Dynamic Visual Servoing of Robots Using Uncalibrated Eye-in-hand Visual Feedback

Abstract

This paper presents a new adaptive controller for a robot manipulator to control position of projections of unknown targets using the visual feedback from an eye-in-hand camera. The controller is designed to cope with the case when the intrinsic and extrinsic parameters of the camera are not calibrated. The controller employs the depth-independent image Jacobian to map the errors on the image plane onto the joint space. By using the depth-independent image Jacobian, it is possible to linearly parameterize the unknown camera parameters and the unknown coordinates of the target points in the closed loop dynamics of the system. A new algorithm is developed to estimate unknown parameters on-line. By minimizing the errors between the real and estimated projections of the target points on the image plane, this new adaptive algorithm can guarantee the convergence of the estimated parameters to the real values. With a full consideration of dynamic responses of the root manipulator, we employed the Lyapunov method to prove asymptotic convergence of the image errors. Experiments have been conducted to demonstrate the performance of the proposed controller