Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations 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 were 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 methods (LSS) in handling asynchronous actions and rule-based planning methods (PIBT) for MAPF. We analyze the properties of our method and test it against several baselines with up to 1000 agents in various maps. Given a runtime limit, our method can handle an order of magnitude more agents than the baselines with about 25% longer makespan.

翻译：多智能体路径规划旨在为多个智能体寻找从各自起始位置到目标位置的无碰撞路径，同时最小化路径代价。尽管已有许多MAPF算法被开发出来，并能处理多达数千个智能体，但这些算法通常依赖于每个智能体的动作消耗一个时间单位、且所有智能体动作在相同离散时间步启动的同步假设，这可能限制其实际应用。目前仅有少数算法针对异步动作问题进行研究，且均集中于追求最优解而可扩展性有限。本文开发了位于另一极端的新规划器，通过为大量智能体寻找无界次优解，以牺牲解质量为代价换取可扩展性。我们的方法结合了处理异步动作的搜索方法（LSS）与面向MAPF的基于规则的规划方法（PIBT）。我们分析了该方法的特性，并在多种地图中与多个基线方法进行了多达1000个智能体的对比测试。在给定运行时间限制下，本方法能处理的智能体数量比基线方法高出一个数量级，而完工时间仅延长约25%。