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.
@article{arxiv.2208.01222,
title = {Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding},
author = {Xinyi Zhong and Jiaoyang Li and Sven Koenig and Hang Ma},
journal= {arXiv preprint arXiv:2208.01222},
year = {2022}
}