endpoint is not the end of the path of any agent
+ # or is not the delivery location of any task it's free
+ if e not in endPaths and e not in deliveryLocations:
+ free_endpoints.append(e)
+ return free_endpoints
+
+ def get_non_task_endpoints(self):
+ return self.non_task_endpoints
+
+ ##---------------------##
+ ## Adding new elements ##
+ ##---------------------##
+
+ def add_agent(self, agent_id, agent_pos, on_task_assigned):
+ agent = Agent(agent_id, agent_pos, self, self.environment,
+ self.rotation, on_task_assigned)
+ self.agents[agent.id] = agent
+ self.token.add_agent(agent)
+
+ def add_task(self, task):
+ self.token.add_task(task)
+ self.tasks[task.id] = task
+
+ ##-------------------##
+ ## Utilitary methods ##
+ ##-------------------##
+
+ def request_token(self):
+ return self.token
+
+ def is_endpoint(self, endpoint):
+ endpoint_loc = Location(endpoint.col, endpoint.row, 0)
+ if endpoint_loc in self.non_task_endpoints or endpoint_loc in self.task_endpoints:
+ return True
+ else:
+ return False
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_task.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..03e2b01468fb260764e4733a86d8571643c48b16
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_task.py
@@ -0,0 +1,35 @@
+CURRENT_TASK_ID = 0
+
+class Task:
+ """ Represents a task in the pickup & delivery problem
+
+ Attributes:
+ id: int
+ Identifier of the task
+ pickupLocation: Position
+ The location where the task needs to be picked up
+ deliveryLocation: Position
+ The location where the task needs to be delivered
+ """
+ def __init__(self, id, pickupLocation, deliveryLocation):
+ self.id = id
+
+ self.pickup = pickupLocation
+ self.delivery = deliveryLocation
+
+ # Keep track timestep when task enters the taskset and when it's executed => calculating service time
+ self.entered = None
+ self.delivered = None
+
+ def service_time(self):
+ if self.entered is not None and self.delivered is not None:
+ return self.delivered - self.entered
+ else:
+ return None
+
+ # Better information for debugging
+ def __repr__(self):
+ return f"Task({self.id},Pickup({self.pickup}), Delivery({self.delivery}))"
+
+ def __str__(self):
+ return f"Task({self.id},Pickup({self.pickup}), Delivery({self.delivery}))"
diff --git a/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_token.py b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_token.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e42d8ffd46c51fdebcea3639f91cab0c9e5e311
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_Token_Passing_with_Task_Swapping/tpts_token.py
@@ -0,0 +1,75 @@
+from ta_state import Location
+
+class Token:
+ def __init__(self):
+ # Keep track of the paths of all the agents
+ self.paths = dict()
+ # Keep track of which agent is executing which task
+ self.assignments = dict()
+ # Keep track of the remaining tasks
+ self.taskSet = []
+
+ ##---------##
+ ## Getters ##
+ ##---------##
+
+ # Returns the last location on the path of each agent
+ def get_pathends(self, given_id=None):
+ pathEnds = []
+ for a_id, k in self.paths.items():
+ if given_id is None or a_id != given_id:
+ loc = k[list(k)[-1]]
+ pathEnds.append((Location(loc.col, loc.row, 0), a_id))
+ return pathEnds
+
+ # Get the paths of all agents except the path of agent with ID = own_id
+ def get_other_agents_paths(self, timestep, own_id):
+ other_agent_paths = []
+ for a_id, path in self.paths.items():
+ if a_id != own_id:
+ other_agent_paths.append(path)
+ return other_agent_paths
+
+ ##------------------##
+ ## Add new elements ##
+ ##------------------##
+
+ # Adds a new agent to the system
+ def add_agent(self, agent):
+ a_id = agent.id
+ self.paths[a_id] = dict()
+ self.paths[a_id][0] = agent.pos
+ self.assignments[a_id] = None
+
+ # Adds a new task to the system
+ def add_task(self, task):
+ self.taskSet.append(task)
+
+ ##-------------------##
+ ## Utilitary methods ##
+ ##-------------------##
+
+ # Restores the token with an old state of the token
+ def restore(self, old_token):
+ self.paths = old_token.paths
+ self.assignments = old_token.assignments
+ self.taskSet = old_token.taskSet
+
+ # Generates a copy of this token
+ def copy(self):
+ new_token = Token()
+ # Copy paths
+ new_token.paths = dict()
+ for a_id, path in self.paths.items():
+ new_token.paths[a_id] = dict()
+ for t, loc in path.items():
+ new_token.paths[a_id][t] = loc
+ # Copy assignments
+ new_token.assignments = dict()
+ for a_id, t_id in self.assignments.items():
+ new_token.assignments[a_id] = t_id
+ # Copy tasks
+ new_token.taskSet = []
+ for task in self.taskSet:
+ new_token.taskSet.append(task)
+ return new_token
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvChannel.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6d62add37419b9cfe1ddb9b07a70b150a07a6d7
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvChannel.py
@@ -0,0 +1,63 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from EnvironmentData import EnvironmentData
+
+"""
+class that processes information from SideChannel EnvChannel: stores all incoming information
+in an instance of class EnvironmentData
+"""
+
+
+class EnvChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("621f0a70-4f87-11ea-a6bf-784f4387d1f7")) #must coincide with ChannelId set in c#
+ self.envData = EnvironmentData() # object that stores info on environment
+
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ # We simply read a string from the message and print it.
+ data = msg.read_string()
+ environment_info = json.loads(data)
+
+ self.envData.matrix = environment_info["matrixLayout"]
+ self.envData.rotation = environment_info["rotationRobot"]
+ self.envData.size_of_action = environment_info["sizeOfAction"]
+ self.envData.size_of_observation = environment_info["sizeOfObservation"]
+ self.envData.number_of_robots = environment_info["numberOfRobotsInEnv"]
+ self.envData.number_of_current_tasks = environment_info["numberOfCurrentTasks"]
+ self.envData.can_go_backwards = environment_info["backwards"]
+ self.envData.random_tasks = environment_info["tasksGeneratedRandomly"]
+ self.envData.instance_ids = environment_info["instanceIDSRobots"]
+
+ self.envData.set_initial_nodes_dictionary(environment_info["initialNodesOfRobots"])
+ self.envData.set_tasks_to_be_assigned(environment_info["currentTasks"])
+ self.envData.set_assigned_tasks()
+
+ def send_string(self, data: str) -> None:
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentData.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentData.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddc7bc4ddf6d4e8de2c6a5143287a3809f728196
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentData.py
@@ -0,0 +1,101 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+stores data on environment
+instance methods that initialize environment (used by EnvChannel)
+- set_initial_nodes_dictionary(initial_nodes): initializes current_nodes_dictionary
+containing current(next) nodes for each of the robots
+- set_tasks_to_be_assigned(tasks): initialize tasks_to_be_assigned: tasks that have to be assigned
+- set_assigned_tasks(): initialize assigned_tasks_dictionary containing
+ for every robot the current assigned task
+
+instance methods that update environment info
+- assign_task(robot_instanceID, task): assign task to a given robot
+- add_task_to_tasks_to_be_assigned(task): append a new task to tasks_to_be_assigned
+- task_done(instance_id): remove task from robot that was assigned this task
+"""
+
+
+import json
+
+from Node import Node
+from Task import Position, Task
+
+
+class EnvironmentData:
+
+ def __init__(self):
+ self.matrix = None
+ self.rotation = None
+ self.size_of_action = None
+ self.size_of_observation = None
+ self.number_of_robots = None
+ self.number_of_current_tasks = None
+ self.can_go_backwards = None
+ self.random_tasks = None # if set to true random tasks will be send whenever a task has been completed,
+ # otherwise all tasks self.tasks_to_be_assigned
+
+ self.instance_ids = []
+ self.current_nodes_dictionary = {} # dictionary instance_id -> current node
+ self.tasks_to_be_assigned = [] # tasks that are still to be assigned
+ self.assigned_tasks_dictionary = {} # dictionary instance_id -> assigned task
+
+
+ #########################################
+ ## methods used by EnvChannel to initialize attributes:
+ #########################################
+
+ # given a correct string containing array of nodes
+ def set_initial_nodes_dictionary(self, initial_nodes):
+ for i in range(self.number_of_robots):
+ self.current_nodes_dictionary[self.instance_ids[i]] = Node(initial_nodes[i]["GridPosition"]["Row"],
+ initial_nodes[i]["GridPosition"]["Column"],
+ initial_nodes[i]["Degrees"])
+
+ # given a correct json_string containing array of tasks
+ def set_tasks_to_be_assigned(self, tasks):
+ for i in range(self.number_of_current_tasks):
+ pickup = Position(tasks[i]["PickUp"]["Row"], tasks[i]["PickUp"]["Column"])
+ delivery = Position(tasks[i]["Delivery"]["Row"], tasks[i]["Delivery"]["Column"])
+ self.tasks_to_be_assigned.append(Task(pickup, delivery))
+
+ # initialize dictionary robot instance_id -> task (None)
+ def set_assigned_tasks(self):
+ for i in range(self.number_of_robots):
+ self.assigned_tasks_dictionary[self.instance_ids[i]] = None
+
+ #########################################
+ ## methods used to update environment info
+ #########################################
+
+ # when a task is assigned to a robot, it has to be added to self.assigned_tasks
+ # and removed from self.tasks_to_be_assigned
+ # info has to be sent to unity via the correct channel
+ def assign_task(self, robot_instanceID, task):
+ self.assigned_tasks_dictionary[robot_instanceID] = task
+ self.tasks_to_be_assigned.remove(task)
+
+ # when a new task is available it is added to tasks_to_be_assigned
+ # a check is made whether there are still tasks that can be added, but this should not be happening (diagnostical)
+ def add_task_to_tasks_to_be_assigned(self, task):
+ if len(self.tasks_to_be_assigned) == self.number_of_current_tasks:
+ raise Exception("cannot add more tasks")
+ self.tasks_to_be_assigned.append(task)
+
+ # when a robot has finished his task it has to be removed from assigned_tasks
+ def task_done(self, instance_id):
+ self.assigned_tasks_dictionary[instance_id] = None
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentManager.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentManager.py
new file mode 100644
index 0000000000000000000000000000000000000000..d839fb86c4c64858daad064023e5293d8de187fb
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/EnvironmentManager.py
@@ -0,0 +1,195 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""'
+Class that contains and manages the information on the environment.
+- Initializes and holds information on the environment (EnvData).
+- Contains a reference to the sidechannel (EnvChannel).
+- Contains a reference to the sidechannel (TaskChannel).
+- Contains a reference to the unity environment (UnityEnv).
+Has instance methods
+- reset() which resets the environment.
+- step() which assigns tasks, computes a new action based on observation from the unity environment
+ and sends actions back to unity, updates info environment when a task has finished
+- close() which closes the environment/
+
+Has also some auxiliary instance methods:
+- assign_new_task() which finds a new task and a robot that is free, then calls
+assign_task_to_robot(robot_instanceID, task)
+- compute_path(current_node, task) which computes paths starting in current_node completing a certain task
+- initialize_shortest_paths() which initializes a dictionary containing the paths robots are following
+- assign_task_to_robot(robot_instanceID, task) which assigns task to a robot with given instance ID,
+updates EnvData and sends information on assignment back to Unity
+
+Note that this a toy example. No checks are done on collisions.
+"""
+
+import json
+
+from mlagents_envs.base_env import ActionTuple
+
+from Graph import Graph
+from Node import Node
+
+import numpy as np
+
+
+class EnvironmentManager:
+
+ def __init__(self, envData, unityEnv, envChannel, taskChannel):
+ self.envData = envData
+ self.unityEnv = unityEnv
+ self.envChannel = envChannel
+ self.taskChannel = taskChannel
+
+ # IMPORTANT: sends info through envChannel, to be called before setting up connection envData an taskChannel
+ self.reset()
+
+ # taskChannel needs info on envData
+ self.taskChannel.environment_data = envData
+
+ # behavior name for agent
+ self.behavior_name = list(unityEnv.behavior_specs.keys())[0]
+
+ # graph that is used for computing paths
+ self.graph = Graph(envData.matrix, envData.rotation, envData.can_go_backwards)
+
+ # for every robot with instanceID id shortest_paths[id] gives the shortest path to
+ # its currently assigned task
+ self.shortest_paths = {}
+
+ self.initialize_shortest_paths()
+
+ # step in the environment
+ # 1) checks if new tasks can be assigned
+ # 2) collect observations
+ # 3) compute new actions
+ # 4) send actions back to Unity
+ def step(self):
+ self.assign_new_task()
+
+ # get information from the agent that requests an action
+ decision_steps, terminal_steps = self.unityEnv.get_steps(self.behavior_name)
+
+ print("request is being made by")
+ agents = decision_steps.agent_id
+ print(str(len(agents)) + " agents")
+
+ # decision_steps.obs is a list of numpy arrays, first dimension corresponds to the number of agents that has requested
+ # a decision (here len(agents))
+ observations = decision_steps.obs
+
+ print("----------------------------------------")
+
+
+ actions = [] # long list of all actions
+ action_tuple = ActionTuple() # actions to send to unity
+
+ # create an action for all agents that request an action
+ for i in range(len(agents)):
+ observation = observations[0][i] # observation of current agent
+ robot_id_unity = observation[0]
+ row = observation[1]
+ column = observation[2]
+ degrees = observation[3]
+
+ print("robot " + str(robot_id_unity) + " at node [(" + str(row) + "," + str(column) + ")," + str(
+ degrees) + "]")
+
+ path = self.shortest_paths[robot_id_unity]
+
+ # if path is empty, then robot should stay at same node
+ if len(path) == 0:
+ actions.extend([row, column, degrees, 0, 0])
+ # if path is not empty we get next action from shortest path
+ else:
+ next_action = path[0] # is a node or "pickup" or "deliver"
+ if next_action == "pickup":
+ actions.extend([row, column, degrees, 1, 0])
+ elif next_action == "deliver":
+ actions.extend([row, column, degrees, 0, 1])
+ self.envData.assigned_tasks_dictionary[robot_id_unity] = None
+ else:
+ actions.extend([next_action.x, next_action.y, next_action.g, 0, 0])
+ # update current node
+ self.envData.current_nodes_dictionary[robot_id_unity] = Node(next_action.x, next_action.y,
+ next_action.g)
+ # update path
+ self.shortest_paths[robot_id_unity] = path[1:]
+
+ # add action to action_tuple and set the actions
+ actions = np.array(actions)
+ actions.resize(len(agents), self.envData.size_of_action)
+ action_tuple.add_discrete(actions)
+
+ self.unityEnv.set_actions(self.behavior_name, action_tuple)
+ self.unityEnv.step() # send actions to unity and wait until a new decision is requested
+
+ # reset the environment
+ def reset(self):
+ self.unityEnv.reset()
+
+ # close the environment
+ def close(self):
+ self.close()
+
+ # assigns the next available task to an idle robot, if possible
+ def assign_new_task(self):
+ # check if there are tasks that have to be assigned
+ # if this is the case, get first task and assign to available robot
+ if len(self.envData.tasks_to_be_assigned) != 0:
+ task = self.envData.tasks_to_be_assigned[0]
+ # get available robot
+ for robot_id in self.envData.instance_ids:
+ if self.envData.assigned_tasks_dictionary[robot_id] is None:
+ # robot is free: assign task to robot and break out of for loop
+ self.assign_task_to_robot(robot_id, task)
+ break
+
+
+ # compute path from current_node that finishes given task
+ # for test purposes pick up and delivery need to have rotation 0
+ # at pick up delivery robot stays at same node
+ # returns a sequence of nodes and pick up/delivery actions
+ def compute_path(self, current_node, task):
+ path1 = self.graph.computeShortestPath(current_node,
+ Node(task.PickUp.Row, task.PickUp.Column, 0))
+ path1 = path1[1:]
+ path2 = self.graph.computeShortestPath(Node(task.PickUp.Row, task.PickUp.Column, 0),
+ Node(task.Delivery.Row, task.Delivery.Column, 0))
+ path2 = path2[1:]
+
+ return path1 + ["pickup"] + path2 + ["deliver"]
+
+
+ # for every robot initially there is an empty path to be followed
+ def initialize_shortest_paths(self):
+ for robot_id in self.envData.instance_ids:
+ self.shortest_paths[robot_id] = []
+
+ def assign_task_to_robot(self, robot_instanceID, task):
+ # update envData
+ self.envData.assign_task(robot_instanceID, task)
+ # compute path for robot
+ self.shortest_paths[robot_instanceID] = \
+ self.compute_path(self.envData.current_nodes_dictionary[robot_instanceID],task)
+ # send info to unity
+ taskDict = {"Task": {
+ "PickUp": {"Row": task.PickUp.Row, "Column": task.PickUp.Column},
+ "Delivery": {"Row": task.Delivery.Row, "Column": task.Delivery.Column}},
+ "InstanceID": robot_instanceID}
+ self.taskChannel.send_string(json.dumps(taskDict))
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Graph.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e03b997172dae6855dca7f06890606239e7063
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Graph.py
@@ -0,0 +1,126 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+from networkx import DiGraph, shortest_path
+from Node import Node
+
+"""
+Class Graph creates a graph based on matrix, deviation (unit of rotation) and whether robots can move backwards).
+Its nodes contain position in the grid and orientation wrt north (see class <c>Node</c>).
+Networkx package as underlying data structure.
