統計的安全保証付き動的不可制御エージェント間のマルチエージェント経路探索
Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees
予測不能なエージェントの動きを学習予測し、その不確実性をConformal Predictionで定量化してECBSに統合することで、衝突回避の統計的保証を実現する経路探索手法を提案。
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著者: Kegan J. Strawn, Thomy Phan, Eric Wang, Nora Ayanian, Sven Koenig, Lars Lindemann
分類: cs.MA, cs.RO
原文アブストラクト
Existing multi-agent path finding (MAPF) solvers do not account for uncertain behavior of uncontrollable agents. We present a novel variant of Enhanced Conflict-Based Search (ECBS), for both one-shot and lifelong MAPF in dynamic environments with uncontrollable agents. Our method consists of (1) training a learned predictor for the movement of uncontrollable agents, (2) quantifying the prediction error using conformal prediction (CP), a tool for statistical uncertainty quantification, and (3) integrating these uncertainty intervals into our modified ECBS solver. Our method can account for uncertain agent behavior, comes with statistical guarantees on collision-free paths for one-shot missions, and scales to lifelong missions with a receding horizon sequence of one-shot instances. We run our algorithm, CP-Solver, across warehouse and game maps, with competitive throughput and reduced collisions.
