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

2026-04-23

AI Summary

This study addresses the challenge of coverage path planning in large-scale non-convex environments containing obstacles and no-fly zones, where existing methods struggle to simultaneously achieve global energy optimality, multi-platform compatibility, and computational efficiency. The authors propose a multi-robot cooperative coverage framework that uniquely integrates support for heterogeneous robots, global energy optimization, and safety-aware turning constraints. Their approach combines global strip generation, minimum-turn parallel sweeping, safety buffer modeling, efficient multiple Traveling Salesman Problem (mTSP)-based task allocation, and a visibility-graph connection strategy incorporating heading-angle constraints. Experimental validation on real aerial and surface robots demonstrates that, compared to state-of-the-art methods, the proposed framework reduces total energy consumption by 3%–40% and decreases computation time by an order of magnitude for a three-robot system, while ensuring scalability, load balancing, and low planning overhead.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• multi-robot coverage path planning
• energy efficiency
• non-convex regions
• obstacles and no-fly zones
• heterogeneous robotic platforms

Innovation

• multi-robot coverage path planning
• energy-efficient
• non-convex regions
• heterogeneous robotic platforms
• modified visibility graph