Hybrid Neural Network Based Prediction of Inverse Kinematics of Robot Manipulator

Abstract

The fundamental of the inverse kinematics of robot manipulator is to determine the joint variables for a given Cartesian position and orientation of an end effector. Conventional methods to solve inverse kinematics such as geometric, iterative and algebraic are complex for redundant manipulators. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Although artificial neural network (ANN) can be gainfully used to yield the desired results, but the gradient descent learning algorithm does not have ability to search for global optimum and it gives a slow convergence rate. This paper proposes structuring ANN with hybridization of Particle Swarm Optimization to solve the inverse kinematics of 6R robot manipulator. An investigation has been made on accuracies of adopted algorithm. The ANN model used is multi-layered perceptron neural network (MLPNN) with back-propagation (BP) algorithm which is compared with hybrid multi layered perceptron particle swarm optimization (MLPPSO). An attempt has been made to find the best ANN configuration for the problem. It has been observed that MLPPSO gives a faster convergence rate and improves the problem of trapping in local minima. It is found that MLPPSO gives better result and minimum error as compared to MLPBP.