大規模倉庫における生涯マルチエージェント経路計画
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
倉庫内でエージェントが次々と新しい目標地点に向かう生涯MAPF問題に対し、時間窓ごとに衝突を解消するRHCRフレームワークを提案し、最大1000体でも高品質な解を得られることを示した。
著者: Jiaoyang Li, Andrew Tinka, Scott Kiesel, Joseph W. Durham, T. K. Satish Kumar, Sven Koenig
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.
