Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction
🤖 AI Summary
This paper addresses the Multi-Robot Coverage Path Planning (MCPP) problem on 2D four-connected grids containing 2×2 obstacle blocks. We propose an end-to-end, fine-grained grid modeling approach that abandons the conventional quadtree-based coarsening paradigm. Our method introduces three key contributions: (1) Extended Spanning Tree Coverage (ESTC), a novel connectivity-preserving coverage structure; (2) LS-MCPP, a local search framework leveraging three neighborhood operators for efficient refinement; and (3) the first integration of Multi-Agent Path Finding (MAPF) into MCPP post-processing to jointly optimize collision resolution and turning cost minimization. Evaluated on 256×256 grids, our algorithm generates complete coverage paths for 100 robots within minutes—achieving significantly higher solution quality and computational efficiency than state-of-the-art baselines. The approach is validated on real robotic platforms, confirming its practical feasibility.
Application Category
📝 Abstract
In this article, we study multirobot coverage path planning (MCPP) on a four-neighbor 2-D grid <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>, which aims to compute paths for multiple robots to cover all cells of <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid <inline-formula><tex-math notation="LaTeX">$mathcal {H}$</tex-math></inline-formula> and then employ the spanning tree coverage (STC) paradigm to generate paths on <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>, making them inapplicable to grids with partially obstructed <inline-formula><tex-math notation="LaTeX">$2 imes 2$</tex-math></inline-formula> blocks. To address this limitation, we reformulate the problem directly on <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>, revolutionizing grid-based MCPP solving and establishing new NP-hardness results. We introduce extended STC (ESTC), a novel paradigm that extends STC to ensure complete coverage with bounded suboptimality, even when <inline-formula><tex-math notation="LaTeX">$mathcal {H}$</tex-math></inline-formula> includes partially obstructed blocks. Furthermore, we present LS-MCPP, a new algorithmic framework that integrates ESTC with three novel types of neighborhood operators within a local search strategy to optimize coverage paths directly on <inline-formula><tex-math notation="LaTeX">$G$</tex-math></inline-formula>. Unlike prior grid-based MCPP work, our approach also incorporates a versatile postprocessing procedure that applies multiagent path finding (MAPF) techniques to MCPP for the first time, enabling a fusion of these two important fields in multirobot coordination. This procedure effectively resolves inter-robot conflicts and accommodates turning costs by solving an MAPF variant, making our MCPP solutions more practical for real-world applications. Extensive experiments demonstrate that our approach significantly improves solution quality and efficiency, managing up to 100 robots on grids as large as <inline-formula><tex-math notation="LaTeX">$ ext{256} imes ext{256}$</tex-math></inline-formula> within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.
Problem
Research questions and friction points this paper is trying to address.
Extends STC to handle obstructed grids in multi-robot coverage planning
Integrates local search with novel operators for optimized path planning
Resolves robot conflicts using MAPF techniques for practical applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extended-STC paradigm for obstructed grids
Local search with novel neighborhood operators
MAPF techniques for conflict resolution
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