共有環境におけるフロー認識型マルチエージェント経路探索を用いた衝突緩和
Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding
動的な非制御エージェントと共有する環境で、その動きの学習パターンをMAPFに統合し、衝突を最大55%削減するフレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Lukas Heuer, Yufei Zhu, Luigi Palmieri, Andrey Rudenko, Anna Mannucci, Sven Koenig, Martin Magnusson
分類: cs.RO, cs.MA
原文アブストラクト
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.
