文章基本信息

标题：Fast Multi-Robot Motion Planning via Imitation Learning of Mixed-Integer Programs
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作者：Mohit Srinivasan ; Ankush Chakrabarty ; Rien Quirynen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：20
页码：598-604
DOI：10.1016/j.ifacol.2021.11.237
语种：English
出版社：Elsevier
摘要：AbstractWe propose a centralized multi-robot motion planning approach that leverages machine learning and mixed-integer programming (MIP). We train a neural network to imitate optimal MIP solutions and, during execution, the trajectories predicted by the network are used to fix most of the integer variables, resulting in a significantly reduced MIP or even a convex program. If the obtained trajectories are feasible, i.e., collision-free and reaching the goal, they can be used as they are or further refined towards optimality. Since maximizing the likelihood of feasibility is not the standard goal of imitation learning, we propose several techniques aimed at increasing such likelihood. Simulation results show the reduced computational burden associated with the proposed framework and the similarity with the optimal MIP solutions.
关键词：KeywordsMachine LearningPath PlanningMotion ControlOptimal Control