research-article

Subdimensional expansion for multirobot path planning

Artificial Intelligence, Volume 219, Issue C

Pages 1 - 24

https://doi.org/10.1016/j.artint.2014.11.001

Published: 01 February 2015 Publication History

Abstract

Planning optimal paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robot-robot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. We present the M * algorithm, an implementation of subdimensional expansion that adapts the A * planner to perform efficient multirobot planning. M * is proven to be complete and to find minimal cost paths. Simulation results are presented that show that M * outperforms existing optimal multirobot path planning algorithms.

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Cited By

Orthey AAkbar SToussaint M(2024)Multilevel motion planningInternational Journal of Robotics Research10.1177/0278364923120933743:1(3-33)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/02783649231209337
Zhang XLiu YArcaini PJiang MZheng Z(2024)MET-MAPF: A Metamorphic Testing Approach for Multi-Agent Path Finding AlgorithmsACM Transactions on Software Engineering and Methodology10.1145/366966333:8(1-37)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3669663
Hoch BLiers FNeumann SZaragoza Martínez F(2024)The non-stop disjoint trajectories problemDiscrete Optimization10.1016/j.disopt.2024.10083752:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.disopt.2024.100837
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cover image Artificial Intelligence

Artificial Intelligence Volume 219, Issue C

February 2015

92 pages

ISSN:0004-3702

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Copyright © Elsevier B.V.

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Elsevier Science Publishers Ltd.

United Kingdom

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Published: 01 February 2015

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Cited By

Orthey AAkbar SToussaint M(2024)Multilevel motion planningInternational Journal of Robotics Research10.1177/0278364923120933743:1(3-33)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/02783649231209337
Zhang XLiu YArcaini PJiang MZheng Z(2024)MET-MAPF: A Metamorphic Testing Approach for Multi-Agent Path Finding AlgorithmsACM Transactions on Software Engineering and Methodology10.1145/366966333:8(1-37)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3669663
Hoch BLiers FNeumann SZaragoza Martínez F(2024)The non-stop disjoint trajectories problemDiscrete Optimization10.1016/j.disopt.2024.10083752:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.disopt.2024.100837
Fine GAtzmon DAgmon NAgmon NAn BRicci AYeoh W(2023)Anonymous Multi-Agent Path Finding with Individual DeadlinesProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598724(869-877)Online publication date: 30-May-2023
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Okumura KDéfago XElkind E(2023)Quick multi-robot motion planning by combining sampling and searchProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/29(252-261)Online publication date: 19-Aug-2023
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Atzmon DBernardini SFagnani FFairbairn DKoenig SStern RVallati M(2023)Exploiting geometric constraints in multi-agent pathfindingProceedings of the Thirty-Third International Conference on Automated Planning and Scheduling10.1609/icaps.v33i1.27174(17-25)Online publication date: 8-Jul-2023
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Motes JChen TBretl TAguirre MAmato N(2023)Hypergraph-Based Multi-robot Task and Motion PlanningIEEE Transactions on Robotics10.1109/TRO.2023.329701139:5(4166-4186)Online publication date: 1-Oct-2023
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Dayan DSolovey KPavone MHalperin D(2023)Near-Optimal Multi-Robot Motion Planning with Finite SamplingIEEE Transactions on Robotics10.1109/TRO.2023.328115239:5(3422-3436)Online publication date: 1-Oct-2023
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Ren ZRathinam SChoset H(2023)CBSS: A New Approach for Multiagent Combinatorial Path FindingIEEE Transactions on Robotics10.1109/TRO.2023.326699339:4(2669-2683)Online publication date: 1-Aug-2023
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