Multi-Robot Path Planning for Comprehensive Area Coverage in Complex Environments

Manish Kumar, Arindam Ghosh, Muneendra Ojha

Abstract: Multi-robot coverage path planning (mCPP) involves devising efficient motion sequences for robots to cover all positions within a workspace, excluding obstacles. This paper focuses on addressing the path-planning challenge of a Divide Area based on Robot’s Initial Positions (DARP), where a group of mobile robots is tasked with covering a predefined area containing obstacles. This work proposes an improved DARP algorithm for efficient coverage. The proposed algorithm, combined with the $A^{*}$ and the spanning tree coverage algorithm, assigns tasks to robots to optimally achieve full coverage of the desired area. It transforms the initial mCPP problem into individual coverage path planning tasks of single-robot, combining their solutions to form the optimal solution for mCPP. This method significantly improves performance by reducing coverage time, minimizing the number of turns taken by robots, and enhancing overall coverage efficiency.