Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot

Abstract

In order to solve the overlap link constraint, trapped movement, and computational cost problem in current IK model, this paper proposes a new coupled spiking neural network (CSNN) model which is combined with artificial neural network (ANN). Several references of end of effector's movement will be generated as training model of ANN. Current joint positions and angle values, movement direction and distance will be the input data. Angular velocity of every joint will be the output data. However, ANN structure and number of references will be minimized. As an alternative, CSNN will be implemented, where one joint angle is represented by a coupled neurons interconnected to each others. CSNN has feedback input from the current condition of arm robot, and its output will be combined with ANN's output. CSNN interconnection will be optimized using steady state evolutionary algorithm with several epoch. The proposed model is implemented to simulate multiple link planar robot. The result shows the effectiveness of the proposed model which succeeded in several trajectory tests with minimum computational cost.