Optimization design for visual servoing in eye-in-hand robotics

This paper presents an attractive image-based visual servoing approach for eye-in-hand robotic systems. The major contribution of this work is in devising an optimization controller to control the velocity of the camera and GMC (generic model control) is introduced to confirm the output trajectory of the system. Then an adaptive law is proposed to estimate the unknown depth of the feature points of the selected object. The asymptotic convergence of the image errors to zero is proved by the Lyapunov theory. The simulation results are presented for 6 degrees of freedom (DOF) eye-in-hand system, which clearly show the validity of the proposed approach.