TY - JOUR
T1 - Representation-Optimal Multi-Robot Motion Planning Using Conflict-Based Search
AU - Solis Vidana, Juan Irving
AU - Motes, James
AU - Sandstrom, Read
AU - Amato, Nancy
N1 - Funding Information:
Manuscript received October 15, 2020; accepted February 25, 2021. Date of publication March 25, 2021; date of current version April 13, 2021. This letter was recommended for publication by Associate Editor B. Wang and Editor M. A. Hsieh upon evaluation of the reviewers’ comments. This work was supported in part by CONACYT. (Corresponding author: James Motes.) Irving Solis is with the Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77840 USA (e-mail: irvingsolis-89@tamu.edu.).
Publisher Copyright:
© 2016 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.
AB - Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.
KW - Path planning for multiple mobile robots or agents
KW - motion and path planning
UR - http://www.scopus.com/inward/record.url?scp=85103255729&partnerID=8YFLogxK
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U2 - 10.1109/LRA.2021.3068910
DO - 10.1109/LRA.2021.3068910
M3 - Article
AN - SCOPUS:85103255729
SN - 2377-3766
VL - 6
SP - 4608
EP - 4615
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
M1 - 9387143
ER -