+"""
+
+class Graph:
+ def __init__(self, matrix, deviation, cangobackwards):
+ self.matrix = matrix
+ self.number_of_rows = len(matrix)
+ self.number_of_columns = len(matrix[0])
+ self.deviation = deviation
+ self.cangobackwards = cangobackwards
+ self.graph = DiGraph()
+
+ for i in range(self.number_of_rows):
+ for j in range(self.number_of_columns):
+ # we have to add nodes to the graph when we encounter a number different than 1
+ if matrix[i][j] != 1:
+ self.addEdgesByRotation(i, j)
+ self.addEdgesByMovingForward(i,j)
+
+ def addEdgesByRotation(self, row, column):
+ degree = 0
+ while degree < 360:
+ newnode = Node(row, column, degree)
+
+ # add all edges from newnode staying at the same position (row, number), i.e. rotation
+
+ # rotate left
+ rotateLeftNode = Node(row, column, get_degree_in_range_0_360(degree - self.deviation))
+ self.graph.add_edge(newnode, rotateLeftNode)
+
+ # rotate right
+ rotateRightNode = Node(row, column, get_degree_in_range_0_360(degree + self.deviation))
+ self.graph.add_edge(newnode, rotateRightNode)
+
+ degree += self.deviation
+
+ def addEdgesByMovingForward(self, row, column):
+ # check left
+ if column > 0 and self.matrix[row][column-1] != 1:
+ # we can move forward if we have degree 270
+ currentnode = Node(row, column, 270)
+ neighbourleft = Node(row, column-1, 270)
+ self.graph.add_edge(currentnode, neighbourleft)
+ # we can move backward if we have degree 90
+ if self.cangobackwards:
+ currentnode = Node(row, column, 90)
+ neighbourleft = Node(row, column-1, 90)
+ self.graph.add_edge(currentnode, neighbourleft)
+
+ # check right
+ if column < self.number_of_columns -1 and self.matrix[row][column+1] != 1:
+ # we can move forward if we have degree 90
+ currentnode = Node(row, column, 90)
+ neighbourright = Node(row, column+1, 90)
+ self.graph.add_edge(currentnode, neighbourright)
+ # we can move backward if we have degree 270
+ if self.cangobackwards:
+ currentnode = Node(row, column, 270)
+ neighbourright = Node(row, column+1, 270)
+ self.graph.add_edge(currentnode, neighbourright)
+
+ # check up
+ if row > 0 and self.matrix[row-1][column] != 1:
+ # we can move forward if we have degree 0
+ currentnode = Node(row, column, 0)
+ neighbourup = Node(row-1, column, 0)
+ self.graph.add_edge(currentnode, neighbourup)
+ # we can move backward if we have degree 180
+ if self.cangobackwards:
+ currentnode = Node(row, column, 180)
+ neighbourup = Node(row-1, column, 180)
+ self.graph.add_edge(currentnode, neighbourup)
+
+ # check down
+ if row < self.number_of_rows-1 and self.matrix[row+1][column] != 1:
+ # we can move forward if we have degree 180
+ currentnode = Node(row, column, 180)
+ neighbourdown = Node(row+1, column, 180)
+ self.graph.add_edge(currentnode, neighbourdown)
+ # we can move backward if we have degree 0
+ if self.cangobackwards:
+ currentnode = Node(row, column, 0)
+ neighbourdown = Node(row+1, column, 0)
+ self.graph.add_edge(currentnode, neighbourdown)
+
+ def computeShortestPath(self, start_node, end_node):
+ #compute shortest path from start_node to end_node: return a list
+ return shortest_path(self.graph, start_node, end_node)
+
+def get_degree_in_range_0_360(degree):
+ rest = degree % 360
+ if rest < 0:
+ return rest + 360
+ return rest
+
+
+
+
+
+
+
+
+
+
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Node.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Node.py
new file mode 100644
index 0000000000000000000000000000000000000000..126684f885f63b51edcd2b6684d26d7a847c51af
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Node.py
@@ -0,0 +1,57 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Node represent nodes in the graph. Every node contains a
+position (x,y) in the grid
+and a rotation in degree where
+0 = North
+90 = East
+280 = South
+270 = West
+all degrees must be between 0 and 360
+
+attributes are set to private such that they cannot be overwritten and there is no problem with the hash
+"""
+
+class Node:
+ def __init__(self,x,y,g):
+ self._x = x
+ self._y = y
+ self._g = g
+
+ @property
+ def x(self):
+ return self._x
+
+ @property
+ def y(self):
+ return self._y
+
+ @property
+ def g(self):
+ return self._g
+
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.g == other.g
+
+
+ def __hash__(self):
+ return (((self.x * 397) ** self.y) * 397) **self.g
+
+ def __str__(self):
+ return "[(" + str(self.x) + ", " + str(self.y) + ")" + "," + str(self.g) + "]"
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Pipfile b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8108a7836978e89ef3320e61b72d3c4eb3be6
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Pipfile
@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+networkx = "==2.5"
+mlagents = "==0.23.0"
+mlagents-envs = "==0.23.0"
+
+[dev-packages]
+
+[requires]
+python_version = "3.6"
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Task.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Task.py
new file mode 100644
index 0000000000000000000000000000000000000000..16dd163021e1d3fb53b5ea42052621255e9a7e79
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/Task.py
@@ -0,0 +1,50 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Class Task represents tasks which consist of 2 positions: a pickup and a delivery position.
+"""
+
+class Task:
+ def __init__(self, pos1, pos2):
+ self.PickUp = pos1
+ self.Delivery = pos2
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.PickUp == other.PickUp and self.Delivery == other.Delivery
+
+ def __hash__(self):
+ hash_code = hash(self.PickUp)
+ hash_code = (hash_code * 397) ** hash(self.Delivery)
+ return hash_code
+
+ def __str__(self):
+ return "[" + str(self.PickUp) + ", " + str(self.Delivery) + "]"
+
+
+class Position:
+ def __init__(self, x, y):
+ self.Row = x
+ self.Column = y
+
+ def __eq__(self, other):
+ return self.__class__ == other.__class__ and self.Row == other.Row and self.Column == other.Column
+
+ def __hash__(self):
+ return ((self.Row * 397) ** self.Column) * 397
+
+ def __str__(self):
+ return "(" + str(self.Row) + ", " + str(self.Column) + ")"
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/TaskSideChannel.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/TaskSideChannel.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd99877185ec3103f5ccda97c5352856e9745634
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/TaskSideChannel.py
@@ -0,0 +1,58 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import json
+from mlagents_envs.side_channel.side_channel import (
+ SideChannel,
+ IncomingMessage,
+ OutgoingMessage,
+)
+import uuid
+
+from Task import Task, Position
+
+
+"""
+Class that processes info from the TaskSideChannel.
+- receives new tasks that have to assigned to robots
+- sends assignment of tasks to robots
+"""
+
+
+class TaskSideChannel(SideChannel):
+
+ def __init__(self) -> None:
+ super().__init__(uuid.UUID("c11ab982-82b8-4194-b387-3bd0fe9ebdc7")) #must coincide with ChannelId set in c#
+ self.environment_data = None #info on the environment
+
+ # channel receives new tasks that have to be assigned
+ def on_message_received(self, msg: IncomingMessage) -> None:
+ data = msg.read_string()
+ new_task = json.loads(data)
+ new_task = new_task["task"]
+ task_to_add = Task(Position(new_task["PickUp"]["Row"], new_task["PickUp"]["Column"]),
+ Position(new_task["Delivery"]["Row"], new_task["Delivery"]["Column"]))
+ # add task to task to be assigned
+ self.environment_data.add_task_to_tasks_to_be_assigned(task_to_add)
+
+ # channel sends information when a task has been assigned (to which robot)
+ def send_string(self, data: str) -> None:
+
+ # Add the string to an OutgoingMessage
+ msg = OutgoingMessage()
+ msg.write_string(data)
+ # We call this method to queue the data we want to send
+ super().queue_message_to_send(msg)
\ No newline at end of file
diff --git a/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/main.py b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..130dda386a5fd019fb5c84e3c36d457a2493b824
--- /dev/null
+++ b/Algorithms/Online_Decentralized/Algorithm_test_toyexample_online_decentralized/main.py
@@ -0,0 +1,44 @@
+"""
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+"""
+
+"""
+Main file that controls decision making for the Online Centralized Pick Up and Delivery Problem
+This is a toy example!
+Uses EnvironmentManager who assigns tasks in order to available agents
+and then computes shortest paths, no check on collisions.
+"""
+
+from mlagents_envs.environment import UnityEnvironment
+import EnvChannel, TaskSideChannel
+import EnvironmentManager
+
+# as long as simulation_going is true, new actions will be generated
+
+simulation_going = True
+
+# setup side channels and unity environment
+envChannel = EnvChannel.EnvChannel()
+taskChannel = TaskSideChannel.TaskSideChannel()
+env = UnityEnvironment(file_name=None, side_channels=[envChannel, taskChannel])
+
+# set up environment manager
+envManager = EnvironmentManager.EnvironmentManager(envChannel.envData, env, envChannel, taskChannel)
+
+
+while simulation_going:
+ envManager.step()
+
+envManager.close()
diff --git a/PickUpandDelivery/.gitignore b/PickUpandDelivery/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..8c31b4fb7a402827dadeb8e1f7881f6b62b25528
--- /dev/null
+++ b/PickUpandDelivery/.gitignore
@@ -0,0 +1,98 @@
+# This .gitignore file should be placed at the root of your Unity project directory
+#
+# Get latest from https://github.com/github/gitignore/blob/master/Unity.gitignore
+#
+/[Ll]ibrary/
+/[Tt]emp/
+/[Oo]bj/
+/[Bb]uild/
+/[Bb]uilds/
+/[Ll]ogs/
+/[Uu]ser[Ss]ettings/
+
+# MemoryCaptures can get excessive in size.
+# They also could contain extremely sensitive data
+/[Mm]emoryCaptures/
+
+# Asset meta data should only be ignored when the corresponding asset is also ignored
+!/[Aa]ssets/**/*.meta
+
+# Uncomment this line if you wish to ignore the asset store tools plugin
+# /[Aa]ssets/AssetStoreTools*
+
+# Autogenerated Jetbrains Rider plugin
+/[Aa]ssets/Plugins/Editor/JetBrains*
+
+# Visual Studio cache directory
+.vs/
+
+# Gradle cache directory
+.gradle/
+
+# Autogenerated VS/MD/Consulo solution and project files
+ExportedObj/
+.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+*.mdb
+*.opendb
+*.VC.db
+
+# Unity3D generated meta files
+*.pidb.meta
+*.pdb.meta
+*.mdb.meta
+
+# Unity3D generated file on crash reports
+sysinfo.txt
+
+# Builds
+*.apk
+*.aab
+*.unitypackage
+
+# Crashlytics generated file
+crashlytics-build.properties
+
+# Packed Addressables
+/[Aa]ssets/[Aa]ddressable[Aa]ssets[Dd]ata/*/*.bin*
+
+# Temporary auto-generated Android Assets
+/[Aa]ssets/[Ss]treamingAssets/aa.meta
+/[Aa]ssets/[Ss]treamingAssets/aa/*
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+.idea/
+
+# Generated files
+.idea/**/contentModel.xml
+
+.DS_Store
+__pycache__/
+
+
+# Extra
+./build
+./obj
+./Logs
+./Library
+./Temp
+./ProjectSettings
+
+#ML agents
+/Assets/ML-Agents/Timers*
+
diff --git a/PickUpandDelivery/Assembly-CSharp.csproj.DotSettings b/PickUpandDelivery/Assembly-CSharp.csproj.DotSettings
new file mode 100644
index 0000000000000000000000000000000000000000..b5b235a96cba87306e02eb00fa185c185f99c22f
--- /dev/null
+++ b/PickUpandDelivery/Assembly-CSharp.csproj.DotSettings
@@ -0,0 +1,2 @@
+<wpf:ResourceDictionary xml:space="preserve" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" xmlns:s="clr-namespace:System;assembly=mscorlib" xmlns:ss="urn:shemas-jetbrains-com:settings-storage-xaml" xmlns:wpf="http://schemas.microsoft.com/winfx/2006/xaml/presentation">
+ <s:String x:Key="/Default/CodeInspection/CSharpLanguageProject/LanguageLevel/@EntryValue">Default</s:String></wpf:ResourceDictionary>
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Fonts.meta b/PickUpandDelivery/Assets/Fonts.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b21a2be7e93069be1414f4e55f2281efe2bbf640
--- /dev/null
+++ b/PickUpandDelivery/Assets/Fonts.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 7c67c373f3538ef46bc6cad48b8fc69f
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Fonts/ArialCE.ttf b/PickUpandDelivery/Assets/Fonts/ArialCE.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..a53879583c273432b10231e109677f10a30ee290
Binary files /dev/null and b/PickUpandDelivery/Assets/Fonts/ArialCE.ttf differ
diff --git a/PickUpandDelivery/Assets/Fonts/ArialCE.ttf.meta b/PickUpandDelivery/Assets/Fonts/ArialCE.ttf.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b5465334c10b78ec9c2f289eb7b4e28ebb8dc6fb
--- /dev/null
+++ b/PickUpandDelivery/Assets/Fonts/ArialCE.ttf.meta
@@ -0,0 +1,22 @@
+fileFormatVersion: 2
+guid: 424c8962f52026a4c96be91a0f9cc09d
+TrueTypeFontImporter:
+ externalObjects: {}
+ serializedVersion: 4
+ fontSize: 16
+ forceTextureCase: -2
+ characterSpacing: 0
+ characterPadding: 1
+ includeFontData: 1
+ fontName: Arial CE
+ fontNames:
+ - Arial CE
+ fallbackFontReferences: []
+ customCharacters:
+ fontRenderingMode: 0
+ ascentCalculationMode: 1
+ useLegacyBoundsCalculation: 0
+ shouldRoundAdvanceValue: 1
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/ML-Agents.meta b/PickUpandDelivery/Assets/ML-Agents.meta
new file mode 100644
index 0000000000000000000000000000000000000000..cc6931f55431e3405539c60d283f04e4f66e768c
--- /dev/null
+++ b/PickUpandDelivery/Assets/ML-Agents.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 1c329b599f3585243a8ef689890c9264
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Materials.meta b/PickUpandDelivery/Assets/Materials.meta
new file mode 100644
index 0000000000000000000000000000000000000000..56af04ecde68ef744e07dadbee36551a03e67c20
--- /dev/null
+++ b/PickUpandDelivery/Assets/Materials.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 81ccf87ab9c53bf4193803e5daa4485b
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Materials/AgentBlue.mat b/PickUpandDelivery/Assets/Materials/AgentBlue.mat
new file mode 100644
index 0000000000000000000000000000000000000000..98afe276c85991a80022bc840443e213ce8898c9
--- /dev/null
+++ b/PickUpandDelivery/Assets/Materials/AgentBlue.mat
@@ -0,0 +1,80 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!21 &2100000
+Material:
+ serializedVersion: 6
+ m_ObjectHideFlags: 0
+ m_PrefabParentObject: {fileID: 0}
+ m_PrefabInternal: {fileID: 0}
+ m_Name: AgentBlue
+ m_Shader: {fileID: 47, guid: 0000000000000000f000000000000000, type: 0}
+ m_ShaderKeywords: _GLOSSYREFLECTIONS_OFF _SPECULARHIGHLIGHTS_OFF
+ m_LightmapFlags: 4
+ m_EnableInstancingVariants: 0
+ m_DoubleSidedGI: 0
+ m_CustomRenderQueue: -1
+ stringTagMap: {}
+ disabledShaderPasses: []
+ m_SavedProperties:
+ serializedVersion: 3
+ m_TexEnvs:
+ - _BumpMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailAlbedoMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailMask:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailNormalMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _EmissionMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _MainTex:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _MetallicGlossMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _OcclusionMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _ParallaxMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _SpecGlossMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ m_Floats:
+ - _BumpScale: 1
+ - _Cutoff: 0.5
+ - _DetailNormalMapScale: 1
+ - _DstBlend: 0
+ - _GlossMapScale: 1
+ - _Glossiness: 0.5
+ - _GlossyReflections: 0
+ - _Metallic: 0
+ - _Mode: 0
+ - _OcclusionStrength: 1
+ - _Parallax: 0.02
+ - _SmoothnessTextureChannel: 0
+ - _SpecularHighlights: 0
+ - _SrcBlend: 1
+ - _UVSec: 0
+ - _ZWrite: 1
+ m_Colors:
+ - _Color: {r: 0.12941177, g: 0.5882353, b: 0.9529412, a: 1}
+ - _EmissionColor: {r: 0, g: 0, b: 0, a: 1}
diff --git a/PickUpandDelivery/Assets/Materials/AgentBlue.mat.meta b/PickUpandDelivery/Assets/Materials/AgentBlue.mat.meta
new file mode 100644
index 0000000000000000000000000000000000000000..8869d32d9f6cba37e1949e2cedc800dcf82623cc
--- /dev/null
+++ b/PickUpandDelivery/Assets/Materials/AgentBlue.mat.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 65c786e6f7696492e9956e3b8eac5564
+NativeFormatImporter:
+ externalObjects: {}
+ mainObjectFileID: 0
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Materials/Eye.mat b/PickUpandDelivery/Assets/Materials/Eye.mat
new file mode 100644
index 0000000000000000000000000000000000000000..703bddeb2b720271b3f10738fb6345fe61da8e16
--- /dev/null
+++ b/PickUpandDelivery/Assets/Materials/Eye.mat
@@ -0,0 +1,77 @@
+%YAML 1.1
+%TAG !u! tag:unity3d.com,2011:
+--- !u!21 &2100000
+Material:
+ serializedVersion: 6
+ m_ObjectHideFlags: 0
+ m_PrefabParentObject: {fileID: 0}
+ m_PrefabInternal: {fileID: 0}
+ m_Name: Eye
+ m_Shader: {fileID: 46, guid: 0000000000000000f000000000000000, type: 0}
+ m_ShaderKeywords: _ALPHATEST_ON _GLOSSYREFLECTIONS_OFF _SPECULARHIGHLIGHTS_OFF
+ m_LightmapFlags: 4
+ m_EnableInstancingVariants: 0
+ m_DoubleSidedGI: 0
+ m_CustomRenderQueue: 2450
+ stringTagMap:
+ RenderType: TransparentCutout
+ disabledShaderPasses: []
+ m_SavedProperties:
+ serializedVersion: 3
+ m_TexEnvs:
+ - _BumpMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailAlbedoMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailMask:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _DetailNormalMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _EmissionMap:
+ m_Texture: {fileID: 0}
+ m_Scale: {x: 1, y: 1}
+ m_Offset: {x: 0, y: 0}
+ - _MainTex:
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diff --git a/PickUpandDelivery/Assets/Scenes/Online_Decentralized.unity.meta b/PickUpandDelivery/Assets/Scenes/Online_Decentralized.unity.meta
new file mode 100644
index 0000000000000000000000000000000000000000..952bd1e9e110583d94b471a30ff4ddd69f4aee7d
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diff --git a/PickUpandDelivery/Assets/Scripts/Common.meta b/PickUpandDelivery/Assets/Scripts/Common.meta
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diff --git a/PickUpandDelivery/Assets/Scripts/Common/Robot.meta b/PickUpandDelivery/Assets/Scripts/Common/Robot.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c31fb5ac36f8ae0c17fb802fe7e098b3c460fc47
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Robot.meta
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diff --git a/PickUpandDelivery/Assets/Scripts/Common/Robot/IRobot.cs b/PickUpandDelivery/Assets/Scripts/Common/Robot/IRobot.cs
new file mode 100644
index 0000000000000000000000000000000000000000..eaa09010e417ae65c6e35fe66df8e5bc87ff03be
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Robot/IRobot.cs
@@ -0,0 +1,18 @@
+using Common.SimulationManager;
+using Common.Warehouse;
+using UnityEngine;
+
+namespace Common.Robot
+{
+ public interface IRobot
+ {
+ ISimulationManager simulationManager { get; set; }
+ Task assignedTask { get; set; }
+
+ //METHODS
+ MonoBehaviour GetMonoBehaviour();
+ void AssignTask(Task task);
+ void PickUp();
+ Task Deliver();
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Robot/IRobot.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Robot/IRobot.cs.meta
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diff --git a/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs b/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs
new file mode 100644
index 0000000000000000000000000000000000000000..1f4c95db81bad6863742785680ed82b081ae6308
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs
@@ -0,0 +1,255 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using System.Linq;
+using Common.Warehouse;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+namespace Common.Robot
+{
+ /// <summary>
+ /// Static class <c> RobotMethods </c> contains methods used by classes implementing the <c> IRobot </c> interface.
