Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial objectives, offers a more accurate approximation of real-world warehouse planning. In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF. We have developed a new map grid type called cache for temporary item storage and replacement and designed a lock mechanism for it to improve the stability of the planning solution. This cache mechanism was evaluated using various cache replacement policies and a spectrum of input task distributions. We identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. Overall, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, maps and agent configurations.

翻译：多智能体路径规划（MAPF）涉及为多个机器人寻找无碰撞路径，在各应用中至关重要。终身MAPF中，智能体完成初始目标后立即重新分配新目标，能更精确地逼近真实仓储规划场景。本文提出名为“缓存增强终身多智能体路径规划”（CAL-MAPF）的新机制，旨在提升终身MAPF的性能。我们开发了名为“缓存”的新型网格类型用于临时物品存储与替换，并为其设计锁机制以提高规划方案的稳定性。通过采用多种缓存替换策略及不同输入任务分布对该缓存机制进行评测，实验发现显著影响CAL-MAPF性能的三个主要因素：适配的输入任务分布、高缓存命中率及流畅的交通流。总体而言，CAL-MAPF在特定任务分布、地图及智能体配置中展现出性能提升潜力。