マルチゴールタスク割当と経路探索の最適・有界準最適アルゴリズム
Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
各エージェントに順序付きゴール列からなるタスクを割り当て、衝突のない経路を求めるMG-TAPF問題を定式化し、NP困難性を示した上で最適・有界準最適アルゴリズムを提案・比較した。
著者: Xinyi Zhong, Jiaoyang Li, Sven Koenig, Hang Ma
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
We formalize and study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and collision-free paths for the agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent path finding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.
