Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction

Jingtao Tang, Zining Mao, Hang Ma

Abstract: In this article, we study multirobot coverage path planning (MCPP) on a four-neighbor 2-D grid $G$, which aims to compute paths for multiple robots to cover all cells of $G$. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid $\mathcal {H}$ and then employ the spanning tree coverage (STC) paradigm to generate paths on $G$, making them inapplicable to grids with partially obstructed $2 \times 2$ blocks. To address this limitation, we reformulate the problem directly on $G$, 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 $\mathcal {H}$ 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 $G$. 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 $\text{256} \times \text{256}$ within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.