← 研踪 PaperTrailarXiv:2611.10363 · v1
LSRP*: Scalable and Anytime Planning for Multi-Agent Path Finding with Asynchronous Actions
首次提交 2026-11-07 08:58
版本更新 2026-11-07 08:58
雷达观测 2026-11-10 10:11
摘要
Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path costs. Although many MAPF algorithms were developed, most of them rely on a common assumption on synchronized actions, where the actions of all agents start at the same time and always take a time unit. This assumption may limit the use of MAPF planners in practice. To get rid of this assumption, recently, an algorithm called Loosely Synchronized Rule-based Planning (LSRP) was proposed, which can find sub-optimal solutions for many agents. However, LSRP often finds poor quality solutions due to its unbounded sub-optimality. This paper develops a new anytime planner called LSRP* that can keep improving solution quality after the initial solution is obtained until the runtime budget depletes. We analyze the properties of LSRP* and test it against several baselines with up to 1000 agents on various maps. LSRP* can handle up to 25% more agents than LSRP and can reduce up to 40% of the solution cost found by LSRP.
作者备注：15 pages, 14 figures
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v1 · 2026-11-07 08:58 · 最新观测版本
LSRP*: Scalable and Anytime Planning for Multi-Agent Path Finding with Asynchronous Actions
Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path costs. Although many MAPF algorithms were developed, most of them rely on a common assumption on synchronized actions, where the actions of all agents start at the same time and always take a time unit. This assumption may limit the use of MAPF planners in practice. To get rid of this assumption, recently, an algorithm called Loosely Synchronized Rule-based Planning (LSRP) was proposed, which can find sub-optimal solutions for many agents. However, LSRP often finds poor quality solutions due to its unbounded sub-optimality. This paper develops a new anytime planner called LSRP* that can keep improving solution quality after the initial solution is obtained until the runtime budget depletes. We analyze the properties of LSRP* and test it against several baselines with up to 1000 agents on various maps. LSRP* can handle up to 25% more agents than LSRP and can reduce up to 40% of the solution cost found by LSRP.
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