ARTIFICIAL NEURAL NETWORK APPLICATION FOR INVERSE KINEMATIC SOLUTION OF A 5-DOF ROBOTIC ARM

Abstract

The inverse kinematic solution is one of the major problems for real time application of robot control. The conventional solution methods such as geometric, algebraic and numerical iterative are insufficient and slow in the inverse kinematic solution. Recently, different solution methods based on artificial intelligence techniques have been developed in order to solve inverse kinematics of especially redundant robots and to reduce the solution time. In this study, an Artificial Neural Network (ANN) model was designed to solve the inverse kinematics of the 5 degrees of freedom (DOF) robotic arm. Network was trained using database obtained from the experimental setup and ANN model was validated by experimental works. The Neural Network (NN) calculated the joint angles with high accuracy with respect to the given (x, y, z) Cartesian coordinates. The comparison results show that the proposed NN has a high correlation (R=0.9988) and superior performance for solving inverse kinematics of the robotic arm.