This paper presents an adaptive online distributed optimal control approach that is applicable to optimal planning for very-large-scale robotics systems in highly uncertain environments. This approach is developed based on the optimal mass transport theory. It is also viewed as an online reinforcement learning and approximate dynamic programming approach in the Wasserstein-GMM space, where a novel value functional is defined based on the probability density functions of robots and the time-varying obstacle map functions describing the changing environmental information. The proposed approach is demonstrated on the path planning problem of very-largescale robotic systems where the approximated layout of obstacles in the workspace is incrementally updated by the observations of robots, and compared with some existing state-of-the-art approaches. The numerical simulation results show that the proposed approach outperforms these approaches in aspects of the average traveling distance and the energy cost.

中文翻译：

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超大规模机器人系统的自适应在线分布式优化控制

本文提出了一种自适应在线分布式最优控制方法，适用于高度不确定环境中超大规模机器人系统的优化规划。这种方法是基于最优质量传输理论开发的。它也被视为 Wasserstein-GMM 空间中的在线强化学习和近似动态规划方法，其中基于机器人的概率密度函数和描述不断变化的环境信息的时变障碍图函数定义了一个新的值函数。所提出的方法在超大规模机器人系统的路径规划问题上得到证明，其中工作空间中障碍物的近似布局通过机器人的观察逐步更新，并与一些现有的最先进方法进行比较。