Caching-Augmented Lifelong Multi-Agent Path Finding

• URL: http://arxiv.org/abs/2403.13421v3
• Date: Fri, 5 Apr 2024 18:23:38 GMT
• Title: Caching-Augmented Lifelong Multi-Agent Path Finding
• Authors: Yimin Tang, Zhenghong Yu, Yi Zheng, T. K. Satish Kumar, Jiaoyang Li, Sven Koenig,
• Abstract summary: Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial targets, offers a more accurate approximation of real-world warehouse planning. We present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF) designed to improve the performance of Lifelong MAPF.
• Score: 25.575748142768298
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 targets, 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 type of map grid called cache for temporary item storage and replacement, and created a locking mechanism to improve the planning solution's stability. A task assigner (TA) is designed for CAL-MAPF to allocate target locations to agents and control agent status in different situations. CAL-MAPF has been evaluated using various cache replacement policies and input task distributions. We have identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. In general, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, map and agent configurations.

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