Inverse kinematics solution using neural networks from forward kinematics equations

Abstract

This paper presents the inverse kinematics solution using the neural network for a robotic arm in 3-dimension. This paper creates neural networks to represent x, y and z position of the end-effector in the forward kinematics equations. The structure of the network has 4 layers; input layer, 2 hidden layers, and output layer. The input and output layers are defined as robotic arm angle and position of the end-effector, respectively. Then, the network updates the weights by the backpropagation with variable learning rate algorithm until reaching criteria that the output of the network is equal to the desired positions. Finally, the inverse kinematics solution is defined by the optimal weights of the network. To evaluate the performance algorithm, the MATLAB Program is used to demonstrate the robotic arm movement in 3-dimension. As a result, the proposed algorithm can help the robotic arm move to the desired position quickly and correctly.