Practical inverse kinematics of a kinematically redundant robot using a neural network

Abstract

In solving inverse kinematics problems, traditional methods such as RMRC (resolved motion rate control) and the IKM (inverse kinematic method) are mostly complicated and time-consuming. Using a neural network, however, a practical algorithm for obtaining accurate joint angles in a much shorter time is possible. The neural network approach assumes a transfer function between inputs and outputs and trains the network to satisfy the representative input-output pairs in the least squares sense. First, a test of the appropriateness of the neural network method is performed for the case of a planar two degrees of freedom (DOF) robot. Then the neural network method is employed to find three joint angles of a planar 3-DOF robot maximizing local manipulability. In this algorithm, the proximal redundant joint angle is determined from a neural network and then the remaining joint angles are determined from analytical functions. The results from this method compare favourably with those from the other two traditional methods.