Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance

Nir Greshler, Ofir Gordon, Oren Salzman, Nahum Shimkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-based algorithm for solving the problem optimally for a wide set of Co-MAPF problems. Co-CBS uses a cooperation-planning module integrated into CBS such that cooperation planning is decoupled from path planning. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm’s properties.

Original languageEnglish
Title of host publication14th International Symposium on Combinatorial Search, SoCS 2021
EditorsHang Ma, Ivan Serina
Pages173-175
Number of pages3
ISBN (Electronic)9781713834557
StatePublished - 2021
Event14th International Symposium on Combinatorial Search, SoCS 2021 - Guangzhou, Virtual, China
Duration: 26 Jul 202130 Jul 2021

Publication series

Name14th International Symposium on Combinatorial Search, SoCS 2021

Conference

Conference14th International Symposium on Combinatorial Search, SoCS 2021
Country/TerritoryChina
CityGuangzhou, Virtual
Period26/07/2130/07/21

ASJC Scopus subject areas

  • Computer Networks and Communications

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