+ /// - InitializeRobotAtStart(IRobot robot) draws robot using initial nodes and sets parameters for collision detection
+ /// - UpdatePosition(IRobot robot): updates position and rotation for robot
+ /// - CollisionEvent(IRobot robot): determines what happens when a robot collides
+ /// - AssignTask(IRobot robot, Task task): assign task to robot, color robot in task color
+ /// - PickUp(IRobot robot): pick up: add cross on top of robot
+ /// - Deliver(IRobot robot): deliver task: remove cross, reset color of robot
+ /// - CollectObservations(IRobot robot, VectorSensor sensor): collect observations when robot is agent
+ /// - OnActionReceived(IRobot robot, ActionBuffers actionBuffers, Color defaultColor): do action when robot is agent
+ ///
+ /// Private methods:
+ /// - RemoveTask(IRobot robot, Color defaultColor): removes tasks for robot, resets color and removes cross
+ /// - ActivateCross(IRobot robot, Task assignedTask): activate cross on robot using complementary color of task
+ /// - DeactivateCross(IRobot robot): deactivate cross
+ /// </summary>
+ public static class RobotMethods
+ {
+ /// <summary>
+ /// Draw robot at start.
+ /// </summary>
+ /// <param name="robot"></param>
+ public static void InitializeRobotAtStart(IRobot robot)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ // draw robot
+ robotMB.transform.localScale = robot.simulationManager.SIZE * robot.simulationManager.SCALEROBOT * Vector3.one;
+ robotMB.transform.localPosition = new Vector3(
+ robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).GridPosition.Row * robot.simulationManager.SIZE,
+ robotMB.transform.localScale.y / 2 + robot.simulationManager.YROBOTFLOATING, // put on tile + floating
+ robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).GridPosition.Column * robot.simulationManager.SIZE);
+ robotMB.transform.eulerAngles =
+ new Vector3(0, robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).Degrees, 0);
+
+ // add rigidbody and collider for collision detection
+ // change size of boxcollider otherwise there is a collision when
+ robotMB.gameObject.AddComponent<Rigidbody>();
+ robotMB.gameObject.GetComponent<Rigidbody>().useGravity = false;
+ robotMB.gameObject.GetComponent<Rigidbody>().isKinematic = false;
+ robotMB.gameObject.AddComponent<BoxCollider>().isTrigger = true;
+
+ // deactivate cross on top of robot
+ DeactivateCross(robot);
+ }
+
+ /// <summary>
+ /// Update robot position and rotation.
+ /// </summary>
+ /// <param name="robot"></param>
+ public static void UpdatePosition(IRobot robot)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ // only update positions robot if there haven't been detected any collisions
+ if (!robot.simulationManager.collisions)
+ {
+ var maxDistance = robot.simulationManager.SIZE * Time.fixedDeltaTime;
+ var maxDegs = robot.simulationManager.parameters.Rotation * Time.fixedDeltaTime;
+
+ robotMB.transform.localPosition = Vector3.MoveTowards(
+ robotMB.transform.localPosition,
+ new Vector3(
+ robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).GridPosition.Row *
+ robot.simulationManager.SIZE,
+ robotMB.transform.localPosition.y,
+ robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).GridPosition.Column *
+ robot.simulationManager.SIZE),
+ maxDistance
+ );
+
+ robotMB.transform.rotation = Quaternion.RotateTowards(
+ robotMB.transform.rotation,
+ Quaternion.Euler(0, robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID()).Degrees, 0),
+ maxDegs
+ );
+ }
+ }
+
+ /// <summary>
+ /// Method used when robot is decision maker.
+ /// Collect observations and add to sensor.
+ /// An observation for one robot consists of a row, a column and a number of degrees.
+ /// </summary>
+ /// <param name="robot"></param>
+ /// <param name="sensor"></param>
+ public static void CollectObservations(IRobot robot, VectorSensor sensor)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ sensor.AddObservation(robotMB.gameObject.GetInstanceID());
+ var node = robot.simulationManager.GetCurrentNode(robotMB.gameObject.GetInstanceID());
+ sensor.AddObservation(node.GridPosition.Row);
+ sensor.AddObservation(node.GridPosition.Column);
+ sensor.AddObservation(node.Degrees);
+ }
+
+ /// <summary>
+ /// Method used when Robot is decision maker.
+ /// When an action is received the position and location of the robot is updated and if applicable
+ /// pickup or delivery action is invoked.
+ /// </summary>
+ /// <param name="robot"></param>
+ /// <param name="actionBuffers"></param>
+ public static void OnActionReceived(IRobot robot, ActionBuffers actionBuffers, Color defaultColor)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ var individualActions = actionBuffers.DiscreteActions.ToArray();
+ var row = individualActions[0];
+ var column = individualActions[1];
+ var degrees = individualActions[2];
+ var pickup = individualActions[3];
+ var deliver = individualActions[4];
+ Debug.Log(String.Format("Robot {0} received action [{1}, {2}, {3}, {4}, {5}]",
+ robotMB.gameObject.GetInstanceID(), row, column, degrees, pickup, deliver));
+ robot.simulationManager.currentNodes[robotMB.gameObject.GetInstanceID()] = new Node(new Position(row, column), degrees);
+
+ if (pickup == 1)
+ {
+ PickUp(robot);
+ }
+
+ if (deliver == 1)
+ {
+ // remove task in simulation manager
+ robot.simulationManager.RemoveTask(robot.assignedTask);
+
+ // remove task for robot
+ Deliver(robot, defaultColor);
+
+ // create new task
+ robot.simulationManager.AddNewTask();
+ }
+ }
+
+ /// <summary>
+ /// After an collision event, simulationManager has to be notified.
+ /// </summary>
+ /// <param name="robot"></param>
+ public static void CollisionEvent(IRobot robot)
+ {
+ robot.simulationManager.collisions = true;
+ }
+
+ /// <summary>
+ /// Assign task to robot. Color robot using task color.
+ /// </summary>
+ /// <param name="task"> a Task object</param>
+ public static void AssignTask(IRobot robot, Task task)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ robot.assignedTask = task;
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").GetComponent<Renderer>().material.color =
+ robot.simulationManager.currentTasks[task];
+ }
+
+ /// <summary>
+ /// Remove assigned task for robot. Reset color to default and deactivate cross.
+ /// </summary>
+ private static void RemoveTask(IRobot robot, Color defaultColor)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ robot.assignedTask = null;
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").GetComponent<Renderer>().material.color = defaultColor;
+
+ // deactivate cross on top of robot
+ DeactivateCross(robot);
+ }
+
+ /// <summary>
+ /// After pick up, a cross is added on top of the agent. We use the complementary color of the task color.
+ /// </summary>
+ /// <exception cref="Exception"> if robot is not at the pick up position</exception>
+ public static void PickUp(IRobot robot)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ if (! robot.assignedTask.PickUp.Equals(robot.simulationManager.currentNodes[robotMB.gameObject.GetInstanceID()].GridPosition))
+ {
+ throw new Exception("robot " + robotMB.gameObject.GetInstanceID() + "pickup incorrect position: "
+ + robot.simulationManager.currentNodes[robotMB.gameObject.GetInstanceID()].GridPosition);
+ }
+
+ // activate cross on top of robot
+ ActivateCross(robot, robot.assignedTask);
+ }
+
+ /// <summary>
+ /// After delivering a task, the assigned task must be removed and the robot's color has to be resetted to defaultcolor
+ /// and the cross has to be removed.
+ /// </summary>
+ /// <param name="robot"></param>
+ /// <param name="defaultColor"></param>
+ /// <returns></returns>
+ /// <exception cref="Exception">if robot is not at the delivery position</exception>
+ public static Task Deliver(IRobot robot, Color defaultColor)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ if (! robot.assignedTask.Delivery.Equals(robot.simulationManager.currentNodes[robotMB.gameObject.GetInstanceID()].GridPosition))
+ {
+ throw new Exception("robot " + robotMB.gameObject.GetInstanceID() + "delivery incorrect position: "
+ + robot.simulationManager.currentNodes[robotMB.gameObject.GetInstanceID()].GridPosition);
+ }
+
+ var taskToReturn = robot.assignedTask;
+ RemoveTask(robot, defaultColor);
+ return taskToReturn;
+ }
+
+ /// <summary>
+ /// Deactivate cross on top of robot.
+ /// </summary>
+ /// <param name="robot"></param>
+ private static void DeactivateCross(IRobot robot)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x1").gameObject.SetActive(false);
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x2").gameObject.SetActive(false);
+ }
+
+ /// <summary>
+ /// Activate cross on top of robot and color robot in complementary color of assigned task color.
+ /// </summary>
+ private static void ActivateCross(IRobot robot, Task assignedTask)
+ {
+ MonoBehaviour robotMB = robot.GetMonoBehaviour();
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x1").gameObject.SetActive(true);
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x2").gameObject.SetActive(true);
+ Color negativeColor = UtilityMethods.GetNegativeColor(robot.simulationManager.currentTasks[assignedTask]);
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x1").GetComponent<Renderer>().material.color = negativeColor;
+ robotMB.transform.Find("Agent").Find("AgentCube_Blue").Find("x2").GetComponent<Renderer>().material.color = negativeColor;
+ }
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e93eb39c778b1a53d5e604321ad00f3a33e9e6ad
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Robot/RobotMethods.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 94d94501e73f4450bbb36cfedaeb7ec8
+timeCreated: 1628838963
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts.meta b/PickUpandDelivery/Assets/Scripts/Common/Scripts.meta
new file mode 100644
index 0000000000000000000000000000000000000000..e5ce89abb67f1d5865e70e9a3c5562bd3f722eff
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 7f30b9cb10964db1bf69c766b3bd9a94
+timeCreated: 1628838540
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs b/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs
new file mode 100644
index 0000000000000000000000000000000000000000..dceecb4d57b02b61de703c0e1c938d7e7348a5d7
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs
@@ -0,0 +1,66 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using UnityEngine;
+
+namespace Common.Scripts
+{
+ public class CameraMoveScript: MonoBehaviour
+ {
+ [SerializeField] float speed = 0.5f;
+ [SerializeField] float sensitivity = 1.0f;
+
+ Camera cam;
+ Vector3 anchorPoint;
+ Quaternion anchorRot;
+
+ private void Awake()
+ {
+ cam = GetComponent<Camera>();
+ }
+
+ void FixedUpdate()
+ {
+ Vector3 move = Vector3.zero;
+ if (Input.GetKey(KeyCode.W))
+ move += Vector3.forward * speed;
+ if (Input.GetKey(KeyCode.S))
+ move -= Vector3.forward * speed;
+ if (Input.GetKey(KeyCode.D))
+ move += Vector3.right * speed;
+ if (Input.GetKey(KeyCode.A))
+ move -= Vector3.right * speed;
+ if (Input.GetKey(KeyCode.E))
+ move += Vector3.up * speed;
+ if (Input.GetKey(KeyCode.Q))
+ move -= Vector3.up * speed;
+ transform.Translate(move);
+
+ if (Input.GetMouseButtonDown(1))
+ {
+ anchorPoint = new Vector3(Input.mousePosition.y, -Input.mousePosition.x);
+ anchorRot = transform.rotation;
+ }
+ if (Input.GetMouseButton(1))
+ {
+ Quaternion rot = anchorRot;
+ Vector3 dif = anchorPoint - new Vector3(Input.mousePosition.y, -Input.mousePosition.x);
+ rot.eulerAngles += dif * sensitivity;
+ transform.rotation = rot;
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..532e4d444ac4a2869182d28986df5e30ec4fe2ca
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/CameraMoveScript.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 208230689b9953844af11c709edefaea
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs b/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs
new file mode 100644
index 0000000000000000000000000000000000000000..01952c7d4970b942935ac255d76afe0640ec20d2
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs
@@ -0,0 +1,72 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using Common.SimulationManager;
+using Common.Warehouse;
+using UnityEngine;
+
+namespace Common.Scripts
+{
+ /// <summary>
+ /// Script <c> EndPoint </c> takes care of coloring the endpoints and contains methods
+ /// - ResetColors(): changes color back to default, removes cross on top
+ /// - SetAsPickUp(Color taskColor): changes color to taskColor, removes cross on top
+ /// - SetAsDelivery(Color taskColor): changes color to taskColor, adds cross on top in complementary color of taskColor
+ /// </summary>
+
+ public class EndPoint : MonoBehaviour
+ {
+ public ISimulationManager simulationManager;
+
+ // endpoint as default (not pickup, not delivery)
+ public void ResetColors()
+ {
+ gameObject.GetComponent<Renderer>().material.color = simulationManager.colorEndPoint;
+ transform.Find("x1").gameObject.SetActive(false);
+ transform.Find("x2").gameObject.SetActive(false);
+ transform.Find("text").gameObject.SetActive(false);
+ }
+
+ //set as pick up point for a certain task (color and release time)
+ public void SetAsPickUp(Task task)
+ {
+ Color taskColor = simulationManager.currentTasks[task];
+ gameObject.GetComponent<Renderer>().material.color = taskColor;
+ transform.Find("x1").gameObject.SetActive(false);
+ transform.Find("x2").gameObject.SetActive(false);
+ if (task.ReleaseTime != 0)
+ {
+ transform.Find("text").gameObject.SetActive(true);
+ transform.Find("text").gameObject.GetComponent<TextMesh>().text = task.ReleaseTime.ToString();
+ transform.Find("text").gameObject.GetComponent<MeshRenderer>().material.color =
+ UtilityMethods.GetNegativeColor(taskColor);
+ }
+ }
+
+ // set as delivery point for a certain task (color): there will be an x drawn on top
+ public void SetAsDelivery(Task task)
+ {
+ Color taskColor = simulationManager.currentTasks[task];
+ gameObject.GetComponent<Renderer>().material.color = taskColor;
+ transform.Find("x1").gameObject.SetActive(true);
+ transform.Find("x2").gameObject.SetActive(true);
+ transform.Find("text").gameObject.SetActive(false);
+ transform.Find("x1").GetComponent<Renderer>().material.color = UtilityMethods.GetNegativeColor(taskColor);
+ transform.Find("x2").GetComponent<Renderer>().material.color = UtilityMethods.GetNegativeColor(taskColor);
+ }
+
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..1f523e30802ecd593c4dbc9e8c5bccd0f6bcb03e
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/EndPoint.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: f3dcff173f5992d4db93b38be21d0d1d
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs b/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs
new file mode 100644
index 0000000000000000000000000000000000000000..f42f783961dc28d5c8678fa41534c878444e0cb5
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs
@@ -0,0 +1,154 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using Common.SimulationManager;
+using Common.Warehouse;
+using UnityEngine;
+
+namespace Common.Scripts
+{
+ /// <summary>
+ /// Script <c> Floor </c> sets up the floor
+ /// - draws tiles, walls, endpoints, parking
+ /// - draws initial tasks (pick up and delivery)
+ /// and sets up the camera
+ /// </summary>
+
+ public class Floor : MonoBehaviour
+ {
+ [SerializeField]
+ private GameObject tileWallPrefab = default; // prefab for tiles and walls
+ [SerializeField]
+ private GameObject endPointPrefab = default; // prefab for endPoints
+ [SerializeField]
+ private Camera mainCamera = default;
+ [SerializeField]
+ private MonoBehaviour SimulationController = default;// add gameobject SimulationController in editor
+
+ private ISimulationManager simulationManager;
+
+ // Start is called before the first frame update
+ void Start()
+ {
+ simulationManager = SimulationController as ISimulationManager;
+
+ // draw tiles and walls (initial status, no tasks)
+ for (var row = 0; row < simulationManager.parameters.Matrix.Length; row++)
+ {
+ for (var column = 0; column < simulationManager.parameters.Matrix[0].Length; column++)
+ {
+ GameObject cube;
+
+ if (simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeWall) //wall
+ {
+ cube = Instantiate(tileWallPrefab);
+ cube.GetComponent<Renderer>().material.color = simulationManager.colorWall;
+ }
+
+ else if (simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeTile) // normal tile
+ {
+ cube = Instantiate(tileWallPrefab);
+ cube.GetComponent<Renderer>().material.color = simulationManager.colorTile;
+ }
+
+ else if (simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeEndPoint) // end point
+ {
+ cube = Instantiate(endPointPrefab);
+ cube.GetComponent<EndPoint>().simulationManager = simulationManager;
+ cube.GetComponent<EndPoint>().ResetColors();
+ simulationManager.endPoints[new Position(row, column)] = cube;
+ }
+ else //parking
+ {
+ cube = Instantiate(tileWallPrefab);
+ cube.GetComponent<Renderer>().material.color = simulationManager.colorParking;
+ }
+
+ // we want to detect collisions between robots and walls
+ cube.GetComponent<BoxCollider>().enabled =
+ simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeWall;
+ cube.GetComponent<BoxCollider>().isTrigger =
+ simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeWall;
+
+ // add size tile/wall
+ cube.transform.localScale = new Vector3(simulationManager.SIZE,
+ simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeWall ? simulationManager.SIZE : 0.2f,
+ simulationManager.SIZE);
+
+ cube.transform.position = new Vector3(row*simulationManager.SIZE,
+ 0, column*simulationManager.SIZE);
+
+ cube.transform.parent = gameObject.transform;
+ }
+ }
+ // add colors to current tasks
+ //TODO: what with tasks from list? -> they might overlap
+ foreach (var task in simulationManager.currentTasks)
+ {
+ var pickUp = task.Key.PickUp;
+ var delivery = task.Key.Delivery;
+
+ simulationManager.endPoints[pickUp].GetComponent<EndPoint>().SetAsPickUp(task.Key);
+ simulationManager.endPoints[delivery].GetComponent<EndPoint>().SetAsDelivery(task.Key);
+ }
+
+ SetCamera();
+ }
+
+ private void SetCamera()
+ {
+ var maxbound = Math.Max(simulationManager.parameters.Matrix.Length, simulationManager.parameters.Matrix[0].Length);
+ mainCamera.gameObject.transform.position =
+ new Vector3(simulationManager.SIZE * maxbound + simulationManager.SIZE,
+ simulationManager.SIZE * maxbound, - simulationManager.SIZE);
+ mainCamera.gameObject.transform.LookAt(gameObject.GetBoundsWithChildren().center);
+ }
+ }
+
+ public static class UnityExtensions
+ {
+ public static Bounds GetBoundsWithChildren(this GameObject gameObject)
+ {
+ // GetComponentsInChildren() also returns components on gameobject which you call it on
+ // you don't need to get component specially on gameObject
+ Renderer[] renderers = gameObject.GetComponentsInChildren<Renderer>();
+
+ // If renderers.Length = 0, you'll get OutOfRangeException
+ Bounds bounds = renderers.Length > 0 ? renderers[0].bounds : new Bounds();
+
+ // Start from 1 because we've already encapsulated renderers[0]
+ for (int i = 1; i < renderers.Length; i++)
+ {
+ if (renderers[i].enabled)
+ {
+ bounds.Encapsulate(renderers[i].bounds);
+ }
+ }
+
+ return bounds;
+ }
+
+ public static void FocusOn(this Camera camera, GameObject focusedObject, float marginPercentage)
+ {
+ Bounds bounds = focusedObject.GetBoundsWithChildren();
+ float maxExtent = bounds.extents.magnitude;
+ float minDistance = (maxExtent * marginPercentage) / Mathf.Sin(Mathf.Deg2Rad * camera.fieldOfView / 2f);
+ camera.transform.position = focusedObject.transform.position - Vector3.forward * minDistance;
+ camera.nearClipPlane = minDistance - maxExtent;
+ }
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b0c33767854c37e49af831d753810e7981dd87c3
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Scripts/Floor.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a74be3508dbe36342858e20cec768f98
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SideChannels.meta b/PickUpandDelivery/Assets/Scripts/Common/SideChannels.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fe09df17e6f2a481f6867d37b24c0acaefde0dae
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SideChannels.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: f0be1d97d4954270a69446174d8b05e2
+timeCreated: 1628838678
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..dfbd17e817abffaef70f4ba6783981a6801d30a2
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs
@@ -0,0 +1,49 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using Unity.MLAgents.SideChannels;
+
+namespace Common.SideChannels
+{
+ /// <summary>
+ /// SideChannel used for sending initial information on environment to Python client.
