Multi-agent path finding for large agents

Jiaoyang Li, Pavel Surynek, Ariel Felner, Hang Ma, T. K. Satish Kumar, Sven Koenig

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

1 Scopus citations

Abstract

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a two-level tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. In this paper, we formalize and study MAPF for large agents that considers the shapes of agents. We present a generalized version of CBS, called Multi-Constraint CBS (MC-CBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. Experimental results show that all MC-CBS variants significantly outperform CBS. The best variant also outperforms EPEA (a state-of-the-art A-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.

Original languageEnglish
Title of host publicationProceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019
EditorsPavel Surynek, William Yeoh
PublisherAAAI press
Pages186-187
Number of pages2
ISBN (Electronic)9781577358084
StatePublished - 2019
Externally publishedYes
Event12th International Symposium on Combinatorial Search, SoCS 2019 - Napa, United States
Duration: 16 Jul 201917 Jul 2019

Publication series

NameProceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019

Conference

Conference12th International Symposium on Combinatorial Search, SoCS 2019
Country/TerritoryUnited States
CityNapa
Period16/07/1917/07/19

Bibliographical note

Publisher Copyright:
Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This paper is a short version of (Li et al. 2019). The research at the University of Southern California was supported by the National Science Foundation (NSF) under grant numbers 1409987, 1724392, 1817189 and 1837779 as well as a gift from Amazon. The research was also supported by the United States-Israel Bi-national Science Foundation (BSF) under grant number 2017692 and the Czech Science Foundation (GACR) under grant number 19-17966S. ∗This paper is a short version of (Li et al. 2019). The research at the University of Southern California was supported by the National Science Foundation (NSF) under grant numbers 1409987, 1724392, 1817189 and 1837779 as well as a gift from Amazon. The research was also supported by the United States-Israel Binational Science Foundation (BSF) under grant number 2017692 and the Czech Science Foundation (GACR) under grant number 19-17966S. Copyright ©c 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

FundersFunder number
United States–Israel Bi-National Science Foundation
National Science Foundation1409987, 1724392, 1837779, 1817189
Bonfils-Stanton Foundation2017692
United States-Israel Binational Science Foundation
Grantová Agentura České Republiky19-17966S

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