Forward kinematic-like neural network for solving the 3D reaching inverse kinematics problems

Abstract

This paper presents the inverse kinematic solutions based on neural networks. General neural network approaches use data of the end-effector positions as an input and angle joints as an output to train the neural network for mapping the input to the output. However, the proposed method creates the custom networks from forward kinematic equations. This special structure makes the network like a position finder with ability to automatically adjust angle joints until the end-effector reaches the desired position by backpropagation with variable learning rate algorithm. Then, the solutions of angles can be found from the final weights and bias values. Moreover, the proposed network use less number of neurons and amount of the solution space is not depend on the training data. Finally, to evaluate the performance algorithm, the MATLAB Program is used to demonstrate a 4-DOF robotic arm movement in 3-dimensional. As a result, the proposed algorithm can help a robotic arm move to the desired position (3D reaching) quickly and correctly.