DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning

Motivation

This project deals with the path planning problem of a team of mobile robots, in order to cover an area of interest, with prior-defined obstacles.

DARP algorithm divides the terrain into a number of equal areas each corresponding to a specific robot, so as to guarantee complete coverage, non-backtracking solution, minimum coverage path, while at the same time does not need any preparatory stage.

But how does this algorithm work?

In essence, the DARP algorithm follows a cyclic coordinate descent optimization scheme updating each robots’ territory separately but towards achieving the overall multi-robot Coverage Path Planning (mCPP) objectives.

After the desired area division is achieved, we use Spanning Tree Coverage (STC) algorithm to produce the optimal path for each robot, in order to achieve full coverage of the area of interest.

Requirements

This project was created using:

• Python version >= 3.6.14
• OpenCV version >= 4.5.2.54
• Pygame version >= 2.0.1
• Scipy version >= 1.7.1
• nose == 1.3.7
• scikit-learn

Installation and Running

To install the application, use:

git clone https://github.com/alice-st/DARP-Python.git
cd DARP
./Dependencies.sh DARP
source DARP/bin/activate

To run the application, use:

python3 multiRobotPathPlanner.py

Usage

By default, without defining any parameters, the multiRobotPathPlanner is going to run for the following setup:

where the red, green and purple cells denote the initial positions of the 3 robots respectively and the black cells denote the environments obstacles.

To define specific parameters please use the instructions below:

To modify the Grid Dimensions, use:

python3 multiRobotPathPlanner.py -grid x y

where x, y are the desired rows and columns of the Grid respectively (default: 10, 10).

To modify the number of Robots and their Initial Positions, use:

python3 multiRobotPathPlanner.py -in_pos a b c

where a, b, c, are the cells' numbers in the Grid (default: 0, 3, 9) (row=0,column=0 --> cell=0, row=0,column=1 --> cell=1 etc.)

To assign different portions to each Robot (not Equal), use:

python3 multiRobotPathPlanner.py -nep -portions p_a p_b p_c

where p_a p_b p_c are the portions assigned to Robots a, b and c respectively. Their sum should be equal to 1. (default: 0.2, 0.3, 0.5)

If -nep is activated (set to True), the algorithm runs for not equal territories with 20%, 30% and 50% coverage per robot. Otherwise, the algorithm runs for equal territories with 33,33% coverage per robot.

To use different positions for the obstacles in the Grid, use:

python3 multiRobotPathPlanner.py -obs_pos o1 o2 o3

where o1 o2 and o3 are the positions of the obstacles in the Grid. Obstacle positions should not overlap with Robots' initial positions. (default: 5, 6, 7) (row=0,column=0 --> cell=0, row=0,column=1 --> cell=1 etc.)

To visualize the results, use:

python3 multiRobotPathPlanner.py -vis

To run the Unittests use:

nosetests --nocapture mainUnitTest.py

Demo example:

python3 multiRobotPathPlanner.py -vis -nep -obs_pos 10 11 12 21 22 23 33 34 35 45 46 47 57 -in_pos 0 99 32 -portions 0.7 0.2 0.1

Example execution

Using a 20x20 Grid area, four robots with initial positions 10, 32, 99 and 250 and Equal portions of the Grid shared between the robots, we obtained the following results:

Assignment Matrix

Final coverage paths for all robots

For each robot path has been utilized the mode that results in the minimum number of turns to completely cover its respective sub-region.

Extra Material

Paper: Zenodo

Medium: Medium

GitHub repositories: Java

GUI demo: YouTube

ROS integration: Wiki

Cite as

@article{kapoutsisdarp,
  title={DARP: Divide Areas Algorithm for Optimal Multi-Robot Coverage Path Planning},
  author={Kapoutsis, Athanasios Ch and Chatzichristofis, Savvas A and Kosmatopoulos, Elias B},
  journal={Journal of Intelligent \& Robotic Systems},
  pages={1--18},
  publisher={Springer}
}