+ /// </summary>
+
+ public class EnvSideChannel : SideChannel
+ {
+ public EnvSideChannel()
+ {
+ ChannelId = new Guid("621f0a70-4f87-11ea-a6bf-784f4387d1f7");
+ }
+
+ protected override void OnMessageReceived(IncomingMessage msg)
+ {
+ //var receivedString = msg.ReadString();
+ //Debug.LogError("From Python : " + receivedString);
+ }
+
+ public void SendEnv(string message)
+ {
+ var stringToSend = message;
+ using (var msgOut = new OutgoingMessage())
+ {
+ msgOut.WriteString(stringToSend);
+ QueueMessageToSend(msgOut);
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7524541d0ada964c61a8bb7166de5742f326606b
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/EnvSideChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 30ce75aad8425a644b59307793a9af24
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs
new file mode 100644
index 0000000000000000000000000000000000000000..067346e475ab7a23e0c537e7f5b60f003d06c7cc
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs
@@ -0,0 +1,68 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Newtonsoft.Json;
+using Unity.MLAgents.SideChannels;
+using UnityEngine;
+
+namespace Common.SideChannels
+{
+ /// <summary>
+ /// SideChannel used for sending and receiving information on tasks.
+ /// - sends initial information on tasks (initial tasks)
+ /// - receives information on assigned tasks
+ /// </summary>
+
+ public class TaskSideChannel : SideChannel
+ {
+ private class TaskAssignment
+ {
+ public Task Task = null;
+ public int InstanceId = 0;
+ }
+
+ public ISimulationManager SimulationManager;
+
+ public TaskSideChannel()
+ {
+ ChannelId = new Guid("c11ab982-82b8-4194-b387-3bd0fe9ebdc7");
+ }
+
+ protected override void OnMessageReceived(IncomingMessage msg)
+ {
+ var receivedString = msg.ReadString();
+ Debug.Log("message received");
+ var newTask = JsonConvert.DeserializeObject<TaskAssignment>(receivedString);
+ if (newTask != null)
+ {
+ SimulationManager.AssignTask(newTask.Task, newTask.InstanceId);
+ }
+ }
+
+ public void SendEnv(string message)
+ {
+ var stringToSend = message;
+ using (var msgOut = new OutgoingMessage())
+ {
+ msgOut.WriteString(stringToSend);
+ QueueMessageToSend(msgOut);
+ }
+ }
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..607eb97952f2026887a605686f0e9d79ff05ee69
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SideChannels/TaskSideChannel.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 15225950aa7307b4086f7d0a544ca3c1
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SimulationManager.meta b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager.meta
new file mode 100644
index 0000000000000000000000000000000000000000..21f8c0cbd4b13a7977cf237a1d1b69f3f1198b50
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: e59645e254214d8995295041954a75cf
+timeCreated: 1628838787
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs
new file mode 100644
index 0000000000000000000000000000000000000000..b2b6b7c410feedcc0003d2a2124240357226e9c7
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs
@@ -0,0 +1,89 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.Collections.Generic;
+using Common.SideChannels;
+using Common.Warehouse;
+using UnityEngine;
+
+namespace Common.SimulationManager
+{
+ public interface ISimulationManager
+ {
+ // PARAMETERS
+ // parameters of warehouse
+ WarehouseParameters parameters { get; set; }
+ // offline or online
+ bool online { get; }
+ // input warehouse and tasks
+ string warehouseConfigFile { get; }
+ string tasksListFile { get; }
+
+ // COLORS
+ // colors for tiles and walls
+ Color colorWall { get; }
+ Color colorTile { get; }
+ Color colorEndPoint { get; }
+ Color colorParking { get; }
+
+ // colors used for tasks and the robots that have been assigned these tasks: initially colors will be created
+ // and stored in notAssignedColors, when assigned to a task, color will be moved to assignedColors
+ HashSet<Color> assignedColors { get; set; }
+ HashSet<Color> notAssignedColors { get; set; }
+
+ // ENDPOINTS
+ // dictionary containing all endpoints
+ // every endpointposition points to a gameobject, gameobject will be setup by the Floor script
+ Dictionary<Position, GameObject> endPoints { get; set; }
+
+ // TASKS
+ // at every timestep there will be max numberOfTasks tasks that have to be assigned (by the python code)
+ // current available tasks and their assigned colors: these are the tasks to remain to be assigned to robots
+ Dictionary<Task, Color> currentTasks { get; set; }
+ // when using a predefined set of tasks, the tasks that are not yet in currentTasks are sstored in remainingTasks
+ Task[] remainingTasks { get; set; }
+
+ // ROBOTS
+ // For every robot the current node where he is going, i.e. the next action
+ Dictionary<int, Node> currentNodes { get; }
+ // unity instance ID and corresponding game object
+ Dictionary<int, GameObject> robotGameObjects { get; set; }
+
+ // ENVIRONMENT VARIABLES
+ // check for collisions
+ bool collisions { get; set; }
+ // observation- and actionsize
+ int observationSize { get; }
+ int actionSize { get; }
+ // environment variables for drawing
+ float SIZE { get; } // Size of square/tile, this is kept constant to avoid having to change camera perspective
+ float SCALEROBOT { get; } // Ratio robot to tile, for drawing robot
+ float YROBOTFLOATING { get; } // Floating y value robot, for drawing robot
+
+ //SIDE CHANNELS
+ EnvSideChannel envChannel { get; set; }
+ TaskSideChannel taskChannel { get; set; }
+
+
+ //METHODS
+ Node GetCurrentNode(int instanceID);
+ void RemoveTask(Task task);
+ void AddNewTask();
+ void AssignTask(Task task, int instanceID);
+ MonoBehaviour GetMonoBehaviour();
+ }
+}
+
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c921a4853f3f02bbaf6ade3d8564793494197b64
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/ISimulationManager.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: b03b437f56df4d82a29a944ac1b111ae
+timeCreated: 1628603152
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs
new file mode 100644
index 0000000000000000000000000000000000000000..a178dbd9b5fab9a5b93c45ad0c28508ef10c64cb
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs
@@ -0,0 +1,484 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Common.Robot;
+using Common.Scripts;
+using Common.SideChannels;
+using Common.Warehouse;
+using Newtonsoft.Json;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Unity.MLAgents.SideChannels;
+using Random = System.Random;
+using UnityEngine;
+
+namespace Common.SimulationManager
+{
+ /// <summary>
+ /// Static class <c> SimulationManagerMethods </c> contains methods used by
+ /// classes implementing the <c> ISimulationManager </c> interface.
+ /// - SetUpWarehouse<T>(ISimulationManager simulationManager, GameObject robotPrefab, bool isDecisionMaker): setup warehouse, tasks,
+ /// side channels, behavior component
+ /// - GetCurrentNode(ISimulationManager simulationManager, int instanceID): get next node for robot with given id
+ /// - AssignTask<T>(ISimulationManager simulationManager, Task task, int instanceID): assign a task to a robot with given instanceID
+ /// - AddNewTask(ISimulationManager simulationManager): add a new task to <c> simulationManager.currentTasks </c> if there are
+ /// still tasks available
+ /// - RemoveTask(ISimulationManager simulationManager, Task task): remove a task from <c> simulationManager.currentTasks </c>
+ /// - CollectObservations(ISimulationManager simulationManager, VectorSensor sensor): collect observations for all robots and
+ /// add to sensor
+ /// - ShowCollisionMessage(ISimulationManager simulationManager): show message collision
+ ///
+ /// private methods:
+ /// - ReadInput(ISimulationManager simulationManager): set parameters and tasks from input
+ /// - SetUpRobots<T>(ISimulationManager simulationManager, GameObject robotPrefab): initialize and set up robots
+ /// - SetUpSideChannels(ISimulationManager simulationManager): set up side channels
+ /// - SetUpBehaviorParameters(ISimulationManager simulationManager): set up behavior parameters
+ /// - CreateColors(ISimulationManager simulationManager): create as many colors as there are tasks available at every point in time
+ /// and store in set <c> simulationManager.notAssignedColors </c>
+ /// - SetUpEndPoints(ISimulationManager simulationManager): store positions for all endpoints in dictionary <c> simulationManager.endPoints </c>
+ /// - InitializeCurrentTasks(ISimulationManager simulationManager): store first tasks in <c>
+ /// - GetNewTask(ISimulationManager simulationManager): get next task to be added to <c> simulationManager.currentTasks </c>
+ /// </summary>
+ public static class SimulationManagerMethods
+ {
+
+ /// <summary>
+ /// Sets up warehouse: reads input, creates colors for tasks, sets up tasks, sets up endpoints, sets up robots
+ /// using the prefab, sets up side channels. If applicable sets up a behavior component.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="robotPrefab"></param>
+ /// <param name="isDecisionMaker"></param>
+ /// <typeparam name="T"></typeparam>
+ public static void SetUpWarehouse<T>(ISimulationManager simulationManager, GameObject robotPrefab, bool isDecisionMaker) where T:IRobot
+ {
+ // read input and set up parameters
+ ReadInput(simulationManager);
+
+ // setup colors
+ CreateColors(simulationManager);
+
+ // setup endpoints
+ SetUpEndPoints(simulationManager);
+
+ // setup tasks:
+ InitializeCurrentTasks(simulationManager);
+
+ // setup robots
+ SetUpRobots<T>(simulationManager, robotPrefab);
+
+ // set up side channels
+ SetUpSideChannels(simulationManager);
+
+ // set up behavior parameters
+ if (isDecisionMaker)
+ {
+ SetUpBehaviorParameters(simulationManager);
+ }
+ }
+
+ /// <summary>
+ /// Returns current node, i.e. the next node to go to, for a robot with a given instance ID.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="instanceID"></param>
+ /// <returns>current node</returns>
+ public static Node GetCurrentNode(ISimulationManager simulationManager, int instanceID)
+ {
+ return simulationManager.currentNodes[instanceID];
+ }
+
+ /// <summary>
+ /// Assign a task to a robot with a given instanceID.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="task"></param>
+ /// <param name="instanceID"></param>
+ /// <typeparam name="T"> the corresponding robot type </typeparam>
+ public static void AssignTask<T>(ISimulationManager simulationManager, Task task, int instanceID) where T:IRobot
+ {
+ simulationManager.robotGameObjects[instanceID].GetComponent<T>().AssignTask(task);
+ }
+
+ /// <summary>
+ /// Add a task to <c> simulationManager.currentTasks </c> if there are still tasks available.
+ /// Assign a color and send information to python code.
+ /// </summary>
+ /// <exception cref="Exception">thrown when currentTasks has already reached capacity</exception>
+ public static void AddNewTask(ISimulationManager simulationManager)
+ {
+ if (simulationManager.currentTasks.Count == simulationManager.parameters.NumberOfTasks)
+ {
+ throw new Exception("currentTasks is full");
+ }
+
+ if (simulationManager.notAssignedColors.Count == 0)
+ {
+ throw new Exception("no colors left");
+ }
+
+ var newTask = GetNewTask(simulationManager);
+
+ // newTask can be null if there are no tasks left
+ if (newTask != null)
+ {
+ // get color from notAssignedColors (will not be empty, we can always take element at index 0) and move to assignedColors
+ var color = simulationManager.notAssignedColors.ElementAt(0);
+ simulationManager.notAssignedColors.Remove(color);
+ simulationManager.assignedColors.Add(color);
+
+ // add task to currentTasks
+ simulationManager.currentTasks.Add(newTask, color);
+
+ // color pickup/deliver
+ simulationManager.endPoints[newTask.PickUp].GetComponent<EndPoint>().SetAsPickUp(newTask);
+ simulationManager.endPoints[newTask.Delivery].GetComponent<EndPoint>().SetAsDelivery(newTask);
+
+ // use task sidechannel
+ var jsonTask = JsonConvert.SerializeObject(
+ new
+ {
+ task = newTask
+ });
+ simulationManager.taskChannel.SendEnv(jsonTask);
+ }
+ }
+
+ /// <summary>
+ /// Remove a task from <c> simulationManager.currentTasks </c> that was assigned to a robot.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="task"></param>
+ /// <exception cref="Exception"></exception>
+ public static void RemoveTask(ISimulationManager simulationManager, Task task)
+ {
+ if (!simulationManager.currentTasks.ContainsKey(task))
+ {
+ throw new Exception("task was not assigned and cannot be removed: " + task);
+ }
+
+ var colorOfTask = simulationManager.currentTasks[task];
+
+ // reset color of pickup/deliver endpoints
+ simulationManager.endPoints[task.PickUp].GetComponent<EndPoint>().ResetColors();
+ simulationManager.endPoints[task.Delivery].GetComponent<EndPoint>().ResetColors();
+
+ // move color to notAssignedColors
+ simulationManager.assignedColors.Remove(colorOfTask);
+ simulationManager.notAssignedColors.Add(colorOfTask);
+
+ // remove from currentTasks
+ simulationManager.currentTasks.Remove(task);
+ }
+
+ /// <summary>
+ /// Method used when simulationManager is decision maker.
+ /// Collect observations for each robot and add to sensor.
