Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests

TL;DR

This paper introduces an energy-efficient multi-robot coverage path planning framework for complex non-convex regions, outperforming existing methods in energy use and scalability, demonstrated through real-world experiments.

Contribution

The proposed MRCPP framework innovates by integrating global swath generation, safe turning buffers, workload balancing with an mTSP solver, and a modified visibility graph for disjoint segments.

Findings

Reduces energy consumption by 3% to 40% compared to state-of-the-art.

Decreases computation time by an order of magnitude.

Maintains balanced workload and scalability across multiple robots.

Abstract

This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing obstacles and no-fly zones (NFZ). Existing minimum-energy coverage planning algorithms utilize meta-heuristic boustrophedon workspace decomposition. Therefore, even with minimum energy objectives and energy consumption constraints, they cannot achieve optimal energy efficiency. Moreover, most existing frameworks support only a single type of robotic platform. MRCPP overcomes these limitations by: generating globally-informed swath generation, creating parallel sweeping paths with minimal turns, calculating safety buffers to ensure safe turning clearance, using an efficient mTSP solver to balance workloads and minimize mission time, and connecting disjoint segments via a modified visibility graph that tracks heading angles while maintaining transitions within safe regions. The efficacy of the proposed MRCPP framework is demonstrated through real-world experiments involving autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs). Evaluations demonstrate that the proposed MRCPP consistently outperforms state-of-the-art planners, reducing average total energy consumption by 3\% to 40\% for a team of 3 robots and computation time by an order of magnitude, while maintaining balanced workload distribution and strong scalability across increasing fleet sizes. The MRCPP framework is released as an open-source package and videos of real-world and simulated experiments are available at https://mrc-pp.github.io.