论文标题
使用集成增强的RNN的冗余机器人操纵器的运动学分辨率
Kinematic Resolutions of Redundant Robot Manipulators using Integration-Enhanced RNNs
论文作者
论文摘要
最近，引入了描述冗余机器人操纵器的跟踪操作的时变二次编程（QP）框架，以处理许多机器人控制任务的运动分辨率。基于这种时间变化的QP框架的概括，提出了两个方案，即重复运动方案和混合扭矩方案。但是，当冗余机器人操纵器执行跟踪任务时，测量噪声是不可避免的。为了解决这个问题，本文提出了一种新颖的集成增强的复发性神经网络（IE-RNN）。与上述两个方案相关联，IE-RNN可以准确地完成跟踪任务。理论分析和仿真结果都证明，在不同种类的测量噪声下，IE-RNN的残余误差可以收敛到零。此外，实践实验是精心进行的，以验证所提出的IE-RNN的出色收敛性和强大的鲁棒性。
Recently, a time-varying quadratic programming (QP) framework that describes the tracking operations of redundant robot manipulators is introduced to handle the kinematic resolutions of many robot control tasks. Based on the generalization of such a time-varying QP framework, two schemes, i.e., the Repetitive Motion Scheme and the Hybrid Torque Scheme, are proposed. However, measurement noises are unavoidable when a redundant robot manipulator is executing a tracking task. To solve this problem, a novel integration-enhanced recurrent neural network (IE-RNN) is proposed in this paper. Associating with the aforementioned two schemes, the tracking task can be accurately completed by IE-RNN. Both theoretical analyses and simulations results prove that the residual errors of IE-RNN can converge to zero under different kinds of measurement noises. Moreover, practical experiments are elaborately made to verify the excellent convergence and strong robustness properties of the proposed IE-RNN.
