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

TL;DR

This paper discusses the challenges and future research directions for scaling Lifelong Multi-Agent Path Finding to realistic scenarios involving many agents, congestion, and real-world complexities, based on a competition-winning approach.

Contribution

It identifies key research challenges in high-quality solutions, congestion alleviation, and real-world applicability, proposing future directions for advancing LMAPF.

Findings

Winning approach to 2023 LMAPF competition.

Identified challenges in large-scale, congested, and realistic settings.

Proposed future research directions for LMAPF improvements.

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.