Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities

• URL: http://arxiv.org/abs/2404.16162v1
• Date: Wed, 24 Apr 2024 19:37:18 GMT
• Title: Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
• Authors: He Jiang, Yulun Zhang, Rishi Veerapaneni, Jiaoyang Li,
• Abstract summary: We present our winning approach to the 2023 League of Robot Runners LMAPF competition. The first challenge is to search for high-quality LMAPF solutions within a limited planning time. The second challenge is to alleviate myopic congestion and the effect of behaviors in LMAPF algorithms. The third challenge is to bridge the gaps between the LMAPF models used in the literature and real-world applications.
• Score: 44.292720085661585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directions. In this paper, we outline three main research challenges. The first challenge is to search for high-quality LMAPF solutions within a limited planning time (e.g., 1s per step) for a large number of agents (e.g., 10,000) or extremely high agent density (e.g., 97.7%). We present future directions such as developing more competitive rule-based and anytime MAPF algorithms and parallelizing state-of-the-art MAPF algorithms. The second challenge is to alleviate congestion and the effect of myopic behaviors in LMAPF algorithms. We present future directions, such as developing moving guidance and traffic rules to reduce congestion, incorporating future prediction and real-time search, and determining the optimal agent number. The third challenge is to bridge the gaps between the LMAPF models used in the literature and real-world applications. We present future directions, such as dealing with more realistic kinodynamic models, execution uncertainty, and evolving systems.

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