Integral reinforcement learning‐based approximate minimum time‐energy path planning in an unknown environment
Integral reinforcement learning‐based approximate minimum time‐energy path planning in an unknown environment
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DOI：
10.1002/rnc.5122
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发表时间：
2020-10
影响因子：
3.9
通讯作者：
Chenyuan He;Yan Wan;Y. Gu;F. Lewis
中科院分区：
文献类型：
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作者：
Chenyuan He;Yan Wan;Y. Gu;F. Lewis
路径规划是许多机器人应用中的基本和关键任务。对于能量受限的机器人平台，路径规划解决方案需要最少的到达时间和最小的能耗。风况等不确定环境对设计有效的最短时间-能量路径规划解决方案提出了挑战。在这篇文章中，我们使用积分强化学习（IRL）在连续状态和控制输入空间中开发了一种最小时间能量路径规划解决方案。为了提供一个基线解决方案的性能评估所提出的解决方案，我们首先开发了一个理论分析的最小时间能量路径规划问题在一个已知的环境中使用庞特里亚金的最小值原则。然后，我们提供了一个在线的自适应解决方案，在一个未知的环境中使用IRL。这是通过将最小时间能量问题转化为近似最小时间能量问题，然后开发基于IRL的最优控制策略来实现的。证明了基于IRL的最优控制策略的收敛性。仿真研究开发的理论分析和建议的IRL为基础的算法进行比较。
Path planning is a fundamental and critical task in many robotic applications. For energy‐constrained robot platforms, path planning solutions are desired with minimum time arrivals and minimal energy consumption. Uncertain environments, such as wind conditions, pose challenges to the design of effective minimum time‐energy path planning solutions. In this article, we develop a minimum time‐energy path planning solution in continuous state and control input spaces using integral reinforcement learning (IRL). To provide a baseline solution for the performance evaluation of the proposed solution, we first develop a theoretical analysis for the minimum time‐energy path planning problem in a known environment using the Pontryagin's minimum principle. We then provide an online adaptive solution in an unknown environment using IRL. This is done through transforming the minimum time‐energy problem to an approximate minimum time‐energy problem and then developing an IRL‐based optimal control strategy. Convergence of the IRL‐based optimal control strategy is proven. Simulation studies are developed to compare the theoretical analysis and the proposed IRL‐based algorithm.