Abstract This paper proposes a learning framework for solving the inverse kinematics (IK) problem of high DOF redundant manipulators. These have several possible combinations to get the end effector (EE) pose. Therefore, for a given EE pose, several joint angle vectors can be associated. However, for a given EE pose, if a set of joint angles is parameterized, the IK problem of redundant manipulators can be reduced to that of non-redundant ones, such that the closed-form analytical methods developed for non-redundant manipulators can be applied to obtain the IK solution. In this paper, some redundant manipulator’s joints are parameterized through workspace clustering and configuration space clustering of the redundant manipulator. The growing neural gas network (GNG) is used for workspace clustering while a neighborhood function (NF) is introduced in configuration space clustering. The results obtained by performing a series of simulations and experiments on redundant manipulators show the effectiveness of the proposed approach.

中文翻译：

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高自由度冗余机械手逆运动学的学习框架

摘要 本文提出了一种解决高自由度冗余机械手逆运动学（IK）问题的学习框架。这些有几种可能的组合来获得末端执行器 (EE) 姿势。因此，对于给定的 EE 姿势，可以关联多个关节角度向量。然而，对于给定的 EE 姿态，如果一组关节角度被参数化，冗余机械臂的 IK 问题可以减少到非冗余机械臂的问题，这样为非冗余机械臂开发的封闭形式分析方法可以用于获得 IK 解决方案。本文通过冗余机械手的工作空间聚类和配置空间聚类对一些冗余机械手的关节进行参数化。不断增长的神经气体网络 (GNG) 用于工作区聚类，而邻域函数 (NF) 被引入配置空间聚类。通过对冗余机械手进行一系列仿真和实验获得的结果表明了所提出方法的有效性。