Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning
Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning
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DOI：
10.23919/acc.2019.8814941
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
George P. Kontoudis;K. Vamvoudakis
中科院分区：
文献类型：
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作者：
George P. Kontoudis;K. Vamvoudakis
本文提出了一种在线的，鲁棒的，无模型的运动规划框架kinodynamic系统。特别是，我们采用了Q-学习算法的两个球员零和动态游戏占最坏情况下的干扰和kinodynamic约束。我们使用一个评论家，和两个演员逼近在线解决有限时域极大极小问题的一种形式的积分强化学习。然后，我们利用终端状态评估结构，以促进在线实施。一个静态的障碍物增加，并提出了一个本地重新规划框架，以保证安全的运动动力学运动规划。严格的基于李雅普诺夫的证明，以保证闭环稳定性，同时保持鲁棒性和最优性。最后，我们评估所提出的框架与模拟的有效性，我们提供了一个定性的比较动息运动规划技术。
This paper presents an online, robust, and model-free motion planning framework for kinodynamic systems. In particular, we employ a Q-learning algorithm for a two player zero-sum dynamic game to account for worst-case disturbances and kinodynamic constraints. We use one critic, and two actor approximators to solve online the finite horizon minimax problem with a form of integral reinforcement learning. We then leverage a terminal state evaluation structure to facilitate the online implementation. A static obstacle augmentation, and a local replanning framework is presented to guarantee safe kinodynamic motion planning. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability, while maintaining robustness and optimality. We finally evaluate the efficacy of the proposed framework with simulations and we provide a qualitative comparison of kinodynamic motion planning techniques.