Adaptive Control Algorithm for Uncalibrated Position-based Visual Servoing

Abstract

The paper proposes an adaptive control algorithm for the uncalibrated position-based visual servoing to complete the tracking task of robot system. The desired and current poses of the end-effector are obtained by the camera and the system error is described in the camera coordination, which can avoid the work of hand-eye calibration. The connection matrix between the robot and the end-effector as well as the camera measurement bias are estimated together by the input/output data in the online control process, which can avoid the calibration between the robot terminal and the end-effector and reduce the influence of the measurement bias. By our proposed algorithm, the controller can update the model. Moreover, the control law and the estimation updated law are designed by the Lyapunov theory, which is proved that the system error and the parameter estimation error can be closed to zero. To verify the performance of the proposed controller, a numerical simulation experiment on a 6 degrees-of-freedom robot manipulator equipped with the end-effector and the eye-to-hand camera under our proposed control method is designed and presented.