基于集成增强RNN的冗余度机器人运动学求解
Kinematic Resolutions of Redundant Robot Manipulators using Integration-Enhanced RNNs
摘要：
最近，一个时变二次规划（QP）框架，描述冗余机器人操作器的跟踪操作被引入到处理许多机器人控制任务的运动学分辨率。基于这种时变QP框架的推广，两种方案，即，重复运动方案和混合转矩方案。然而，测量噪声是不可避免的冗余度机器人操作器执行跟踪任务时。为了解决这个问题，本文提出了一种新的集成增强型递归神经网络（IE-RNN）。结合上述两种方案，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了IE-RNN在不同的测量噪声下都能收敛到零。实验结果表明，该算法具有良好的收敛性和较强的鲁棒性。
英文摘要：
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.
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