论文标题
自适应在线分布式非常大规模机器人系统的最佳控制
Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems
论文作者
论文摘要
本文提出了一种自适应在线分布式最佳控制方法，适用于在高度不确定的环境中针对非常大规模的机器人系统的最佳计划。这种方法是基于最佳质量运输理论开发的。它也被视为在Wasserstein-GMM空间中的在线增强学习和近似动态编程方法，在该空间中，基于机器人的概率密度函数以及描述不断变化的环境信息的随时间变化的障碍物函数来定义新颖的价值功能。在非常大的机器人系统的路径计划问题上证明了该方法，其中近似工作空间的障碍布局通过机器人的观察结果逐渐更新，并与某些现有的最新方法相比。数值模拟结果表明，所提出的方法在平均行进距离和能量成本方面优于这些方法。
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.
