Lyapunov-stable eye-in-hand kinematic visual servoing with unstructured static feature points

In this paper, we address the visual servoing problem of robot manipulators with eye-in-hand cameras. To servo-control the image position of a feature point, traditional image-based controllers require the computation of the point´s position vector with respect to the camera´s frame. However, when the point´s location is uncertain, the stability of traditional visual servoing controllers can not be rigorously guaranteed. To contribute to this problem, in this paper we present two new kinematic image-based controllers that do not require the exact location of static features. The first controller is a depth-free method that uses the camera´s calibration matrix and visual feedback to compute a quasi-position vector of the feature point. The second controller uses adaptive control techniques to iteratively estimate the calibration matrix and the point´s position vector. We prove the stability of both servo-controllers using Lyapunov theory, and present experimental results to evaluate its performance.