Nonlinear tracking control for image-based visual servoing with uncalibrated stereo cameras

Abstract

This paper considers the problem of uncertain camera pose and camera parameters for a 3-degree-of-freedom (DOF) robot manipulator in nonlinear visual servoing tracking control. To solve this problem, the typical Kalman filter (KF) algorithm is designed to estimate the image Jacobian matrix online, which can reduce the system noises to improve the robustness of the control system. Visual optimal feedback controller is developed to precisely track the desired position of the robot manipulator. In addition, stereo cameras are incorporated into the robot manipulator system such that the tracking errors in both camera image frame and robot base frame can simultaneously converge to zero. Experimental results are included to illustrate the effectiveness of the proposed approach.