  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.