+ /// An observation for one robot consists of an instance id, a row, a column and a number of degrees.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="sensor"></param>
+ public static void CollectObservations(ISimulationManager simulationManager, VectorSensor sensor)
+ {
+ foreach (var node in simulationManager.currentNodes)
+ {
+ sensor.AddObservation(node.Key);
+ sensor.AddObservation(node.Value.GridPosition.Row);
+ sensor.AddObservation(node.Value.GridPosition.Column);
+ sensor.AddObservation(node.Value.Degrees);
+ }
+ }
+
+ /// <summary>
+ /// When actions are received, do actions. Update position robots, and if applicable pickup or deliver.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="actionBuffers"></param>
+ /// <typeparam name="T"></typeparam>
+ public static void OnActionReceived<T>(ISimulationManager simulationManager, ActionBuffers actionBuffers) where T:IRobot
+ {
+ for (var i = 0; i < simulationManager.parameters.NumberOfRobots; i++)
+ {
+ var individualActions
+ = actionBuffers.DiscreteActions.Skip(i * simulationManager.actionSize).Take(simulationManager.actionSize).ToArray();
+ var instanceID = individualActions[0];
+ var row = individualActions[1];
+ var column = individualActions[2];
+ var degrees = individualActions[3];
+ var pickup = individualActions[4];
+ var deliver = individualActions[5];
+ Debug.Log(String.Format("Robot {0} received action [{1}, {2}, {3}, {4}, {5}]",
+ instanceID, row, column, degrees, pickup, deliver));
+ simulationManager.currentNodes[instanceID] = new Node(new Position(row, column), degrees);
+
+ if (pickup == 1)
+ {
+ simulationManager.robotGameObjects[instanceID].gameObject.GetComponent<T>().PickUp();
+ }
+
+ if (deliver == 1)
+ {
+ // remove task for robot
+ var deliveredTask
+ = simulationManager.robotGameObjects[instanceID].gameObject.GetComponent<T>().Deliver();
+
+ // remove task in simulationmanager
+ RemoveTask(simulationManager,deliveredTask);
+
+ // add new task
+ AddNewTask(simulationManager);
+ }
+ }
+ }
+
+ /// <summary>
+ /// Read input and set parameters and if applicable preset tasks.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void ReadInput(ISimulationManager simulationManager)
+ {
+ simulationManager.parameters = new ReadConfig(simulationManager.warehouseConfigFile).Parameters;
+
+ //offline: check whether randomTasks is set to false
+ if (!simulationManager.online && simulationManager.parameters.RandomTasks)
+ {
+ throw new ArgumentException("In offline setting random tasks should be set to false");
+ }
+
+ // if tasks are not generated randomly they are taken from a preset list of tasks
+ if (! simulationManager.parameters.RandomTasks)
+ {
+ // release time allowed (online -> false, offline -> true)
+ simulationManager.remainingTasks =
+ new ReadTasks(simulationManager.tasksListFile, simulationManager.parameters.Matrix,
+ simulationManager.parameters.CodeEndPoint, ! simulationManager.online).ListOfTasks;
+ // in any case there should be at least simulationManager.parameters.NumberOfTasks in preset list of tasks
+ if (simulationManager.remainingTasks.Length < simulationManager.parameters.NumberOfTasks)
+ {
+ throw new ArgumentException("given predefined set of tasks should contain at least " +
+ "the given number of tasks");
+ }
+ // in offline setting simulationManager.parameters.NumberOfTasks in preset list of tasks must be exactly
+ // equal to the length of the list of tasks
+ if (!simulationManager.online && simulationManager.remainingTasks.Length !=
+ simulationManager.parameters.NumberOfTasks)
+ {
+ throw new ArgumentException("given predefined set of tasks should contain " +
+ "the given number of tasks");
+ }
+ }
+ }
+
+ public static void ShowCollisionMessage(ISimulationManager simulationManager)
+ {
+ GUI.Box(new Rect(50, 50, 100, 25), "Collision!");
+ }
+
+ /// <summary>
+ /// Set up robots using a prefab. Save the gameobject together with its instance id in <c> simulationManager.robotGameObjects </c>
+ /// and add th current node together with its istance id to <c> simulationManager.currentNodes </c>.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <param name="robotPrefab"></param>
+ /// <typeparam name="T"></typeparam>
+ private static void SetUpRobots<T>(ISimulationManager simulationManager, GameObject robotPrefab) where T:IRobot
+ {
+ for (var i = 0; i < simulationManager.parameters.NumberOfRobots; i++)
+ {
+ var robot = MonoBehaviour.Instantiate(robotPrefab);
+ robot.GetComponent<T>().simulationManager = simulationManager;
+ simulationManager.robotGameObjects.Add(robot.GetInstanceID(), robot);
+ simulationManager.currentNodes.Add(robot.GetInstanceID(), simulationManager.parameters.InitialNodes[i]);
+ }
+ }
+
+ /// <summary>
+ /// Set up side channels and send initial information on warehouse to Python code.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void SetUpSideChannels(ISimulationManager simulationManager)
+ {
+ // create side channel for communication on the environment
+ simulationManager.envChannel = new EnvSideChannel();
+ // The channel must be registered with the SideChannelManager class
+ SideChannelManager.RegisterSideChannel(simulationManager.envChannel);
+ // communicate info on environment to Python code through the SideChannel
+ var json = JsonConvert.SerializeObject(
+ new
+ {
+ matrixLayout = simulationManager.parameters.Matrix,
+ rotationRobot = simulationManager.parameters.Rotation,
+ sizeOfAction = simulationManager.actionSize,
+ sizeOfObservation = simulationManager.observationSize,
+ numberOfRobotsInEnv = simulationManager.parameters.NumberOfRobots,
+ initialNodesOfRobots = simulationManager.parameters.InitialNodes,
+ currentTasks = simulationManager.currentTasks.Keys,
+ numberOfCurrentTasks = simulationManager.parameters.NumberOfTasks,
+ backwards = simulationManager.parameters.CanGoBackWards,
+ instanceIDSRobots = simulationManager.robotGameObjects.Keys,
+ tasksGeneratedRandomly = simulationManager.parameters.RandomTasks
+ });
+ simulationManager.envChannel.SendEnv(json);
+
+ // create side channel for communication on tasks
+ simulationManager.taskChannel = new TaskSideChannel();
+ SideChannelManager.RegisterSideChannel(simulationManager.taskChannel);
+ simulationManager.taskChannel.SimulationManager = simulationManager;
+ }
+
+ /// <summary>
+ /// Set up behaviorparameters.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void SetUpBehaviorParameters(ISimulationManager simulationManager)
+ {
+ MonoBehaviour simulationManagerMB = simulationManager.GetMonoBehaviour();
+ var component = simulationManagerMB.GetComponent<BehaviorParameters>();
+ // Set up the parameters of the behavioral component.
+ component.BrainParameters.VectorObservationSize
+ = simulationManager.parameters.NumberOfRobots*simulationManager.observationSize;
+ component.BrainParameters.ActionSpec
+ = ActionSpec.MakeDiscrete(new int [simulationManager.parameters.NumberOfRobots*simulationManager.actionSize]);
+ }
+
+ /// <summary>
+ /// Create random colors as many as there are available tasks (at any point in time). They should be all different
+ /// and also not equal to the default tile/wall/endpoint/parking colors.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void CreateColors(ISimulationManager simulationManager)
+ {
+ simulationManager.notAssignedColors = new HashSet<Color>();
+
+ var rand = new Random();
+ while (simulationManager.notAssignedColors.Count < simulationManager.parameters.NumberOfTasks)
+ {
+ var randomColor = new Color(
+ (float) rand.NextDouble(),
+ (float) rand.NextDouble(),
+ (float) rand.NextDouble(),
+ 1
+ );
+
+ if (!randomColor.Equals(simulationManager.colorWall) && !randomColor.Equals(simulationManager.colorTile)
+ && !randomColor.Equals(simulationManager.colorEndPoint) &&
+ !randomColor.Equals(simulationManager.colorParking))
+ {
+ simulationManager.notAssignedColors
+ .Add(randomColor); // if randomColor is already in notAssignedColors it will not be added
+ }
+ }
+ }
+
+ /// <summary>
+ /// Setup Endpoints dictionary. Gameobjects will be created and added by the <c>Floor</c> script.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void SetUpEndPoints(ISimulationManager simulationManager)
+ {
+ simulationManager.endPoints = new Dictionary<Position, GameObject>();
+
+ for (var row = 0; row < simulationManager.parameters.Matrix.Length; row++)
+ {
+ for (var column = 0; column < simulationManager.parameters.Matrix[0].Length; column++)
+ {
+ if (simulationManager.parameters.Matrix[row][column] == simulationManager.parameters.CodeEndPoint)
+ {
+ simulationManager.endPoints.Add(new Position(row, column), null);
+ }
+ }
+ }
+ }
+
+ /// <summary>
+ /// Setup CurrentTasks: add tasks to <c> simulationManager.currentTasks </c> until we have
+ /// <c> simulationManager.parameters.NumberOfTaskss </c> tasks. Assign a color to each task. Initialize
+ /// <c> simulationManager.assignedColor </c> to keep track of assigned colors.
+ /// Initial coloring is done by the <c>Floor</c> script.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ private static void InitializeCurrentTasks(ISimulationManager simulationManager)
+ {
+ simulationManager.currentTasks = new Dictionary<Task, Color>();
+ simulationManager.assignedColors = new HashSet<Color>();
+
+ while (simulationManager.currentTasks.Count < simulationManager.parameters.NumberOfTasks)
+ {
+ // get new task (this will not be null by the restriction that remainingTasks should have at least numberOfTasks elements)
+ var newTask = GetNewTask(simulationManager);
+
+ // get color from notAssignedColors (will not be empty, we can always take element at index 0) and move to assignedColors
+ var color = simulationManager.notAssignedColors.ElementAt(0);
+ simulationManager.notAssignedColors.Remove(color);
+ simulationManager.assignedColors.Add(color);
+
+ // add to currentTasks
+ simulationManager.currentTasks.Add(newTask, color);
+ }
+ }
+
+ /// <summary>
+ /// Get a new task.
+ /// - If randomTasks == true, we create a new task that is not in currentTasks.
+ /// - If randomTasks == false, we get the next task from remainingTasks, if there are no tasks left return null.
+ /// </summary>
+ /// <param name="simulationManager"></param>
+ /// <returns>A new task</returns>
+ private static Task GetNewTask(ISimulationManager simulationManager)
+ {
+ //random task generation
+ if (simulationManager.parameters.RandomTasks)
+ {
+ var random = new Random();
+ // get random endpoint as candidate for next pick up
+ var randomPickUp = simulationManager.endPoints.Keys.ElementAt(random.Next(0, simulationManager.endPoints.Count));
+ // update randomPickUp until is not an endpoint in currentTasks
+ while (simulationManager.currentTasks.Any(task => task.Key.HasEndPoint(randomPickUp)))
+ {
+ randomPickUp = simulationManager.endPoints.Keys.ElementAt(random.Next(0, simulationManager.endPoints.Count));
+ }
+ // get random endpoint as candidate for next delivery
+ var randomDelivery = simulationManager.endPoints.Keys.ElementAt(random.Next(0, simulationManager.endPoints.Count));
+ //update randomDelivery until is not an endpoint in currentTasks and different from randomPickUp
+ while (simulationManager.currentTasks.Any(task => task.Key.HasEndPoint(randomDelivery))
+ || randomDelivery.Equals(randomPickUp))
+ {
+ randomDelivery = simulationManager.endPoints.Keys.ElementAt(random.Next(0, simulationManager.endPoints.Count));
+ }
+
+ return new Task(randomPickUp, randomDelivery,0);
+ }
+
+ // non random task generation: we get next task from remainingTasks and update remainingTasks
+
+ // there are no remaining tasks left
+ if (simulationManager.remainingTasks.Length == 0)
+ {
+ return null;
+ }
+
+ // there are remaining tasks left
+ var newTask = simulationManager.remainingTasks[0];
+ simulationManager.remainingTasks = simulationManager.remainingTasks.Skip(1).ToArray();
+ return newTask;
+ }
+
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..b85ef31b92a6040395056edf8759a1ceace1266d
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/SimulationManager/SimulationManagerMethods.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: cd8f59650b1a42caa417c3842ef8806c
+timeCreated: 1628858423
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/UtilityMethods.meta b/PickUpandDelivery/Assets/Scripts/Common/UtilityMethods.meta
new file mode 100644
index 0000000000000000000000000000000000000000..60296f45b4f36ed92b0fda5e4a1922d1984feafb
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/UtilityMethods.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: ddc2bac031dc4237900a0faec627a6fd
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse.meta
new file mode 100644
index 0000000000000000000000000000000000000000..05f2a350aae902853017b79884c3d1db2ec82ac6
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: 911a6d88db034382a0c812352f12439e
+timeCreated: 1628838833
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs
new file mode 100644
index 0000000000000000000000000000000000000000..e2dabd56b9ee0682341efaf71745569b58a0005c
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs
@@ -0,0 +1,67 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Class <c> Node </c> represents the nodes in the graph. Every node contains
+ /// - a position (x,y) in the grid
+ /// - the difference in degrees with the direction north:
+ /// - 0 = North
+ /// - 90 = East
+ /// - 180 = South
+ /// - 270 = West
+ /// </summary>
+
+ public class Node
+ {
+ public Position GridPosition { get; set; }
+ public int Degrees { get; }
+
+ public Node(Position pos, int degrees)
+ {
+ GridPosition = pos;
+ Degrees = degrees;
+ }
+
+ protected bool Equals(Node other)
+ {
+ return Equals(GridPosition, other.GridPosition) && Degrees == other.Degrees;
+ }
+
+ public override bool Equals(object obj)
+ {
+ if (ReferenceEquals(null, obj)) return false;
+ if (ReferenceEquals(this, obj)) return true;
+ if (obj.GetType() != this.GetType()) return false;
+ return Equals((Node) obj);
+ }
+
+ public override int GetHashCode()
+ {
+ unchecked
+ {
+ return ((GridPosition != null ? GridPosition.GetHashCode() : 0) * 397) ^ Degrees;
+ }
+ }
+
+ public override string ToString()
+ {
+ return "(" + GridPosition.Row + "," + GridPosition.Column + ")" + " " + Degrees;
+ }
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..c1332fd67db4a07ac8df46efb8df6ae36a0c26a2
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Node.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 192efae1e20ced443998b710269bfeee
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs
new file mode 100644
index 0000000000000000000000000000000000000000..69973cb6e99f53800fd3d68d121a1a5edec1e8cf
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs
@@ -0,0 +1,63 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// A position in the grid is described by two integers Row and Column,e.g.
+ /// for a grid with dimensions 2 rows and 3 columns we have following positions
+ /// (0,0) (0,1) (0,2)
+ /// (1,0) (1,1) (1,2)
+ /// </summary>
+
+ public class Position
+ {
+ public int Row { get; set; }
+ public int Column { get; set; }
+
+ public Position(int row, int column)
+ {
+ Row = row;
+ Column = column;
+ }
+
+ protected bool Equals(Position other)
+ {
+ return Row == other.Row && Column == other.Column;
+ }
+
+ public override bool Equals(object obj)
+ {
+ if (ReferenceEquals(null, obj)) return false;
+ if (ReferenceEquals(this, obj)) return true;
+ if (obj.GetType() != this.GetType()) return false;
+ return Equals((Position) obj);
+ }
+
+ public override int GetHashCode()
+ {
+ unchecked
+ {
+ return (Row * 397) ^ Column;
+ }
+ }
+
+ public override string ToString()
+ {
+ return "(" + Row + "," + Column + ")";
+ }
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..fd9da1efbff63704ba48101e50d5423fcd20af34
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Position.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 31b30aafb0088be4aab3311716cb5315
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs
new file mode 100644
index 0000000000000000000000000000000000000000..826b1aa592e2d79293c6df58418d71a3e5bfbc36
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs
@@ -0,0 +1,171 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System;
+using System.IO;
+using Newtonsoft.Json;
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Class reads a json string and converting it to an object of the class WarehouseParameters.
+ /// does multiple checks on the resulting object
+ /// - CheckMatrix(): checks dimensions of the matrix and the values of the entries
+ /// - CheckRotation(): checks whether the unit of rotation is a divisor of 90
+ /// - CheckNumberOfRobots(): checks whether for each robot exactly 1 initial node is given
+ /// - CheckValidityNodes(): checks whether the given initial nodes are valid nodes (the position should
+ /// correspond to a parking location in the matrix and the rotation should be a divisor of the unit of rotation)
+ /// - CheckNumberOfTasks(): checks whether there no more current tasks than the available endpoints can accomodate
+ /// </summary>
+
+ public class ReadConfig
+ {
+ public WarehouseParameters Parameters { get; }
+ private int numberOfEndpoints;
+
+ public ReadConfig(string pathToFile)
+ {
+ var jsonString = File.ReadAllText(pathToFile);
+ Parameters = JsonConvert.DeserializeObject<WarehouseParameters>(jsonString);
+ CheckMatrix();
+ CheckRotation();
+ CheckNumberOfRobots();
+ CheckValidityNodes();
+ CheckNumberOfTasks();
+ }
+
+ /// <summary>
+ /// Checks validity of rotation.
+ /// </summary>
+ /// <exception cref="ArgumentException"> thrown when the rotation is not a divisor of 90.</exception>
+ private void CheckRotation()
+ {
+ if (90 % Parameters.Rotation != 0)
+ {
+ throw new ArgumentException("Invalid rotation in config file");
+ }
+ }
+
+ /// <summary>
+ /// Checks number of initial nodes.
+ /// </summary>
+ /// <exception cref="ArgumentException"> thrown when number of initial nodes does not coincide
+ /// with given number of robots.</exception>
+ private void CheckNumberOfRobots()
+ {
+ if (Parameters.NumberOfRobots != Parameters.InitialNodes.Length)
+ {
+ throw new ArgumentException("Invalid number of robots in config file");
+ }
+ }
+
+ /// <summary>
+ /// Checks validity of initial nodes.
+ /// </summary>
+ /// <exception cref="ArgumentException"> thrown when an initial node is not a parking node
+ /// or an allowed rotation. </exception>
+ private void CheckValidityNodes()
+ {
+ for (var i = 0; i < Parameters.NumberOfRobots; i++)
+ {
+ var initialNode = Parameters.InitialNodes[i];
+ if (Parameters.Matrix[initialNode.GridPosition.Row][initialNode.GridPosition.Column] != Parameters.CodeParking
+ || IsInvalidNodeRotation(initialNode.Degrees))
+ {
+ throw new ArgumentException("Invalid initial node in config file: " + initialNode);
+ }
+ }
+ }
+
+ /// <summary>
+ /// Checks shape of matrix and entries. Counts number of endpoints.
+ /// </summary>
+ /// <exception cref="ArgumentException"> thrown when matrix does not have correct shape or when entries are not valid</exception>
+ private void CheckMatrix()
+ {
+ var numberOfRows = Parameters.Matrix.Length;
+ if (numberOfRows == 0)
+ {
+ throw new ArgumentException("Matrix has length 0");
+ }
+
+ var numberOfColumns = Parameters.Matrix[0].Length;
+
+ for (var row = 0; row < numberOfRows; row++)
+ {
+ // check shape
+ if (Parameters.Matrix[row].Length != numberOfColumns)
+ {
+ throw new ArgumentException("Matrix has incorrect shape");
+ }
+
+ for (var column = 0; column < numberOfColumns; column++)
+ {
+ if (IsInvalidCode(Parameters.Matrix[row][column]))
+ {
+ throw new ArgumentException("Matrix contains unaccepted value at position " +
+ "(" + row + "," + column + ")");
+ }
+
+ if (Parameters.Matrix[row][column] == Parameters.CodeEndPoint)
+ {
+ numberOfEndpoints++;
+ }
+ }
+ }
+ }
+
+ /// <summary>
+ /// Checks whether the rotation is valid for nodes.
+ /// </summary>
+ /// <param name="rotation"> an int </param>
+ /// <returns> true when the unit of rotation is not a divisor of rotation</returns>
+ private bool IsInvalidNodeRotation(int rotation)
+ {
+ return rotation % Parameters.Rotation != 0;
+ }
+
+ /// <summary>
+ /// Checks whether it is a valid code.
+ /// </summary>
+ /// <param name="code"></param>
+ /// <returns> true when the given code is not valid.</returns>
+ private bool IsInvalidCode(int code)
+ {
+ return code != Parameters.CodeWall
+ && code != Parameters.CodeTile
+ && code != Parameters.CodeParking
+ && code != Parameters.CodeEndPoint;
+ }
+
+ /// <summary>
+ /// Checks whether the number of tasks is not more than the endpoints can accomodate, i.e. endpoints/2.
+ /// Do not call this method before CheckMatrix().
+ /// </summary>
+ /// <exception cref="ArgumentException"> thrown when there are no endpoints or when there are too many tasks</exception>
+ private void CheckNumberOfTasks()
+ {
+ if (numberOfEndpoints == 0)
+ {
+ throw new ArgumentException("There are no endpoints. Did you run Checkmatrix()?");
+ }
+ if (Parameters.NumberOfTasks > numberOfEndpoints / 2)
+ {
+ throw new ArgumentException("Invalid number of tasks, not enough endpoints");
+ }
+ }
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..d90c1b489f0716c655e49760feb09a2af960a648
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadConfig.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 12faaaf5e8d363f4b8d5a75aafc56fe2
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs
new file mode 100644
index 0000000000000000000000000000000000000000..4c0c2b22d1b04fd90d71694fe70c7caa9ef617a2
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs
@@ -0,0 +1,70 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+using System;
+using System.IO;
+using Newtonsoft.Json;
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Class <c> ReadTasks </c> is used to read a preset list of tasks (json format). It converts the list to
+ /// an instance of Task[] and will only be called in the offline problem setting.
