Dynamic eye-in-hand visual servoing with unknown target positions

Abstract

This paper presents a new adaptive controller for a robot manipulator by using the visual feedback from an eye-in-hand camera. The controller is designed to cope with the case when the target 3-D positions are unknown. The controller employs the depth-independent interaction matrix to map the image errors onto the joint inputs of the manipulator. A new algorithm is developed to estimate the unknown parameters on-line. The Lyapunov theory is used to prove asymptotic stability of the proposed controller based on the nonlinear dynamics of the robot manipulator. Experiments have been conducted to demonstrate the performance of the proposed controller. © 2010 Springer-Verlag Berlin Heidelberg.