Parallel Collaborative Motion Planning with Alternating Direction Method of Multipliers

Collaborative motion planning for multi-agent systems is a challenging problem because of the existence of highly nonlinear and nonconvex constraints. Such difficulties also lead to inavoidable computational inefficiency, which significantly prohibits applying the existing collaborative motion plann...

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Veröffentlicht in:IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society S. 1 - 6
Hauptverfasser: Zhang, Xiaoxue, Cheng, Zilong, Ma, Jun, Zhao, Lin, Xiang, Cheng, Lee, Tong Heng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 13.10.2021
Schlagworte:
alternating direction method of multipliers (ADMM)
Collaboration
collaborative planning
Computational modeling
Convex functions
Dynamics
Heuristic algorithms
motion planning
multi-agent system
Nonlinear dynamical systems
Planning
ISSN:2577-1647
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Zusammenfassung:Collaborative motion planning for multi-agent systems is a challenging problem because of the existence of highly nonlinear and nonconvex constraints. Such difficulties also lead to inavoidable computational inefficiency, which significantly prohibits applying the existing collaborative motion planning algorithms to complex scenarios. This paper proposes a parallel computational algorithm to achieve collaborative motion planning efficiently, considering the nonlinear dynamics model and the nonconvex collision-avoidance constraints. Specifically, the alternating direction method of multipliers (ADMM) framework is elegantly incorporated to separate the large-scale cooperative nonconvex planning problem as two tractable and manageable subproblems, where the two subproblems handle the dynamics constraints and collision-free constraints, respectively. In the proposed approach, the differential dynamic programming (DDP) method is utilized to effectively solve the nonlinear subproblem with the dynamics constraints; meanwhile, the interior point (IPOPT) method is employed to address the nonconvex subproblem derived from the collision-avoidance constraints. Finally, two simulation scenarios are successfully implemented to illustrate the effectiveness of the proposed algorithm.
ISSN:2577-1647
DOI:10.1109/IECON48115.2021.9589675