{"title":"Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction","authors":"Jingtao Tang;Zining Mao;Hang Ma","doi":"10.1109/TRO.2025.3567476","DOIUrl":null,"url":null,"abstract":"In this article, we study multirobot coverage path planning (MCPP) on a four-neighbor 2-D grid <inline-formula><tex-math>$G$</tex-math></inline-formula>, which aims to compute paths for multiple robots to cover all cells of <inline-formula><tex-math>$G$</tex-math></inline-formula>. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid <inline-formula><tex-math>$\\mathcal {H}$</tex-math></inline-formula> and then employ the spanning tree coverage (STC) paradigm to generate paths on <inline-formula><tex-math>$G$</tex-math></inline-formula>, making them inapplicable to grids with partially obstructed <inline-formula><tex-math>$2 \\times 2$</tex-math></inline-formula> blocks. To address this limitation, we reformulate the problem directly on <inline-formula><tex-math>$G$</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>$\\mathcal {H}$</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>$G$</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>$\\text{256} \\times \\text{256}$</tex-math></inline-formula> within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3348-3367"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Robotics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10989517/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles.
Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.