+ /// Does multiple checks on the resulting object:
+ /// - every tasks has to be a valid endpoint and has to correspond to the correct code in the config matrix
+ /// - whether release time is allowed or not
+ /// </summary>
+ public class ReadTasks
+ {
+ public Task[] ListOfTasks;
+ private int[][] Matrix;
+ private int EndPointCode;
+ private bool ReleaseTimeAllowed;
+
+ public ReadTasks(string pathToFile, int[][] matrix, int endPointCode, bool releaseTimeAllowed)
+ {
+ Matrix = matrix;
+ var jsonString = File.ReadAllText(pathToFile);
+ ListOfTasks = JsonConvert.DeserializeObject<Task[]>(jsonString);
+ EndPointCode = endPointCode;
+ ReleaseTimeAllowed = releaseTimeAllowed;
+ CheckInput();
+ }
+
+ // check whether pickup and delivery correspond to the correct number in the matrix
+ private void CheckInput()
+ {
+ foreach(var task in ListOfTasks)
+ {
+ if (Matrix[task.PickUp.Row][task.PickUp.Column] != EndPointCode)
+ {
+ throw new ArgumentException("Invalid task pickup " + task.PickUp);
+ }
+ if (Matrix[task.Delivery.Row][task.Delivery.Column] != EndPointCode)
+ {
+ throw new ArgumentException("Invalid task deliver " + task.Delivery);
+ }
+
+ if (!ReleaseTimeAllowed && task.ReleaseTime != 0)
+ {
+ throw new ArgumentException("Releasetime task cannot be equal to zero " + task.PickUp + " " +
+ task.Delivery);
+ }
+ }
+ }
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..2f1bf911cc1f3bf5b117c7ad9eee104a42e5dd8a
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/ReadTasks.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 50ade04715a988d4d8eac455082416b2
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs
new file mode 100644
index 0000000000000000000000000000000000000000..d90ffb3b2b881c05189a8a5fcad82a1c92696ffa
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs
@@ -0,0 +1,76 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.ComponentModel;
+using Newtonsoft.Json;
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Class <c> Task </c> represents a task. A task consists of 2 positions: a pick up and a delivery position and a release time.
+ /// Default release time equals 0.
+ ///
+ /// contains instance method
+ /// - HasEndPoint(Position pos): checks whether pos is an endpoint (pick up or delivery) of the task
+ /// </summary>
+ public class Task
+ {
+ public Position PickUp { get; set; }
+ public Position Delivery { get; set; }
+
+ [DefaultValue(0)]
+ [JsonProperty(DefaultValueHandling = DefaultValueHandling.Populate)]
+ public int ReleaseTime { get; set; }
+
+ public Task(Position pickUp, Position delivery, int releaseTime)
+ {
+ PickUp = pickUp;
+ Delivery = delivery;
+ ReleaseTime = releaseTime;
+ }
+
+ // check whether a position is an endpoint of a task
+ public bool HasEndPoint(Position pos)
+ {
+ return pos.Equals(PickUp) || pos.Equals(Delivery);
+ }
+
+ protected bool Equals(Task other)
+ {
+ return Equals(PickUp, other.PickUp) && Equals(Delivery, other.Delivery) && ReleaseTime == other.ReleaseTime;
+ }
+
+ public override bool Equals(object obj)
+ {
+ if (ReferenceEquals(null, obj)) return false;
+ if (ReferenceEquals(this, obj)) return true;
+ if (obj.GetType() != this.GetType()) return false;
+ return Equals((Task) obj);
+ }
+
+ public override int GetHashCode()
+ {
+ unchecked
+ {
+ var hashCode = (PickUp != null ? PickUp.GetHashCode() : 0);
+ hashCode = (hashCode * 397) ^ (Delivery != null ? Delivery.GetHashCode() : 0);
+ hashCode = (hashCode * 397) ^ ReleaseTime;
+ return hashCode;
+ }
+ }
+
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..ad3b93b8ac7e92d8013bba97bad2b1cabf056426
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/Task.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 8bbe96fcd2953ed4b9c3fdee2b6ccc1d
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs
new file mode 100644
index 0000000000000000000000000000000000000000..06adb2fe93e9a97a63b90189f80e7113dc5579e4
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs
@@ -0,0 +1,40 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using UnityEngine;
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Static class <c> UtilityMethods </c> contains static helper methods.
+ /// </summary>
+
+ public static class UtilityMethods
+ {
+ /// <summary>
+ /// Returns complementary color.
+ /// </summary>
+ /// <param name="color"></param>
+ /// <returns>the complementary color</returns>
+ public static Color GetNegativeColor(Color color)
+ {
+ Color.RGBToHSV(color, out float H, out float S, out float V);
+ float negativeH = (H + 0.5f) % 1f;
+ return Color.HSVToRGB(negativeH, S, V);
+ }
+ }
+
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4daac6d5cd73cf85b3440f6134fd85e22d2bb0e2
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/UtilityMethods.cs.meta
@@ -0,0 +1,3 @@
+fileFormatVersion: 2
+guid: fc3d6a786841450292dc559c0be8cb2f
+timeCreated: 1628848095
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs
new file mode 100644
index 0000000000000000000000000000000000000000..135953c6d534a5af7872869b4ba5ccc97c4495b9
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs
@@ -0,0 +1,46 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+namespace Common.Warehouse
+{
+ /// <summary>
+ /// Class <c> WarehouseParameters </c> contains all parameters needed to set up the warehouse.
+ /// </summary>
+
+ public class WarehouseParameters
+ {
+ public int[][] Matrix { get; set; } // 1 is wall, 0 is tile, 2 is endpoint (pick up or delivery), 3 is parking
+
+ public int Rotation { get; set; } // unit of rotation
+ public int TimeRobotPerTile { get; set; } // time needed to traverse one tile
+
+ public int NumberOfRobots { get; set; }
+ public Node[] InitialNodes { get; set; }
+
+ public int NumberOfTasks { get; set; } // at all times there will be this number of tasks to be done
+
+ public bool CanGoBackWards { get; set; } // robot can go backwards
+
+ public bool RandomTasks { get; set; } // whether tasks are created random or taken from list
+
+ // codes for matrix entries
+ public readonly int CodeWall = 1;
+ public readonly int CodeTile = 0;
+ public readonly int CodeEndPoint = 3;
+ public readonly int CodeParking = 2;
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs.meta b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..4596e3b46bb250696629a500ee8d5dda622240d5
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Common/Warehouse/WarehouseParameters.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 810902285cb122d4cb8ceec8fa99aa9f
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Centralized.meta b/PickUpandDelivery/Assets/Scripts/Offline_Centralized.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7bbad8183aa6465f04c21a4f444efddab7c31ff6
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Centralized.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: 125388dc41b974b4f9620a18d4842943
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..7dddb615cbf73f83d3235ff66e61eb14cc5afc52
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs
@@ -0,0 +1,90 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+using Common.Robot;
+using Common.SimulationManager;
+using Common.Warehouse;
+using UnityEngine;
+
+/// <summary>
+/// Script <c> RobotOnlineDecentralized </c>
+/// has public instance methods:
+/// - GetMonoBehaviour(): return instance as Monobehaviour
+/// - AssignTask(Task task): assign a task to robot and update color of robot accordingly
+/// - PickUp(): after picking up, a cross is drawn on top of the robot using the complementary color of the task
+/// - Deliver(): calls RemoveTask() and returns delivered task
+/// has private instance methods:
+/// - OnTriggerEnter(Collider other) for collision detection
+/// </summary>
+public class RobotOfflineCentralized : MonoBehaviour, IRobot
+{
+ public ISimulationManager simulationManager { get; set; }
+
+ public Task assignedTask { get; set; } = null; // the task a robot is assigned, mainly used for coloring
+ [SerializeField] private Material idleColor = default; // color when no task assigned
+
+ void Start()
+ {
+ RobotMethods.InitializeRobotAtStart(this);
+ }
+
+ void FixedUpdate()
+ {
+ RobotMethods.UpdatePosition(this);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ // collision handling
+ private void OnTriggerEnter(Collider other)
+ {
+ RobotMethods.CollisionEvent(this);
+ }
+
+ /// <summary>
+ /// Assign task to robot.
+ /// </summary>
+ /// <param name="task"> a Task object</param>
+ public void AssignTask(Task task)
+ {
+ RobotMethods.AssignTask(this, task);
+ }
+
+ /// <summary>
+ /// Pick up.
+ /// </summary>
+ public void PickUp()
+ {
+ RobotMethods.PickUp(this);
+ }
+
+ /// <summary>
+ /// Delivery.
+ /// </summary>
+ /// <returns>the delivered task</returns>
+ public Task Deliver()
+ {
+ return RobotMethods.Deliver(this, idleColor.color);
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..504752fbabf7d90204f43f2609538c60f5de30aa
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/RobotOfflineCentralized.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 54b9a4088e4329849b24b0da23b5d39d
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Centralized/SimulationManagerOfflineCentralized.cs b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/SimulationManagerOfflineCentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..8e35665fabe9fb3b281bb4e53f5f5f33d2496b35
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/SimulationManagerOfflineCentralized.cs
@@ -0,0 +1,219 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.Collections.Generic;
+using System;
+using System.IO;
+using UnityEngine;
+using Unity.MLAgents.SideChannels;
+using Newtonsoft.Json;
+using System.Linq;
+using Common.Scripts;
+using Common.SideChannels;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Policies;
+using Unity.MLAgents.Sensors;
+using Random = System.Random;
+
+/// <summary>
+/// Class <c> SimulationManagerOfflineCentralized </c> controls the simulation for the Offline Centralized Pick Up and
+/// Delivery problem.
+///
+/// The only fields that should be altered here is warehouseConfigFile, the path to the txt file containing
+/// the configurable parameters of the simulation and the path to the preset tasks.
+///
+/// Has instance methods:
+/// - GetMonoBehaviour(): return object as monobehaviour
+/// - GetCurrentNode(int instanceID): get current node for a robot with given instance ID
+/// - RemoveTask(Task task): remove a tasks from current tasks
+/// - AddNewTask(): add a new task to current tasks
+/// - AssignTask(Task task, int instanceID): assign a task to a robot with given instance ID
+/// </summary>.
+
+
+public class SimulationManagerOfflineCentralized : Agent, ISimulationManager
+{
+ public string warehouseConfigFile { get; } = "ConfigurationFiles/Offline_Centralized/warehousejson.txt";
+ public string tasksListFile { get; }= "ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt";
+
+ // online or offline
+ public bool online { get; } = false;
+
+ private int counter; // Count time in ms (used in advance method), to make sure every timeRobotPerTile a new action or node is given
+
+ // colors
+ // colors for walls, tiles and prefabs
+ public Color colorWall { get; } = Color.black;
+ public Color colorTile { get; } = Color.white;
+ public Color colorEndPoint { get; }= new Color(0.5f, 0.5f, 1f); // blue-ish
+ public Color colorParking { get; } = Color.gray;
+
+ // colors for tasks and the robots that have been assigned these tasks
+ public HashSet<Color> assignedColors { get; set; } = new HashSet<Color>();
+ public HashSet<Color> notAssignedColors { get; set; } = new HashSet<Color>();
+
+ // endpoints
+ // dictionary containing all endpoints
+ // every endpoint will point to a gameobject, this will be setup by the Floor script
+ public Dictionary<Position, GameObject> endPoints { get; set; }= new Dictionary<Position, GameObject>();
+
+ // parameters of warehouse
+ public WarehouseParameters parameters { get; set; }
+
+ // tasks
+ // at every timestep there will be max numberOfTasks tasks that have to be assigned (by the python code), in
+ // the offline setting this has to be same as the number of preset tasks
+ // current available tasks and their assigned colors: these are the tasks to remain to be assigned to robots
+ public Dictionary<Task, Color> currentTasks { get; set; } = new Dictionary<Task, Color>();
+ // all remaining tasks, used to store tasks read from file
+ public Task[] remainingTasks { get; set; }
+
+ // robots
+ [SerializeField] private GameObject robotPrefab = default; // GameObject prefab used for the robot
+
+ // For every robot the current node where he is going, i.e. the next action
+ public Dictionary<int, Node> currentNodes { get; } = new Dictionary<int, Node>();
+
+ // For every robot the corresponding gameobject
+ public Dictionary<int, GameObject> robotGameObjects { get; set; } = new Dictionary<int, GameObject>();
+
+ // environment variables
+ // check for collisions
+ public bool collisions { get; set; }
+
+ // observations and actions
+ public int observationSize { get; }= 4; // id,x,y,rotation
+ public int actionSize { get; } = 6; // id,x,y,rotation,pick up, delivery
+
+ // environment variables for drawing
+ public float
+ SIZE { get; } = 1; // Size of square/tile, this is kept constant to avoid having to change camera perspective
+
+ public float SCALEROBOT { get; } = 0.49f; // Ratio robot to tile, for drawing robot
+ public float YROBOTFLOATING { get; }= 0.2f; // Floating y value robot, for drawing robot
+
+ // sidechannels
+ public EnvSideChannel envChannel { get; set; } // SideChannel for communication initial environment parameters
+ public TaskSideChannel taskChannel { get; set; } // SideChannel for communication on tasks
+
+ void Awake()
+ {
+ SimulationManagerMethods.SetUpWarehouse<RobotOfflineCentralized>(this, robotPrefab, true);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Every timeRobotPerTile a new decision is requested: observations are collected (<c>CollectObservations</c>) and actions are received
+ /// (<c>OnActionReceived</c>)
+ /// </summary>
+ public void FixedUpdate()
+ {
+ counter += 20; // FixedUpdate is called every 20ms, add 20ms to counter
+ // if counter has passed timeRobotPerTile and there is a python client listening new actions are requested
+ // if a collisions has been detected no new requests are made
+ if (counter >= parameters.TimeRobotPerTile && Academy.Instance.IsCommunicatorOn)
+ {
+ if (!collisions)
+ {
+ RequestDecision(); // manually request a decision
+ }
+
+ counter = 0; // reset counter
+ }
+ }
+
+ /// <summary>
+ /// Collect observations, will be executed when requested.
+ /// Note that the number of observations per robot should be equal to <c>ObservationSize</c>.
+ /// </summary>
+ /// <param name="sensor"></param>
+ public override void CollectObservations(VectorSensor sensor)
+ {
+ SimulationManagerMethods.CollectObservations(this, sensor);
+ }
+
+ /// <summary>
+ /// Receive actions, will be executed when requested.
+ /// Note that the number of actions per robot should be equal to <c>ActionSize</c>.
+ /// </summary>
+ public override void OnActionReceived(ActionBuffers actionBuffers)
+ {
+ SimulationManagerMethods.OnActionReceived<RobotOfflineCentralized>(this,actionBuffers);
+ }
+
+ /// <summary>
+ /// Show message when a collision has occured.
+ /// </summary>
+ private void OnGUI()
+ {
+ if (collisions)
+ {
+ SimulationManagerMethods.ShowCollisionMessage(this);
+ }
+ }
+
+ /// <summary>
+ /// Returns current node, i.e. the next node to go to, for a robot with a given instance ID.
+ /// </summary>
+ /// <param name="instanceID"> an int</param>
+ /// <returns> A node corresponding to the current node.</returns>
+ public Node GetCurrentNode(int instanceID)
+ {
+ return SimulationManagerMethods.GetCurrentNode(this,instanceID);
+ }
+
+ /// <summary>
+ /// Remove a task from CurrentTasks. Reset color of pick up and delivery endpoints.
+ /// Make color available.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <exception cref="Exception">thrown if it was not assigned to a robot</exception>
+ public void RemoveTask(Task task)
+ {
+ SimulationManagerMethods.RemoveTask(this, task);
+ }
+
+ /// <summary>
+ /// Add a task to currentTasks if there are still tasks available. Assign a color and send information to python code.
+ /// </summary>
+ /// <exception cref="Exception">thrown when currentTasks has already reached capacity</exception>
+ public void AddNewTask()
+ {
+ SimulationManagerMethods.AddNewTask(this);
+ }
+
+ /// <summary>
+ /// Assign a task to a robot with a given instanceID.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <param name="instanceID"></param>
+ public void AssignTask(Task task, int instanceID)
+ {
+ SimulationManagerMethods.AssignTask<RobotOfflineCentralized>(this, task, instanceID);
+ }
+}
+
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Centralized/SimulationManagerOfflineCentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Offline_Centralized/SimulationManagerOfflineCentralized.cs.meta
new file mode 100644
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@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: ec6a5fb578df5504186ba78f15f70a56
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Decentralized.meta b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9eb4f284b983de8ee07f3f68b610a085b5768b39
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+++ b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: c819bbdde6c1a9143a5866dbd9669aa0
+folderAsset: yes
+DefaultImporter:
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+ userData:
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diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..fe2a58e17db8ddab215ed704284098f418058054
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs
@@ -0,0 +1,135 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+using System;
+using System.Linq;
+using Common.Robot;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+/// <summary>
+/// Script <c> RobotOfflineDecentralized </c>
+/// has public instance methods:
+/// - GetMonoBehaviour(): return instance as Monobehaviour
+/// - AssignTask(Task task): assign a task to robot and update color of robot accordingly
+/// - CollectObservations(VectorSensor sensor): collect an observation
+/// - OnActionReceived(ActionBuffers actionBuffers): do action
+/// - Pickup() and Delivery(): unused methods from interface IRobot
+/// has private instance methods:
+/// - OnTriggerEnter(Collider other) for collision detection
+/// </summary>
+public class RobotOfflineDecentralized : Agent, IRobot
+{
+ public ISimulationManager simulationManager { get; set; }
+
+ private int
+ counter; // Count time in ms (used in advance method), to make sure every timeRobotPerTile a new action or node is given
+
+ public Task assignedTask { get; set; } = null; // the task a robot is assigned, mainly used for coloring
+ [SerializeField] private Material idleColor = default; // color when no task assigned
+
+ void Start()
+ {
+ RobotMethods.InitializeRobotAtStart(this);
+ }
+
+
+ /// <summary>
+ /// Every timeRobotPerTile a new decision is requested: observations are collected (<c>CollectObservations</c>) and actions are received
+ /// (<c>OnActionReceived</c>).
+ /// </summary>
+ void FixedUpdate()
+ {
+ counter += 20; // FixedUpdate is called every 20ms, add 20ms to counter
+ // if counter has passed timeRobotPerTile and there is a python client listening new actions are requested
+ // if a collisions has been detected no new requests are made
+ if (counter >= simulationManager.parameters.TimeRobotPerTile && Academy.Instance.IsCommunicatorOn)
+ {
+ if (!simulationManager.collisions)
+ {
+ Debug.Log("request by " + gameObject.GetInstanceID());
+ RequestDecision(); // manually request a decision
+ }
+
+ counter = 0; // reset counter
+ }
+ RobotMethods.UpdatePosition(this);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Collect observation, will be executed when requested.
