Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

• URL: http://arxiv.org/abs/2208.01222v1
• Date: Tue, 2 Aug 2022 03:17:29 GMT
• Title: Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
• Authors: Xinyi Zhong, Jiaoyang Li, Sven Koenig, Hang Ma
• Abstract summary: We study the multi-goal task assignment and path finding (MG-TAPF) problem from theoretical and algorithmic perspectives. 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.
• Score: 25.11387753357413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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