Inverse Kinematics Solution of Hyper-Redundant Robot Manipulators Using Wavelet-Based Neural Network

In this paper an efficient method for solving the inverse kinematics of Hyper-Redundant Robots is presented. The method is based on a flexible curve named backbone. Three approaches are presented to solve the inverse kinematics. In the first approach the "assumed mode method" is used. In the second approach, first an optimum problem using the "calculus of variations" and minimization of bending of backbone curve is defined. Then a numerical method named "resolved rate motion" is used for solving the obtained governing equations. In the third approach, a neural network based on wavelets is used to increase accuracy while reducing the computation time. Simulation results show that through the application of this network the learning time is significantly reduced in comparison to the conventional neural networks such as back propagation type networks.