Inverse Kinematic Solution of Robot Manipulator Using Hybrid Neural Network

Abstract

Inverse kinematics of robot manipulator is to determine the joint variables for a given Cartesian position and orientation of an end effector. 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 slow convergence rate. This paper proposes structured ANN with hybridization of Gravitational Search Algorithm to solve inverse kinematics of 6R PUMA robot manipulator. The ANN model used is multi-layered perceptron neural network (MLPNN) with back-propagation (BP) algorithm which is compared with hybrid multi layered perceptron gravitational search algorithm (MLPGSA). An attempt has been made to find the best ANN configuration for the problem. It has been observed that MLPGSA gives faster convergence rate and improves the problem of trapping in local minima. It is found that MLPGSA gives better result and minimum error as compared to MLPBP.