Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions - Zhou, Shuai; Zhao, Shizhe; Ren, Zhongqiang (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
Given a graph representing the workspace, Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective start vertex to their respective goal vertex while minimizing path costs. Although many MAPF algorithms were developed and can handle up to thousands of agents, they usually rely on the assumption that each action of the agent takes a time unit, and the actions of all agents are synchronized in a sense that the actions of agents start at the same discrete time step, which may limit their use in practice. Only a few algorithms have been developed to address asynchronous actions, and they all lie on one end of the spectrum, focusing on finding optimal solutions with limited scalability. This paper develops new planners that lie on the other end of the spectrum, trading off solution quality for scalability, by finding an unbounded sub-optimal solution for many agents. Our method leverages both search-based methods in handling asynchronous actions and techniques in rule-based planning for MAPF. We analyze the properties of our method and test it against several baselines with up to a thousand agents with asynchronous actions in various maps. Given a runtime limit, our method can handle an order of magnitude more agents than the existing methods with about 25% longer makespan.