Automatic Generation of Feedback Stabilizable State Space for Non-holonomic Mobile Robots
Automatic Generation of Feedback Stabilizable State Space for Non-holonomic Mobile Robots
复制标题
非完整移动机器人反馈稳定状态空间的自动生成
DOI：
10.1109/iciprob54042.2022.9798729
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kobayashi Yuichi
中科院分区：
文献类型：
--
作者：
Nakahara Ken;Kobayashi Yuichi
机器人控制问题的学习方法通常需要大量的试验，这对于使这些方法得到更广泛的应用至关重要。作为提高学习效率的一种手段，引入控制理论中发展的方法和思想是有希望的。本文旨在将非线性控制方法的思想引入学习方法中，提出了一种获取状态空间的方法，该状态空间允许在非完整约束的两轮移动机器人上实现控制目标。在所提出的框架中，假设传感器的知识无法提前获得。提出了一种应对非完整控制器方案的自适应网格分布算法。实验证实该方法能够使机器人稳定到达目标点。该方法提出了一种有效整合机器学习和控制理论的思想，有潜力成为一种统一的学习方法，可以以较少的样本或试验应用于各种控制目标。
Learning approaches to robot control problems generally require a lot of trials, which is crucial to make the approaches available in wider applications. As a means to improve learning efficiency, it is promising to introduce methodologies and ideas developed in the control theory. Aiming at introducing an idea of nonlinear control method to learning approach, this paper presents an acquisition of a state space that allows control to reach a target for the two-wheeled mobile robot with non-holonomic constraints. In the proposed framework, it is assumed that knowledge of the sensor is not available in advance. An adaptive grid distribution algorithm to cope with a non-holonomic controller scheme is proposed. It was experimentally confirmed that the robot could reach the target point stably by the proposed method. The proposed method presents an idea to effectively integrate machine learning and control theory and it has the potential to become a unified learning method that can be applied to various control targets with fewer samples or trials.
DOI：
10.5555/1756006.1953033
发表时间：
2010-03
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
Evangelos A. Theodorou;J. Buchli;S. Schaal
通讯作者：
Evangelos A. Theodorou;J. Buchli;S. Schaal
用于组织视觉运动协调的基于扩散的学习理论
影响因子：
1.9
作者：
Zhiwei Luo;Masami Ito
通讯作者：
Masami Ito