Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering

Authors

  • Pavel Surynek Czech Technical University in Prague

Keywords:

Heuristic Search, Satisfiability Modulo Theories, Motion and Path Planning

Abstract

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MG-MAPF: a heuristic search-based algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the satisfiability modulo theories (SMT), called SMT-Hamiltonian-CBS (SMT-HCBS).

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Published

2021-05-18

How to Cite

Surynek, P. (2021). Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12409-12417. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17472

Issue

Section

AAAI Technical Track on Search and Optimization