Path planning for robust image-based control

Abstract

Vision feedback control loop techniques are efficient for a large class of applications, but they come up against difficulties when the initial and desired robot positions are distant. Classical approaches are based on the regulation to zero of an error function computed from the current measurement and a constant desired one. By using such an approach, it is not obvious how to introduce any constraint in the realized trajectories or to ensure the convergence for all the initial configurations. In this paper, we propose a new approach to resolve these difficulties by coupling path planning in image space and image-based control. Constraints such that the object remains in the camera field of view or the robot avoids its joint limits can be taken into account at the task planning level. Furthermore, by using this approach, current measurements always remain close to their desired value, and a control by image-based servoing ensures robustness with respect to modeling errors. The proposed method is based on the potential field approach and is applied whether the object shape and dimensions are known or not, and when the calibration parameters of the camera are well or badly estimated. Finally, real-time experimental results using an eye-in-hand robotic system are presented and confirm the validity of our approach.