Uncalibrated visual servoing technique using large residual

摘要

This paper addresses a moving target tracking system with estimation of the image Jacobian without knowledge of camera configuration or robot kinematics. We propose an efficient algorithm to track a moving target using a numerical method and apply this algorithm to a robot system. The robot system is controlled using the nonlinear least squares optimization technique. The Full Newton's method and the secant approximation method are used to calculate joint angles. In this paper, large residuals of joint values are calculated using the secant approximation method. The image Jacobian is then estimated using the recursive least squares (RLS) algorithm. In addition, the velocity of the target influences the performance of the system because of the delay to process an image. Thus, we present a motion prediction algorithm for a moving target. The target position on the image plane is predicted using the autoregressive model (ARM). We then compare the performance of the small residual case with the performance of the large residual case using simulation. The experimental results show the improved performance using this prediction algorithm.