Inverse kinematics learning for redundant robot manipulators with blending of support vector regression machines

Abstract

Redundant robot manipulator is a kind of robot arm having more degrees-of-freedom (DOF) than required for a given task. Due to the extra DOF, it can be used to accomplish many complicated tasks, such as dexterous manipulation, obstacle avoidance, singularity avoidance, collision free, etc. However, modeling the inverse kinematics of such kind of robot manipulator remains challenging due to its property of null space motion. In this paper, support vector regression (SVR) is implemented to solve the inverse kinematics problem of redundant robotic manipulators. To further improve the prediction accuracy of SVR, a special machine learning technique called blending is used in this work. The proposed approach is verified in MATLAB with a seven DOF Mitsubishi PA-10 robot and the simulation results have proved its high accuracy and effectiveness.