+ /// </summary>
+ /// <param name="sensor"></param>
+ public override void CollectObservations(VectorSensor sensor)
+ {
+ RobotMethods.CollectObservations(this,sensor);
+ }
+
+ /// <summary>
+ /// Do action when received, will be executed when requested.
+ /// </summary>
+ /// <param name="actionBuffers"></param>
+ public override void OnActionReceived(ActionBuffers actionBuffers)
+ {
+ RobotMethods.OnActionReceived(this, actionBuffers, idleColor.color);
+ }
+
+ // collision handling
+ private void OnTriggerEnter(Collider other)
+ {
+ RobotMethods.CollisionEvent(this);
+ }
+
+ /// <summary>
+ /// Assign task to robot.
+ /// </summary>
+ /// <param name="task"> a Task object</param>
+ public void AssignTask(Task task)
+ {
+ RobotMethods.AssignTask(this, task);
+ }
+
+ /// <summary>
+ /// Pick up.
+ /// </summary>
+ public void PickUp()
+ {
+ RobotMethods.PickUp(this);
+ }
+
+ /// <summary>
+ /// Delivery.
+ /// </summary>
+ /// <returns>the delivered task</returns>
+ public Task Deliver()
+ {
+ return RobotMethods.Deliver(this, idleColor.color);
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs.meta
new file mode 100644
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+++ b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/RobotOfflineDecentralized.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: e224beeb8f8781f479c65279d3e07a01
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..c6f0d22e7530e21a350006dfa2548fddb2bb281e
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs
@@ -0,0 +1,173 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.Collections.Generic;
+using System;
+using System.IO;
+using UnityEngine;
+using Unity.MLAgents.SideChannels;
+using Newtonsoft.Json;
+using System.Linq;
+using Common.Scripts;
+using Common.SideChannels;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Random = System.Random;
+
+/// <summary>
+/// Class <c> SimulationManagerOfflineDecentralized </c> controls the simulation for the Offline Decentralized Pick Up and
+/// Delivery problem.
+///
+/// The only fields that should be altered here is warehouseConfigFile, the path to the txt file containing
+/// the configurable parameters of the simulation and the path to the preset tasks.
+///
+/// Has instance methods:
+/// - GetMonoBehaviour(): return object as monobehavior
+/// - GetCurrentNode(int instanceID): get current node for a robot with given instance ID
+/// - RemoveTask(Task task): remove a tasks from current tasks
+/// - AddNewTask(): add a new task to current tasks
+/// - AssignTask(Task task, int instanceID): assign a task to a robot with given instance ID
+/// </summary>
+
+
+public class SimulationManagerOfflineDecentralized : MonoBehaviour, ISimulationManager
+{
+ public string warehouseConfigFile { get; }= "ConfigurationFiles/Offline_Decentralized/warehousejson.txt";
+ public string tasksListFile { get; }= "ConfigurationFiles/Offline_Decentralized/tasks_releasetime.txt";
+
+ // online or offline
+ public bool online { get; } = false;
+
+ // colors
+ // colors for walls, tiles and prefabs
+ public Color colorWall { get; } = Color.black;
+ public Color colorTile { get; } = Color.white;
+ public Color colorEndPoint { get; }= new Color(0.5f, 0.5f, 1f); // blue-ish
+ public Color colorParking { get; } = Color.gray;
+
+ // colors for tasks and the robots that have been assigned these tasks
+ public HashSet<Color> assignedColors { get; set; } = new HashSet<Color>();
+ public HashSet<Color> notAssignedColors { get; set; } = new HashSet<Color>();
+
+ // endpoints
+ // dictionary containing all endpoints
+ // every endpoint will point to a gameobject, this will be setup by the Floor script
+ public Dictionary<Position, GameObject> endPoints { get; set; }= new Dictionary<Position, GameObject>();
+
+ // parameters of warehouse
+ public WarehouseParameters parameters { get; set; }
+
+ // tasks
+ // at every timestep there will be max numberOfTasks tasks that have to be assigned (by the python code), in
+ // the offline setting this has to be same as the number of preset tasks
+ // current available tasks and their assigned colors: these are the tasks to remain to be assigned to robots
+ public Dictionary<Task, Color> currentTasks { get; set; } = new Dictionary<Task, Color>();
+ // all remaining tasks, used to store tasks read from file
+ public Task[] remainingTasks { get; set; }
+
+ // robots
+ [SerializeField] private GameObject robotPrefab = default; // GameObject prefab used for the robot
+
+ // For every robot the current node where he is going, i.e. the next action
+ public Dictionary<int, Node> currentNodes { get; } = new Dictionary<int, Node>();
+
+ // For every robot the corresponding gameobject
+ public Dictionary<int, GameObject> robotGameObjects { get; set; } = new Dictionary<int, GameObject>();
+
+ // environment variables
+ // check for collisions
+ public bool collisions { get; set; }
+
+ // observations and actions
+ public int observationSize { get; }= 4; //id,x,y,rotation
+ public int actionSize { get; } = 5; //x,y,rotation,pick up, delivery
+
+ // environment variables for drawing
+ public float
+ SIZE { get; } = 1; // Size of square/tile, this is kept constant to avoid having to change camera perspective
+
+ public float SCALEROBOT { get; } = 0.49f; // Ratio robot to tile, for drawing robot
+ public float YROBOTFLOATING { get; }= 0.2f; // Floating y value robot, for drawing robot
+
+ // sidechannels
+ public EnvSideChannel envChannel { get; set; } // SideChannel for communication initial environment parameters
+ public TaskSideChannel taskChannel { get; set; } // SideChannel for communication on tasks
+
+ void Awake()
+ {
+ SimulationManagerMethods.SetUpWarehouse<RobotOfflineDecentralized>(this, robotPrefab, false);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Show message when a collision has occured.
+ /// </summary>
+ public void OnGUI()
+ {
+ if (collisions)
+ {
+ SimulationManagerMethods.ShowCollisionMessage(this);
+ }
+ }
+
+ /// <summary>
+ /// Returns current node, i.e. the next node to go to, for a robot with a given instance ID.
+ /// </summary>
+ /// <param name="instanceID"> an int</param>
+ /// <returns> A node corresponding to the current node.</returns>
+ public Node GetCurrentNode(int instanceID)
+ {
+ return SimulationManagerMethods.GetCurrentNode(this,instanceID);
+ }
+
+ /// <summary>
+ /// Remove a task from CurrentTasks that was assigned to a robot.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <exception cref="Exception">thrown if it was not assigned to a robot</exception>
+ public void RemoveTask(Task task)
+ {
+ SimulationManagerMethods.RemoveTask(this, task);
+ }
+
+ /// <summary>
+ /// Add a task to currentTasks if there are still tasks available. Assign a color and send information to python code.
+ /// </summary>
+ /// <exception cref="Exception">thrown when currentTasks has already reached capacity</exception>
+ public void AddNewTask()
+ {
+ SimulationManagerMethods.AddNewTask(this);
+ }
+
+ /// <summary>
+ /// Assign a task to a robot with a given instanceID.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <param name="instanceID"></param>
+ public void AssignTask(Task task, int instanceID)
+ {
+ SimulationManagerMethods.AssignTask<RobotOfflineDecentralized>(this, task, instanceID);
+ }
+}
+
diff --git a/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..9b690ed9a0c7b40954dc07699918f57f0881d7f9
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Offline_Decentralized/SimulationManagerOfflineDecentralized.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/PickUpandDelivery/Assets/Scripts/Online_Centralized.meta b/PickUpandDelivery/Assets/Scripts/Online_Centralized.meta
new file mode 100644
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diff --git a/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs b/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..2803a4333c420edb2c16900c9b1d8c730d1c1c13
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs
@@ -0,0 +1,90 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using Common.Robot;
+using Common.SimulationManager;
+using Common.Warehouse;
+using UnityEngine;
+
+/// <summary>
+/// Script <c> RobotOnlineCentralized </c>
+/// has public instance methods:
+/// - GetMonoBehaviour(): return instance as Monobehaviour
+/// - AssignTask(Task task): assign a task to robot and update color of robot accordingly
+/// - PickUp(): after picking up, a cross is drawn on top of the robot using the complementary color of the task
+/// - Deliver(): calls RemoveTask() and returns delivered task
+/// has private instance methods:
+/// - OnTriggerEnter(Collider other) for collision detection
+/// </summary>
+
+public class RobotOnlineCentralized : MonoBehaviour, IRobot
+{
+ public ISimulationManager simulationManager { get; set; }
+
+ public Task assignedTask { get; set; } = null; // the task a robot is assigned, mainly used for coloring
+ [SerializeField] private Material idleColor = default; // color when no task assigned
+
+ void Start()
+ {
+ RobotMethods.InitializeRobotAtStart(this);
+ }
+
+ void FixedUpdate()
+ {
+ RobotMethods.UpdatePosition(this);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ // collision handling
+ private void OnTriggerEnter(Collider other)
+ {
+ RobotMethods.CollisionEvent(this);
+ }
+
+ /// <summary>
+ /// Assign task to robot.
+ /// </summary>
+ /// <param name="task"> a Task object</param>
+ public void AssignTask(Task task)
+ {
+ RobotMethods.AssignTask(this, task);
+ }
+
+ /// <summary>
+ /// Pick up.
+ /// </summary>
+ public void PickUp()
+ {
+ RobotMethods.PickUp(this);
+ }
+
+ /// <summary>
+ /// Delivery.
+ /// </summary>
+ /// <returns>the delivered task</returns>
+ public Task Deliver()
+ {
+ return RobotMethods.Deliver(this, idleColor.color);
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs.meta
new file mode 100644
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+++ b/PickUpandDelivery/Assets/Scripts/Online_Centralized/RobotOnlineCentralized.cs.meta
@@ -0,0 +1,11 @@
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diff --git a/PickUpandDelivery/Assets/Scripts/Online_Centralized/SimulationManagerOnlineCentralized.cs b/PickUpandDelivery/Assets/Scripts/Online_Centralized/SimulationManagerOnlineCentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9cb299d077f184010af2d1195a4a79c9624d5ec9
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Online_Centralized/SimulationManagerOnlineCentralized.cs
@@ -0,0 +1,211 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.Collections.Generic;
+using System;
+using UnityEngine;
+using Common.SideChannels;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+
+/// <summary>
+/// Class <c> SimulationManagerOnlineCentralized </c> controls the simulation for the Online Centralized Pick Up and
+/// Delivery problem.
+///
+/// The only field that should be altered here is warehouseConfigFile, the path to the txt file containing
+/// the configurable parameters of the simulation. Optionally a preset set of tasks.
+///
+/// Has instance methods:
+/// - GetMonoBehaviour(): return object as monobehaviour
+/// - GetCurrentNode(int instanceID): get current node for a robot with given instance ID
+/// - RemoveTask(Task task): remove a tasks from current tasks
+/// - AddNewTask(): add a new task to current tasks
+/// - AssignTask(Task task, int instanceID): assign a task to a robot with given instance ID
+/// </summary>
+
+
+public class SimulationManagerOnlineCentralized : Agent, ISimulationManager
+{
+ //public string warehouseConfigFile {get;} = "ConfigurationFiles/Online_Centralized/warehousejson_random.txt";
+ public string warehouseConfigFile { get; } = "ConfigurationFiles/Online_Centralized/warehousejson_not_random.txt";
+ public string tasksListFile { get; }= "ConfigurationFiles/Online_Centralized/tasks.txt";
+
+ // online or offline
+ public bool online { get; } = true;
+
+ private int counter; // Count time in ms (used in advance method), to make sure every timeRobotPerTile a new action or node is given
+
+ // colors
+ // colors for walls, tiles and prefabs
+ public Color colorWall { get; } = Color.black;
+ public Color colorTile { get; } = Color.white;
+ public Color colorEndPoint { get; }= new Color(0.5f, 0.5f, 1f); // blue-ish
+ public Color colorParking { get; } = Color.gray;
+
+ // colors for tasks and the robots that have been assigned these tasks
+ public HashSet<Color> assignedColors { get; set; } = new HashSet<Color>();
+ public HashSet<Color> notAssignedColors { get; set; } = new HashSet<Color>();
+
+ // endpoints
+ // dictionary containing all endpoints
+ // every endpoint will point to a gameobject, this will be setup by the Floor script
+ public Dictionary<Position, GameObject> endPoints { get; set; }= new Dictionary<Position, GameObject>();
+
+ // parameters of warehouse
+ public WarehouseParameters parameters { get; set; }
+
+ // tasks
+ // at every timestep there will be max numberOfTasks tasks that have to be assigned (by the python code)
+ // current available tasks and their assigned colors: these are the tasks to remain to be assigned to robots
+ public Dictionary<Task, Color> currentTasks { get; set; } = new Dictionary<Task, Color>();
+ // all remaining tasks, not yet in currentTasks, if not generated randomly and taken from predefined list
+ public Task[] remainingTasks { get; set; }
+
+ // robots
+ [SerializeField] private GameObject robotPrefab = default; // GameObject prefab used for the robot
+
+ // For every robot the current node where he is going, i.e. the next action
+ public Dictionary<int, Node> currentNodes { get; } = new Dictionary<int, Node>();
+
+ // For every robot the corresponding gameobject
+ public Dictionary<int, GameObject> robotGameObjects { get; set; }= new Dictionary<int, GameObject>();
+
+ // environment variables
+ // check for collisions
+ public bool collisions { get; set; }
+
+ // observations and actions
+ public int observationSize { get; }= 4; // id,x,y,rotation
+ public int actionSize { get; }= 6; // id,x,y,rotation,pick up, delivery
+
+ // environment variables for drawing
+ public float
+ SIZE { get; } = 1; // Size of square/tile, this is kept constant to avoid having to change camera perspective
+
+ public float SCALEROBOT { get; } = 0.49f; // Ratio robot to tile, for drawing robot
+ public float YROBOTFLOATING { get; }= 0.2f; // Floating y value robot, for drawing robot
+
+ // sidechannels
+ public EnvSideChannel envChannel { get; set; } // SideChannel for communication initial environment parameters
+ public TaskSideChannel taskChannel { get; set; } // SideChannel for communication on tasks
+
+ void Awake()
+ {
+ SimulationManagerMethods.SetUpWarehouse<RobotOnlineCentralized>(this, robotPrefab, true);
+ }
+
+ /// <summary>
+ /// Every timeRobotPerTile a new decision is requested: observations are collected (<c>CollectObservations</c>) and actions are received
+ /// (<c>OnActionReceived</c>)
+ /// </summary>
+ public void FixedUpdate()
+ {
+ counter += 20; // FixedUpdate is called every 20ms, add 20ms to counter
+ // if counter has passed timeRobotPerTile and there is a python client listening new actions are requested
+ // if a collisions has been detected no new requests are made
+ if (counter >= parameters.TimeRobotPerTile && Academy.Instance.IsCommunicatorOn)
+ {
+ if (!collisions)
+ {
+ RequestDecision(); // manually request a decision
+ }
+
+ counter = 0; // reset counter
+ }
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Collect observations, will be executed when requested.
+ /// </summary>
+ /// <param name="sensor"></param>
+ public override void CollectObservations(VectorSensor sensor)
+ {
+ SimulationManagerMethods.CollectObservations(this, sensor);
+ }
+
+ /// <summary>
+ /// Receive actions, will be executed when requested.
+ /// Note that the number of actions per robot should be equal to <c>ActionSize</c>.
+ /// </summary>
+ public override void OnActionReceived(ActionBuffers actionBuffers)
+ {
+ SimulationManagerMethods.OnActionReceived<RobotOnlineCentralized>(this,actionBuffers);
+ }
+
+ /// <summary>
+ /// Show message when a collision has occured.
+ /// </summary>
+ private void OnGUI()
+ {
+ if (collisions)
+ {
+ SimulationManagerMethods.ShowCollisionMessage(this);
+ }
+ }
+
+ /// <summary>
+ /// Returns current node, i.e. the next node to go to, for a robot with a given instance ID.
+ /// </summary>
+ /// <param name="instanceID"> an int</param>
+ /// <returns> A node corresponding to the current node.</returns>
+ public Node GetCurrentNode(int instanceID)
+ {
+ return SimulationManagerMethods.GetCurrentNode(this,instanceID);
+ }
+
+ /// <summary>
+ /// Remove a task from CurrentTasks. Reset color of pick up and delivery endpoints.
+ /// Make color available.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <exception cref="Exception">thrown if it was not assigned to a robot</exception>
+ public void RemoveTask(Task task)
+ {
+ SimulationManagerMethods.RemoveTask(this, task);
+ }
+
+ /// <summary>
+ /// Add a task to currentTasks if there are still tasks available. Assign a color and send information to python code.
+ /// </summary>
+ /// <exception cref="Exception">thrown when currentTasks has already reached capacity</exception>
+ public void AddNewTask()
+ {
+ SimulationManagerMethods.AddNewTask(this);
+ }
+
+ /// <summary>
+ /// Assign a task to a robot with a given instanceID.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <param name="instanceID"></param>
+ public void AssignTask(Task task, int instanceID)
+ {
+ SimulationManagerMethods.AssignTask<RobotOnlineCentralized>(this, task, instanceID);
+ }
+}
+
diff --git a/PickUpandDelivery/Assets/Scripts/Online_Centralized/SimulationManagerOnlineCentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Online_Centralized/SimulationManagerOnlineCentralized.cs.meta
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diff --git a/PickUpandDelivery/Assets/Scripts/Online_Decentralized/RobotOnlineDecentralized.cs b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/RobotOnlineDecentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..9b866a5270c0553dbd9129fad783340836197b78
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/RobotOnlineDecentralized.cs
@@ -0,0 +1,129 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+
+using Common.Robot;
+using Common.SimulationManager;
+using Common.Warehouse;
+using Unity.MLAgents;
+using Unity.MLAgents.Actuators;
+using Unity.MLAgents.Sensors;
+using UnityEngine;
+
+/// <summary>
+/// Script <c> RobotOnlineDecentralized </c>
+/// has public instance methods:
+/// - GetMonoBehaviour(): return instance as Monobehaviour
+/// - AssignTask(Task task): assign a task to robot and update color of robot accordingly
+/// - CollectObservations(VectorSensor sensor): collect an observation
+/// - OnActionReceived(ActionBuffers actionBuffers): do action
+/// - Pickup() and Delivery(): unused methods from interface IRobot
+/// has private instance methods:
+/// - OnTriggerEnter(Collider other) for collision detection
+/// </summary>
+public class RobotOnlineDecentralized : Agent, IRobot
+{
+ public ISimulationManager simulationManager { get; set; }
+
+ private int
+ counter; // Count time in ms (used in advance method), to make sure every timeRobotPerTile a new action or node is given
+
+ public Task assignedTask { get; set; }= null; // the task a robot is assigned, mainly used for coloring
+ [SerializeField] private Material idleColor = default; // color when no task assigned
+
+ void Start()
+ {
+ RobotMethods.InitializeRobotAtStart(this);
+ }
+
+ void FixedUpdate()
+ {
+ counter += 20; // FixedUpdate is called every 20ms, add 20ms to counter
+ // if counter has passed timeRobotPerTile and there is a python client listening new actions are requested
+ // if a collisions has been detected no new requests are made
+ if (counter >= simulationManager.parameters.TimeRobotPerTile && Academy.Instance.IsCommunicatorOn)
+ {
+ if (!simulationManager.collisions)
+ {
+ Debug.Log("request by " + gameObject.GetInstanceID());
+ RequestDecision(); // manually request a decision
+ }
+
+ counter = 0; // reset counter
+ }
+
+ RobotMethods.UpdatePosition(this);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Collect observation, will be executed when requested.
