リアルタイムMAPFのためのリアルタイムLaCAM
Real-Time LaCAM for Real-Time MAPF
完全性保証を持つ初のリアルタイム多エージェント経路計画手法を提案し、混雑環境でもミリ秒単位の制限時間で全時間計画と同等の成功率を達成した。
著者: Runzhe Liang, Rishi Veerapaneni, Daniel Harabor, Jiaoyang Li, Maxim Likhachev
分類: cs.MA, cs.AI, cs.RO
原文アブストラクト
The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner a finite amount of time before executing and replanning, is more practical for real-world multi-agent systems. Several methods utilize real-time planning schemes but none are provably complete, which leads to livelock or deadlock. Our main contribution is Real-Time LaCAM, the first Real-Time MAPF method with provable completeness guarantees. We do this by leveraging LaCAM (Okumura 2023) in an incremental fashion. Our results show how we can iteratively plan for congested environments with a cutoff time of milliseconds while still maintaining the same success rate as full-horizon LaCAM. We also show how it can be used with a single-step learned MAPF policy.
