This repo contains the implementation of a project: Conceptualization and Implementation of a Novel Area Coverage Path Planning for Multiple Mobile Robots. Multi-agent systems (MASs) have been investigated intensively by scholars from different disciplines, such as computer science, biology, and civil engineering. Area coverage and mapping of an agricultural field is a crucial step towards precision agriculture. In this repo, a distributed area coverage control and mapping framework is proposed to explore and map an unknown 2D environment with multiple agents. Tangent Bug, together with a flood fill inspired algorithm is utilized to circumnavigate and recognize the restricted area in the environment. The proposed framework is easy-scalable with respect to the number of agents, adaptable to environmental changes, and simple to implement. Keywords: Multi-agent system, online area coverage, path planning, cooperative control.
- Anti-flocking multi-robot area coverage path planning
- Domain exploration with and without obstacles
- Tangent Bug circumnavigation algorithm
- Flood-Fill algoorithm
- Plots and robot motion animation
.
├── docs # important reference paper
├── image # simulation results
├── data # store the coverage percent data
├── scripts
│ ├── env_map.py # create 2D grid map with obstacles
│ ├── robot.py # create Robot object with predefined parameters
│ ├── floodFill.py # flood-fill algorith to fill the inner area of an obstacle
│ ├── methods.py # functions such as sigma-norm adjacency matirx
│ ├── global_value.py # define the parameters for robots and simulations
│ ├── save_value.py # save the evolution of the coverage percent value from each simulation
│ ├── robot_animation.py # animate the robot motion
│ ├── tangentbug.py # Tangent Bug algorithm for circumnavigation
│ └── pathPlanning.py # the whole framework
├── tests
└── ...
- numpy
- math
- matplotlib
- scipy
- ffmpeg-python
The simulation is conducted by running the pathPlanning.py file in the scripts folder.
The pathPlanning.py file can be modified by the user to add robots and define obstacles.
from env_map import *
mymap = EnvMap(50,50, 1) # create [50x50] 2D grid map with cell size 1
mymap.add_circle(10, 15, 6) # add a circle obstacle at position [10,15] with radius 6
mymap.add_polygon([24,41,37,37,40,27,32,23,28,30,20,32]) # add a polygon obstacle by defining its vertices
mymap.show_map() # show the map 
from robot import *
robot_number=3
robotList = define_robot(robot_number) # define a group of 3 robots and store the robot object in a list
for robot in robotList:
robot.random_init_state(mymap) # initialize their position
robot.random_init_target() # initialize their target
show_robot(robotList, mymap) # show the initial states and target on the map 
The robot parameters, simulation time and stepsize can be set up in the global_value.py
# simulation parameters
robot_number=3
random_initial_state=1 # the robots are randomly initialized in the environment if 1, otherwise a manual initialization has to be done
coverage_percent=0.95 # simulation will terminate if coverage percent achieved
T=120 # total simulation time. hard break out condition to prevent infinite loop
step_size=0.1 # size of each stepfilename=save_variable(c_percent[:time],'../data/coverage_percent') #save the c_percent data to file in data folder
plot_coverage_percent(c_percent, time) # plot the evolution of the coverage percent
plot_robot_path(robotList, mymap, time) # plot the robot path
anim = visualize_motion(robotList, mymap, time) # show 2D animation
plot_path_on_infomap(robot_path_shot_list, merged_infomap_shot_list, mymap) # plot the snap shot of the trajectory
single_path_on_infomap(robotList, mymap, time) # plot the final result of the path planng together with the generated information map Run the pathPlanning.py script to start the simulation.