+ /// </summary>
+ /// <param name="sensor"></param>
+ public override void CollectObservations(VectorSensor sensor)
+ {
+ RobotMethods.CollectObservations(this,sensor);
+ }
+
+ /// <summary>
+ /// Do action when received, will be executed when requested.
+ /// </summary>
+ /// <param name="actionBuffers"></param>
+ public override void OnActionReceived(ActionBuffers actionBuffers)
+ {
+ RobotMethods.OnActionReceived(this, actionBuffers, idleColor.color);
+ }
+
+ // collision handling
+ private void OnTriggerEnter(Collider other)
+ {
+ RobotMethods.CollisionEvent(this);
+ }
+
+ /// <summary>
+ /// Assign task to robot.
+ /// </summary>
+ /// <param name="task"> a Task object</param>
+ public void AssignTask(Task task)
+ {
+ RobotMethods.AssignTask(this, task);
+ }
+
+ /// <summary>
+ /// Pick up.
+ /// </summary>
+ public void PickUp()
+ {
+ RobotMethods.PickUp(this);
+ }
+
+ /// <summary>
+ /// Delivery.
+ /// </summary>
+ /// <returns>the delivered task</returns>
+ public Task Deliver()
+ {
+ return RobotMethods.Deliver(this, idleColor.color);
+ }
+}
diff --git a/PickUpandDelivery/Assets/Scripts/Online_Decentralized/RobotOnlineDecentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/RobotOnlineDecentralized.cs.meta
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diff --git a/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs
new file mode 100644
index 0000000000000000000000000000000000000000..eb396aba5055ea2c556f8dd00f6c1ec8d40aed04
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs
@@ -0,0 +1,167 @@
+/*
+ Copyright (c) 2021. AI Lab, Vrije Universiteit Brussel.
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+along with this program. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+using System.Collections.Generic;
+using System;
+using UnityEngine;
+using Common.SideChannels;
+using Common.SimulationManager;
+using Common.Warehouse;
+
+/// <summary>
+/// Class <c> SimulationManagerOnlineDecentralized </c> controls the simulation for the Online Decentralized Pick Up and
+/// Delivery problem.
+///
+/// The only field that should be altered here is warehouseConfigFile, the path to the txt file containing
+/// the configurable parameters of the simulation. Optionally a preset set of tasks.
+///
+/// Has instance methods:
+/// - GetMonoBehaviour(): return object as monobehavior
+/// - GetCurrentNode(int instanceID): get current node for a robot with given instance ID
+/// - RemoveTask(Task task): remove a tasks from current tasks
+/// - AddNewTask(): add a new task to current tasks
+/// - AssignTask(Task task, int instanceID): assign a task to a robot with given instance ID
+/// </summary>
+
+
+public class SimulationManagerOnlineDecentralized : MonoBehaviour, ISimulationManager
+{
+ //public string warehouseConfigFile { get; } = "ConfigurationFiles/Online_Decentralized/warehousejson_not_random.txt";
+ public string warehouseConfigFile {get;} = "ConfigurationFiles/Online_Centralized/warehousejson_random.txt";
+ public string tasksListFile { get; } = "ConfigurationFiles/Online_Decentralized/tasks.txt";
+
+ // online or offline
+ public bool online { get; } = true;
+
+ // colors
+ // colors for walls, tiles and prefabs
+ public Color colorWall { get; } = Color.black;
+ public Color colorTile { get; } = Color.white;
+ public Color colorEndPoint { get; }= new Color(0.5f, 0.5f, 1f); // blue-ish
+ public Color colorParking { get; } = Color.gray;
+
+ // colors for tasks and the robots that have been assigned these tasks
+ public HashSet<Color> assignedColors { get; set; }
+ public HashSet<Color> notAssignedColors { get; set; }
+
+ // endpoints
+ // dictionary containing all endpoints
+ // every endpoint will point to a gameobject, this will be setup by the Floor script
+ public Dictionary<Position, GameObject> endPoints { get; set; }
+
+ // parameters of warehouse
+ public WarehouseParameters parameters { get; set; }
+
+ // tasks
+ // at every timestep there will be max numberOfTasks tasks that have to be assigned (by the python code)
+ // current available tasks and their assigned colors: these are the tasks to remain to be assigned to robots
+ public Dictionary<Task, Color> currentTasks { get; set; }
+ // all remaining tasks, not yet in currentTasks, if not generated randomly and taken from predefined list
+ public Task[] remainingTasks { get; set; }
+
+ // robots
+ [SerializeField] private GameObject robotPrefab = default; // GameObject prefab used for the robot
+
+ // For every robot the current node where he is going, i.e. the next action
+ public Dictionary<int, Node> currentNodes { get; } = new Dictionary<int, Node>();
+
+ // For every robot the corresponding gameobject
+ public Dictionary<int, GameObject> robotGameObjects { get; set; }= new Dictionary<int, GameObject>();
+
+ // environment variables
+ // check for collisions
+ public bool collisions { get; set; }
+
+ // observations and actions
+ public int observationSize { get; } = 4; //id,x,y,rotation
+ public int actionSize { get; } = 5; //x,y,rotation,pick up, delivery
+
+ // environment variables for drawing
+ public float
+ SIZE { get; } = 1; // Size of square/tile, this is kept constant to avoid having to change camera perspective
+
+ public float SCALEROBOT { get; } = 0.49f; // Ratio robot to tile, for drawing robot
+ public float YROBOTFLOATING { get; }= 0.2f; // Floating y value robot, for drawing robot
+
+ // sidechannels
+ public EnvSideChannel envChannel { get; set; } // SideChannel for communication initial environment parameters
+ public TaskSideChannel taskChannel { get; set; } // SideChannel for communication on tasks
+
+ void Awake()
+ {
+ SimulationManagerMethods.SetUpWarehouse<RobotOnlineDecentralized>(this, robotPrefab, false);
+ }
+
+ /// <summary>
+ /// Return this as MonoBehaviour.
+ /// </summary>
+ /// <returns></returns>
+ public MonoBehaviour GetMonoBehaviour()
+ {
+ return this;
+ }
+
+ /// <summary>
+ /// Show message when a collision has occured.
+ /// </summary>
+ public void OnGUI()
+ {
+ if (collisions)
+ {
+ SimulationManagerMethods.ShowCollisionMessage(this);
+ }
+ }
+
+ /// <summary>
+ /// Returns current node, i.e. the next node to go to, for a robot with a given instance ID.
+ /// </summary>
+ /// <param name="instanceID"> an int</param>
+ /// <returns> A node corresponding to the current node.</returns>
+ public Node GetCurrentNode(int instanceID)
+ {
+ return SimulationManagerMethods.GetCurrentNode(this,instanceID);
+ }
+
+ /// <summary>
+ /// Remove a task from CurrentTasks that was assigned to a robot.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <exception cref="Exception">thrown if it was not assigned to a robot</exception>
+ public void RemoveTask(Task task)
+ {
+ SimulationManagerMethods.RemoveTask(this, task);
+ }
+
+ /// <summary>
+ /// Add a task to currentTasks if there are still tasks available. Assign a color and send information to python code.
+ /// </summary>
+ /// <exception cref="Exception">thrown when currentTasks has already reached capacity</exception>
+ public void AddNewTask()
+ {
+ SimulationManagerMethods.AddNewTask(this);
+ }
+
+ /// <summary>
+ /// Assign a task to a robot with a given instanceID.
+ /// </summary>
+ /// <param name="task"></param>
+ /// <param name="instanceID"></param>
+ public void AssignTask(Task task, int instanceID)
+ {
+ SimulationManagerMethods.AssignTask<RobotOnlineDecentralized>(this, task, instanceID);
+ }
+}
+
diff --git a/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs.meta b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs.meta
new file mode 100644
index 0000000000000000000000000000000000000000..7e69fb08dd730ea41b154b9fe5213a4e28948a23
--- /dev/null
+++ b/PickUpandDelivery/Assets/Scripts/Online_Decentralized/SimulationManagerOnlineDecentralized.cs.meta
@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: a6033bfb5a3a1f747b8a528a445711a6
+MonoImporter:
+ externalObjects: {}
+ serializedVersion: 2
+ defaultReferences: []
+ executionOrder: 0
+ icon: {instanceID: 0}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Shaders.meta b/PickUpandDelivery/Assets/Shaders.meta
new file mode 100644
index 0000000000000000000000000000000000000000..58b6c1ad82bc6cf9bb3caeb3afcaf71c00f3d8de
--- /dev/null
+++ b/PickUpandDelivery/Assets/Shaders.meta
@@ -0,0 +1,8 @@
+fileFormatVersion: 2
+guid: c5ae9f1e9e172db45b8fcd2ac5c729b3
+folderAsset: yes
+DefaultImporter:
+ externalObjects: {}
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/Assets/Shaders/TextShader.shader b/PickUpandDelivery/Assets/Shaders/TextShader.shader
new file mode 100644
index 0000000000000000000000000000000000000000..4f433e4bda5c6c2d7ec7ae61ba06f6bff7e0b456
--- /dev/null
+++ b/PickUpandDelivery/Assets/Shaders/TextShader.shader
@@ -0,0 +1,18 @@
+Shader "GUI/3D Text Shader - Cull Back" {
+ Properties {
+ _MainTex ("Font Texture", 2D) = "white" {}
+ _Color ("Text Color", Color) = (1,1,1,1)
+ }
+
+ SubShader {
+ Tags { "Queue"="Transparent" "IgnoreProjector"="True" "RenderType"="Transparent" }
+ Lighting Off Cull Back ZWrite Off Fog { Mode Off }
+ Blend SrcAlpha OneMinusSrcAlpha
+ Pass {
+ Color [_Color]
+ SetTexture [_MainTex] {
+ combine primary, texture * primary
+ }
+ }
+ }
+}
\ No newline at end of file
diff --git a/PickUpandDelivery/Assets/Shaders/TextShader.shader.meta b/PickUpandDelivery/Assets/Shaders/TextShader.shader.meta
new file mode 100644
index 0000000000000000000000000000000000000000..dcd31128c12e19e8cb61854841daf86ab4c86e42
--- /dev/null
+++ b/PickUpandDelivery/Assets/Shaders/TextShader.shader.meta
@@ -0,0 +1,9 @@
+fileFormatVersion: 2
+guid: 3b25d06e715175541a7601ed730df7c6
+ShaderImporter:
+ externalObjects: {}
+ defaultTextures: []
+ nonModifiableTextures: []
+ userData:
+ assetBundleName:
+ assetBundleVariant:
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f62b76ad2e3d4b861325ef0b7eb42389ca6e419
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks.txt
@@ -0,0 +1,52 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ }
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81ed04c0bf18e033fe7179b97919ff63f696d922
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/tasks_releasetime.txt
@@ -0,0 +1,57 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ },
+ "ReleaseTime": 5
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ },
+ "ReleaseTime": 9
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ },
+ "ReleaseTime": 10
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ },
+ "ReleaseTime": 2
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ },
+ "ReleaseTime": 2
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6c208ca746adf0f3fed6cefc87bc3e5a9b104892
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Centralized/warehousejson.txt
@@ -0,0 +1,26 @@
+{
+ "Matrix": [
+ [2,2,2,2,2,2,2,2,2,2],
+ [2,2,2,2,2,2,2,2,2,2],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
+ "Rotation": 45,
+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
+ {"GridPosition":{"Row":0,"Column":0},"Degrees":0},
+ {"GridPosition":{"Row":1,"Column":4},"Degrees":270},
+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
+ "NumberOfTasks": 5,
+ "CanGoBackWards": false,
+ "RandomTasks": false
+}
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f62b76ad2e3d4b861325ef0b7eb42389ca6e419
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks.txt
@@ -0,0 +1,52 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ }
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks_releasetime.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks_releasetime.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81ed04c0bf18e033fe7179b97919ff63f696d922
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/tasks_releasetime.txt
@@ -0,0 +1,57 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ },
+ "ReleaseTime": 5
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ },
+ "ReleaseTime": 9
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ },
+ "ReleaseTime": 10
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ },
+ "ReleaseTime": 2
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ },
+ "ReleaseTime": 2
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/warehousejson.txt b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/warehousejson.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6c208ca746adf0f3fed6cefc87bc3e5a9b104892
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Offline_Decentralized/warehousejson.txt
@@ -0,0 +1,26 @@
+{
+ "Matrix": [
+ [2,2,2,2,2,2,2,2,2,2],
+ [2,2,2,2,2,2,2,2,2,2],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
+ "Rotation": 45,
+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
+ {"GridPosition":{"Row":0,"Column":0},"Degrees":0},
+ {"GridPosition":{"Row":1,"Column":4},"Degrees":270},
+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
+ "NumberOfTasks": 5,
+ "CanGoBackWards": false,
+ "RandomTasks": false
+}
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Centralized/tasks.txt b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/tasks.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f62b76ad2e3d4b861325ef0b7eb42389ca6e419
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/tasks.txt
@@ -0,0 +1,52 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ }
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_not_random.txt b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_not_random.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ac87e23386d1b4c8fcefd1f55f4c6596aa0e5abe
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_not_random.txt
@@ -0,0 +1,26 @@
+{
+ "Matrix": [
+ [2,2,2,2,2,2,2,2,2,2],
+ [2,2,2,2,2,2,2,2,2,2],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
+ "Rotation": 45,
+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
+ {"GridPosition":{"Row":0,"Column":0},"Degrees":0},
+ {"GridPosition":{"Row":1,"Column":4},"Degrees":270},
+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
+ "NumberOfTasks": 3,
+ "CanGoBackWards": false,
+ "RandomTasks": false
+}
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_random.txt b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_random.txt
new file mode 100644
index 0000000000000000000000000000000000000000..985e2f98b15dc780fe819ea29221f566ad395764
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Centralized/warehousejson_random.txt
@@ -0,0 +1,26 @@
+{
+ "Matrix": [
+ [2,2,2,2,2,2,2,2,2,2],
+ [2,2,2,2,2,2,2,2,2,2],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
+ "Rotation": 45,
+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
+ {"GridPosition":{"Row":0,"Column":0},"Degrees":0},
+ {"GridPosition":{"Row":1,"Column":4},"Degrees":270},
+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
+ "NumberOfTasks": 3,
+ "CanGoBackWards": false,
+ "RandomTasks": true
+}
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/tasks.txt b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/tasks.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f62b76ad2e3d4b861325ef0b7eb42389ca6e419
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/tasks.txt
@@ -0,0 +1,52 @@
+[
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 2
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 2,
+ "Column": 9
+ },
+ "Delivery": {
+ "Row": 3,
+ "Column": 9
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 3,
+ "Column": 4
+ },
+ "Delivery": {
+ "Row": 5,
+ "Column": 7
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 6,
+ "Column": 0
+ },
+ "Delivery": {
+ "Row": 7,
+ "Column": 3
+ }
+ },
+ {
+ "PickUp": {
+ "Row": 7,
+ "Column": 5
+ },
+ "Delivery": {
+ "Row": 9,
+ "Column": 6
+ }
+ }
+]
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_not_random.txt b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_not_random.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ac87e23386d1b4c8fcefd1f55f4c6596aa0e5abe
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_not_random.txt
@@ -0,0 +1,26 @@
+{
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+ [2,2,2,2,2,2,2,2,2,2],
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+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
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+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
+ {"GridPosition":{"Row":0,"Column":0},"Degrees":0},
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+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
+ "NumberOfTasks": 3,
+ "CanGoBackWards": false,
+ "RandomTasks": false
+}
diff --git a/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_random.txt b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_random.txt
new file mode 100644
index 0000000000000000000000000000000000000000..985e2f98b15dc780fe819ea29221f566ad395764
--- /dev/null
+++ b/PickUpandDelivery/ConfigurationFiles/Online_Decentralized/warehousejson_random.txt
@@ -0,0 +1,26 @@
+{
+ "Matrix": [
+ [2,2,2,2,2,2,2,2,2,2],
+ [2,2,2,2,2,2,2,2,2,2],
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+ [3,0,1,1,1,1,1,1,0,3],
+ [3,0,3,3,3,3,3,3,0,3],
+ [3,0,0,0,0,0,0,0,0,3]
+ ],
+ "Rotation": 45,
+ "TimeRobotPerTile": 1000,
+ "NumberOfRobots": 3,
+ "InitialNodes" : [
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+ {"GridPosition":{"Row":1,"Column":4},"Degrees":270},
+ {"GridPosition":{"Row":0,"Column":9},"Degrees":270}
+ ],
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+ "CanGoBackWards": false,
+ "RandomTasks": true
+}
diff --git a/PickUpandDelivery/Packages/manifest.json b/PickUpandDelivery/Packages/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e9d691a6c4a01ff2d6d7575d39ad268b1aec2145
--- /dev/null
+++ b/PickUpandDelivery/Packages/manifest.json
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+{
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+ "com.unity.modules.vehicles": "1.0.0",
+ "com.unity.modules.video": "1.0.0",
+ "com.unity.modules.vr": "1.0.0",
+ "com.unity.modules.wind": "1.0.0",
+ "com.unity.modules.xr": "1.0.0"
+ }
+}
diff --git a/PickUpandDelivery/Packages/packages-lock.json b/PickUpandDelivery/Packages/packages-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..2eb734f3c1e7fd77fd7d0e096bd6fec7e0b1407b
--- /dev/null
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+}
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diff --git a/PickUpandDelivery/ProjectSettings/ClusterInputManager.asset b/PickUpandDelivery/ProjectSettings/ClusterInputManager.asset
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