Adaptive regulation of robot joint velocity in uncalibrated visual servoing

We propose an efficient tracking algorithm for uncalibrated visual tracking system based on image-based visual servo control. We present an efficient algorithm to track a target using a numerical method and a method to regulate the joint velocities for the stability of the system efficiently. 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 the joint angles. Large residuals of joint values are calculated using the secant approximation method. We define a secondary cost function to regulate the joint velocities using the adaptive limit of joint velocity, and propose the modified iterative forms of the joint values. The image Jacobian is then estimated using the recursive least squares (RLS) algorithm. We then apply this method in a visual servoing application.