Adaptive Visual Servoing of Robot Manipulators Using Uncalibrated Eye-in-hand Visual Feedback

摘要

This paper extends the method in our earlier work to control S target points to their desired positions on the image plane by using the visual feedback form an eye-in-hand camera. The controller is designed to cope with the case when the intrinsic and extrinsic parameters of the camera as well as the target positions are not calibrated. The controller employs the depth-independent interaction matrix to map the errors on the image plane onto the joint space. By using the depth-independent interaction matrix, 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. Based on the nonlinear robot dynamics, we prove asymptotic convergence of the image errors to zero by the Lyapunov theory. Experiments have been conducted to demonstrate the performance of the proposed controller.