遅延確率を考慮したマルチエージェント経路計画
Multi-Agent Path Finding with Delay Probabilities
実行遅延の確率を考慮したMAPF問題を定式化し、衝突を防ぐ分散実行ポリシーと2段階探索による解法を提案した。
著者: Hang Ma, T. K. Satish Kumar, Sven Koenig
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.
