We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medical (e.g. surgery assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state of the art and propose future directions within the proposed classification.

To cite this version:Youcef Mezouar, François Chaumette. Path planning for robust image-based control. IEEE Transactions on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE), 2002, 18 (4), pp.534-549. 534 IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 18, NO. 4, PP. 534-549, AUGUST 2002 Path Planning for Robust Image-based Control Youcef Mezouar and François Chaumette, Member, IEEE 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 approach, it is not obvious to introduce any constraint in the realized trajectories and 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 the robustness with respect to modeling errors. The proposed method is based on the potential field approach and is applied when 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.