A recurrent neural network approach for visual servoing of manipulators

Abstract

The control of robotic manipulators has received significant amount of attention in the control and robotics communities. In this paper, we investigate the kinematic control of a manipulator with an eye-in-hand camera, which is referred to as visual servoing. The visual servoing problem is formulated as a constrained optimization problem, which is then solved via a recurrent neural network. By this approach, the visual servoing with respect to a static point object is achieved with the feature coordinate errors in the image space converging to zero. Besides, joint angle and velocity limits of the manipulator are satisfied, which thus enhances the safety of the manipulator during the visual servoing process. The performance of the approach is guaranteed via theoretical analysis and verified via a simulative example.