research-article

Conflict-based search for optimal multi-agent pathfinding

Authors:

Nathan R. SturtevantAuthors Info & Claims

Artificial Intelligence, Volume 219, Issue C

Pages 40 - 66

https://doi.org/10.1016/j.artint.2014.11.006

Published: 01 February 2015 Publication History

Abstract

In the multi-agent pathfinding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions. Most previous work on solving this problem optimally has treated the individual agents as a single 'joint agent' and then applied single-agent search variants of the A* algorithm.In this paper we present the Conflict Based Search (CBS) a new optimal multi-agent pathfinding algorithm. CBS is a two-level algorithm that does not convert the problem into the single 'joint agent' model. At the high level, a search is performed on a Conflict Tree (CT) which is a tree based on conflicts between individual agents. Each node in the CT represents a set of constraints on the motion of the agents. At the low level, fast single-agent searches are performed to satisfy the constraints imposed by the high level CT node. In many cases this two-level formulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks.Additionally we present the Meta-Agent CBS (MA-CBS) algorithm. MA-CBS is a generalization of CBS. Unlike basic CBS, MA-CBS is not restricted to single-agent searches at the low level. Instead, MA-CBS allows agents to be merged into small groups of joint agents. This mitigates some of the drawbacks of basic CBS and further improves performance. In fact, MA-CBS is a framework that can be built on top of any optimal and complete MAPF solver in order to enhance its performance. Experimental results on various problems show a speedup of up to an order of magnitude over previous approaches.

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Walker TSturtevant NFelner ADastani MSichman JAlechina NDignum V(2024)Clique Analysis and Bypassing in Continuous-Time Conflict-Based SearchProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663220(2540-2542)Online publication date: 6-May-2024
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Tang HBerto FMa ZHua CAhn KPark JDastani MSichman JAlechina NDignum V(2024)HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663206(2498-2500)Online publication date: 6-May-2024
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cover image Artificial Intelligence

Artificial Intelligence Volume 219, Issue C

February 2015

92 pages

ISSN:0004-3702

Issue’s Table of Contents

Copyright © Elsevier B.V.

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Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 February 2015

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Cited By

Wang IWang T(2025)Efficient routing in robotic movable fulfillment systems with integer programmingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10284991:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.rcim.2024.102849
Walker TSturtevant NFelner ADastani MSichman JAlechina NDignum V(2024)Clique Analysis and Bypassing in Continuous-Time Conflict-Based SearchProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663220(2540-2542)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663220
Tang HBerto FMa ZHua CAhn KPark JDastani MSichman JAlechina NDignum V(2024)HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663206(2498-2500)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663206
Chong YLi JSycara KDastani MSichman JAlechina NDignum V(2024)Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal ConstraintsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663110(2210-2212)Online publication date: 6-May-2024
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Friedrich PZhang YCurry MDierks LMcAleer SLi JSandholm TSeuken SLarson K(2024)Scalable mechanism design for multi-agent path findingProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/7(58-66)Online publication date: 3-Aug-2024
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