用动态障碍物运动规律优化多机器人路径，减少55%冲突
Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding
- 将不可控代理的运动模式融入中央路径规划
- 实测可降低55%与动态障碍物的冲突
- 适合大规模机器人在人机共存环境部署
在共享环境中部署大型机器人集群时，机器人与不可控动态代理之间的意外冲突会导致任务延迟。现有研究多关注保持多智能体路径规划（MAPF）的完整性，却较少利用额外环境信息提升解的质量。为此，我们提出流动感知多智能体路径规划（FA-MAPF），将学习到的不可控代理运动模式集成至中心化MAPF算法中。在多种基准地图上的仿真测试以及真实地图上基于人类轨迹数据的实验均表明，相较于最先进基线方法，FA-MAPF能持续减少与不可控代理的冲突，最多达55%，且不牺牲任务效率。
原文摘要 · Abstract (English)
Deploying multi-robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments, individual robot operations can be delayed due to unforeseen conflicts with uncontrollable agents. While existing research primarily focuses on preserving the completeness of Multi-Agent Path Finding (MAPF) solutions considering delays, there is limited emphasis on utilizing additional environmental information to enhance solution quality in the presence of other dynamic agents. To this end, we propose Flow-Aware Multi-Agent Path Finding (FA-MAPF), a novel framework that integrates learned motion patterns of uncontrollable agents into centralized MAPF algorithms. Our evaluation, conducted on a diverse set of benchmark maps with simulated uncontrollable agents and on a real-world map with recorded human trajectories, demonstrates the effectiveness of FA-MAPF compared to state-of-the-art baselines. The experimental results show that FA-MAPF can consistently reduce conflicts with uncontrollable agents, up to 55%, without compromising task efficiency.
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