This paper presents a new adaptive controller for image-based dynamic control of a robot manipulator using a fixed camera whose intrinsic and extrinsic parameters are not known. To map the visual signals onto the joints of the robot manipulator, this paper proposes a depth-independent interaction matrix, which differs from the traditional interaction matrix in that it does not depend on the depths of the feature points. Using the depth-independent interaction matrix makes the unknown camera parameters appear linearly in the closed-loop dynamics so that a new algorithm is developed to estimate their values on-line. This adaptive algorithm combines the Slotine-Li method with on-line minimization of the errors between the real and estimated projections of the feature points on the image plane. 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 verify the performance of the proposed controller. The results demonstrated good convergence of the image errors.

This paper presents a new adaptive controller for dynamic image-based visual servoing of a robot manipulator when the intrinsic and extrinsic parameters of the camera are not calibrated. To cope with nonlinear dependence of the image Jacobian on the unknown parameters, this controller employs a matrix called nonscaled image Jacobian which does not depend on the scale factors determined by the depths of feature points. By removing the scale factors, the camera parameters appear linearly in the close-loop dynamics so that a new algorithm, different from Slotine and Li’s, is developed to estimate their values on-line. In the parameter adaptation, in addition to the regressor term, our algorithm also uses the errors between the real and estimated projections of the feature points on the image plane so as to guarantee the convergence of the estimated parameters to the real values up to a scale. A new Lyapunov function is introduced to prove asymptotic convergence of the image errors based on the robot dynamics. Experiments have been conducted to demonstrate the performance of the proposed controller.

This paper presents a new adaptive controller for dynamic image-based visual servoing of a robot manipulator when the intrinsic and extrinsic parameters of the camera are not calibrated. To cope with nonlinear dependence of the image Jacobian on the unknown parameters, this controller employs a matrix called nonscaled image Jacobian which does not depend on the scale factors determined by the depths of feature points. By removing the scale factors, the camera parameters appear linearly in the close-loop dynamics so that a new algorithm, different from Slotine and Li's, is developed to estimate their values on-line. In the parameter adaptation, in addition to the regressor term, our algorithm also uses the errors between the real and estimated projections of the feature points on the image plane so as to guarantee the convergence of the estimated parameters to the real values up to a scale. A new Lyapunov function is introduced to prove asymptotic convergence of the image errors based on the robot dynamics. Experiments have been conducted to demonstrate the performance of the proposed controller.