Precise positioning of binocular eye-to-hand robotic manipulators

This article addresses the visual servoing of a rigid robotic manipulator equipped with a binocular vision system in eye-to-hand configuration. The control goal is to move the robot end-effector to a visually determined target position precisely without knowing the precise camera model. Many vision-based robotic positioning systems have been successfully implemented and validated by supporting experimental results. Nevertheless, this research aims at providing stability analysis for a class of robotic set-point control systems employing image-based feedback laws. Specifically, by exploring epipolar geometry of the binocular vision system, a binocular visual constraint is found to assist in establishing stability property of the feedback system. Any three-degree-of-freedom positioning task, if satisfying appropriate conditions with the image-based encoding approach, can be encoded in such a way that the encoded error, when driven to zero, implies that the original task has been accomplished with precision. The corresponding image-based control law is proposed to drive the encoded error to zero. The overall closed-loop system is exponentially stable provided that the binocular model imprecision is small.