Vision Based Robot Manipulator Control with Neural Modeling of Jacobian Matrix

Visual servoing system is a system to control a robot by visual feedback so that robot drives from any arbitrary start position to the target positions. Various ways، including control by using model of the robot، designing controller directly، and using Jacobian matrix have been studied. Since there is not access to model of robot and obtaining a model of robot would be difficult and time consuming، in many cases، the control law is obtained using Jacobian matrix. In this paper، inverse of Jacobian matrix is approximated using artificial neural networks. The approximated neural models are used in control law directly. For each degree of freedom of the robot manipulator، a two-layer feedforward neural network is considered. The distance between end-effector and target along the x-axis and y-axis، and the shoulder joint coordinates along the x-axis and y-axis are the inputs of each of the networks and the outputs are the fraction of the related robot joint changes to the image features changes (the elements of the inverse of Jacobian matrix). The proposed method has been implemented on a real robot manipulator. The experimental results show that the proposed control system can move the end-effector to different target positions in workspace with good accuracy.