Inverse Kinematics Solution of Robot Manipulator End- Effector Position Using Multi-Neural Networks

Abstract

This paper proposes multi-neural networks structure for solving the inverse kinematic problem of the robot manipulator end-effector position. It offers an opportunity to reduce substantially the error of the solution. This error frequently arises when only one neural network is used. In this structure, each neural network is a multilayer perceptron (MLP) trained by the backpropagation algorithm. The proposed approach verified by including it within an overall Cartesian trajectory planning system. This structure could produce the robot joint variables that are not included in the training data with an average error ±0.06º, and ±0.15º, ±0.05º for joint angles θ_1, θ_2 and θ_3 respectively. From the simulation results, the proposed structure of multi-neural network has superior performance for modeling the complex robot kinematics.