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 language | English |
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Title of host publication | Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019 |
Editors | Pavel Surynek, William Yeoh |
Publisher | AAAI press |
Pages | 186-187 |
Number of pages | 2 |
ISBN (Electronic) | 9781577358084 |
State | Published - 2019 |
Externally published | Yes |
Event | 12th International Symposium on Combinatorial Search, SoCS 2019 - Napa, United States Duration: 16 Jul 2019 → 17 Jul 2019 |
Publication series
Name | Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019 |
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Conference
Conference | 12th International Symposium on Combinatorial Search, SoCS 2019 |
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Country/Territory | United States |
City | Napa |
Period | 16/07/19 → 17/07/19 |
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.
Funders | Funder number |
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United States–Israel Bi-National Science Foundation | |
National Science Foundation | 1409987, 1724392, 1837779, 1817189 |
Bonfils-Stanton Foundation | 2017692 |
United States-Israel Binational Science Foundation | |
Grantová Agentura České Republiky | 19-17966S |