Solutions of kinematics of robot manipulators using a Kohonen self-organizing neural network

Abstract

Kohonen self-organizing neural network is used to solve the forward kinematics problems of robot manipulators. Through competition learning, neurons learn their distribution in the training phase. In sequel, the nonlinear mapping has been obtained by proper calibration of training results. The proposed method is based on the unsupervised learning which does not rely on the knowledge of process model information. Simulation results have effectiveness of the proposed method for a two degree planar robot manipulator.