Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

TL;DR

This paper introduces SCoPP, a scalable multi-robot coverage path planning algorithm that efficiently handles non-convex areas with obstacles, balancing workload and optimizing paths for large robot teams in time-critical monitoring tasks.

Contribution

The paper presents a novel, scalable CPP algorithm that accounts for non-convex areas and workload balancing, with demonstrated efficiency on large multi-robot systems.

Findings

SCoPP outperforms existing methods in mission completion time.

The algorithm achieves under 2-minute computation for large maps with 150 robots.

Scalability and computational efficiency are validated through tests on various map sizes.

Abstract

This paper presents a novel multi-robot coverage path planning (CPP) algorithm - aka SCoPP - that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., no-fly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.