Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks

Abstract

This paper presents novel approach for a visual servoing application of six axis robotic arm. Basic image-processing techniques were used for object recognition and position determination of robotic arm. The inverse kinematics solution of the robot arm was performed with artificial neural networks. Afterwards the robot's inverse kinematics solution was completed, the determined joint-angle values were used to control the robot arm. Performance of radial basis function network (RBF) and multilayer perceptron (MLP) were also compared.