Acceptance-Rejection Sampling-Guided Discrete RRT for Multiagent Path Finding

Discrete rapid-exploring random tree (dRRT) methods have been proposed but remain to be improved, showing great potential for efficiently solving multiagent path finding (MAPF) problems. This article proposes a new dRRT algorithm designed to tackle complex and challenging MAPF instances that current...

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Vydáno v:IEEE transactions on industrial informatics s. 1 - 12
Hlavní autoři: Xin, Jianbin, Duan, Yuze, Zhu, Hai, Liang, Jing
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Backtracking
Collision avoidance
Connectors
Heuristic algorithms
Multi-robot systems
Multiagent path finding (MAPF)
multirobot systems
path planning
Planning
Probabilistic logic
Robot kinematics
sampling-based algorithm
Time complexity
Training
ISSN:1551-3203, 1941-0050
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Abstract Discrete rapid-exploring random tree (dRRT) methods have been proposed but remain to be improved, showing great potential for efficiently solving multiagent path finding (MAPF) problems. This article proposes a new dRRT algorithm designed to tackle complex and challenging MAPF instances that current discrete RRT approaches struggle to solve efficiently. In particular, we introduce an acceptance-rejection sampling method that efficiently guides the search for feasible paths toward destinations. Moreover, a multistep expansion strategy is proposed to improve search efficiency and path quality. Theoretical analysis regarding time complexity and probabilistic completeness is also provided. The proposed algorithm has been extensively tested on MAPF benchmark instances and verified in industrial simulations and real-world experiments, demonstrating its superior performance in handling large-scale MAPF instances and its competitiveness compared to state-of-the-art algorithms.
AbstractList Discrete rapid-exploring random tree (dRRT) methods have been proposed but remain to be improved, showing great potential for efficiently solving multiagent path finding (MAPF) problems. This article proposes a new dRRT algorithm designed to tackle complex and challenging MAPF instances that current discrete RRT approaches struggle to solve efficiently. In particular, we introduce an acceptance-rejection sampling method that efficiently guides the search for feasible paths toward destinations. Moreover, a multistep expansion strategy is proposed to improve search efficiency and path quality. Theoretical analysis regarding time complexity and probabilistic completeness is also provided. The proposed algorithm has been extensively tested on MAPF benchmark instances and verified in industrial simulations and real-world experiments, demonstrating its superior performance in handling large-scale MAPF instances and its competitiveness compared to state-of-the-art algorithms.
Author Liang, Jing
Zhu, Hai
Xin, Jianbin
Duan, Yuze
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Snippet Discrete rapid-exploring random tree (dRRT) methods have been proposed but remain to be improved, showing great potential for efficiently solving multiagent...
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SubjectTerms Backtracking
Collision avoidance
Connectors
Heuristic algorithms
Multi-robot systems
Multiagent path finding (MAPF)
multirobot systems
path planning
Planning
Probabilistic logic
Robot kinematics
sampling-based algorithm
Time complexity
Training
Title Acceptance-Rejection Sampling-Guided Discrete RRT for Multiagent Path Finding
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