Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems
Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems
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DOI：
10.1109/tcns.2021.3097306
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发表时间：
2020-03
影响因子：
4.2
通讯作者：
Pingping Zhu;Chang Liu;S. Ferrari
中科院分区：
文献类型：
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作者：
Pingping Zhu;Chang Liu;S. Ferrari
由许多合作代理组成的自治系统有可能实现长时间的任务和数据收集，这对理解空间和时间变化环境中的各种现象至关重要。本文提出的自适应分布式最优控制方法将在线近似动态规划扩展到超大规模机器人（VLSR）系统，这些系统必须运行并适应高度不确定和多变的环境。最优质量运输理论表明，在Wasserstein-Gaussian混合模型空间中，VLSR系统的成本可以由机器人分布和动态环境地图的值函数表示。该方法被证明在一个合作的路径规划问题，在环境中的障碍物的知识随着时间的推移逐步变化的基础上，在现场测量。数值模拟表明，该方法显着优于现有的方法，找到一个近似的最佳解决方案，避免障碍物，并满足所需的最终机器人分布使用最小的能量。
Autonomous systems comprised of many cooperative agents have the potential for enabling long-duration tasks and data collection critical to the understanding of a wide range of phenomena in spatially and temporally variable environments. The adaptive distributed optimal control approach presented in this article extends online approximate dynamic programming to very-large-scale robotics (VLSR) systems that must operate and adapt to highly uncertain and variable environments. Optimal mass transport theory is used to show that, in the Wasserstein–Gaussian mixture model space, the VLSR system's cost to go can be represented by a value functional of the robot distribution and dynamic environmental maps. The approach is demonstrated on a cooperative path planning problem in which knowledge of the obstacles in the environment changes incrementally over time based on in situ measurements. Numerical simulations show that the proposed approach significantly outperforms existing methods by finding an approximately optimal solution that avoids obstacles and meets a desired final robot distribution using minimum energy.