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.

翻译：多智能体路径规划（MAPF）是指将多个智能体从起点移动到目标点而不发生碰撞的问题。终身多智能体路径规划（LMAPF）通过持续向智能体分配新目标来扩展MAPF。我们介绍了在2023年机器人跑者联赛LMAPF竞赛中获胜的方法，这引出了若干有趣的研究挑战和未来方向。本文概述了三大主要研究挑战。第一个挑战是在有限规划时间（例如每步1秒）内，为大量智能体（例如10,000个）或极高智能体密度（例如97.7%）寻找高质量的LMAPF解决方案。我们提出了未来方向，如开发更具竞争力的基于规则和任意时间MAPF算法，以及对最先进的MAPF算法进行并行化处理。第二个挑战是缓解LMAPF算法中的拥塞和短视行为影响。我们提出的未来方向包括：开发运动引导和交通规则以减少拥塞、融入未来预测与实时搜索、以及确定最优智能体数量。第三个挑战是弥合文献中使用的LMAPF模型与真实应用之间的差距。我们提出的未来方向包括：处理更真实的运动动力学模型、执行不确定性以及演进系统。