Windowed MAPF with Completeness Guarantees
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910919832895488 |
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| author | Veerapaneni, Rishi Saleem, Muhammad Suhail Li, Jiaoyang Likhachev, Maxim |
| author_facet | Veerapaneni, Rishi Saleem, Muhammad Suhail Li, Jiaoyang Likhachev, Maxim |
| contents | Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a "windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Windowed MAPF with Completeness Guarantees Veerapaneni, Rishi Saleem, Muhammad Suhail Li, Jiaoyang Likhachev, Maxim Multiagent Systems Artificial Intelligence Robotics Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. Methods that address this typically employ a "windowed" approach and only try to find collision free paths for a small windowed timestep horizon. This adaptation comes at the cost of incompleteness; all current windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework uses heuristic update insights from single-agent real-time heuristic search algorithms as well as agent independence ideas from MAPF algorithms. We also develop Single-Step CBS (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail. |
| title | Windowed MAPF with Completeness Guarantees |
| topic | Multiagent Systems Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2410.01798 |