This paper concerns the stability analysis of a new class of model-free visual servoing methods. These methods are "model-free" since they are based on the estimation of the relative camera orientation between two views of an object without knowing its 3-D model. The visual servoing is decoupled by controlling the rotation of the camera separately from the rest of the system. The way the remaining degrees of freedom are controlled differentiates the methods within the class. For all the methods of the class, the robustness with respect to both camera and hand-eye calibration errors can be analytically studied. In some cases, necessary and sufficient conditions can be found not only for the local asymptotic stability but also for the global asymptotic stability. In the other cases, simple conditions on the calibration errors are sufficient to ensure the global asymptotic stability of the control law. In addition to the theoretical proof of the stability, the experimental results prove the validity of the control strategy proposed in the paper.

In this paper, the classical visual servoing techniques have been extended to the use of several cameras observing different parts of an object. The multi-camera visual servoing has been designed as a part of the task function approach. The particular choice of the task function allows one to simplify the design of the control law and the stability analysis. A positioning task on a cumbersome object has been realized using 2D and 2 1/2 D visual servoings with two cameras, mounted on a manipulator robot, and observing two different parts of the object.

We propose an approach to vision-based robot control, called 2 1/2 D visual servoing, which avoids the respective drawbacks of classical position-based and image-based visual servoing. Contrary to the position-based visual servoing, our scheme does not need any geometric three-dimensional model of the object. Furthermore and contrary to image-based visual servoing, our approach ensures the convergence of the control law in the whole task space. 2 1/2 D visual servoing is based on the estimation of the partial camera displacement from the current to the desired camera poses at each iteration of the control law. Visual features and data extracted from the partial displacement allow us to design a decoupled control law controlling the six camera DOFs. The robustness of our visual servoing scheme with respect to camera calibration errors is also analyzed: the necessary and sufficient conditions for local asymptotic stability are easily obtained. Then, due to the simple structure of the system, sufficient conditions for global asymptotic stability are established. Finally, experimental results with an eye-in-hand robotic system confirm the improvement in the stability and convergence domain of the 2 1/2 D visual servoing with respect to classical position-based and image-based visual servoing.

In this paper we propose a new vision-based robot control approach halfway between the classical position-based and image-based visual servoings. It allows to avoid their respective disadvantages. The homography between some planar feature points extracted from two images (corresponding to the current and desired camera poses) is computed at each iteration. Then, an approximate partial-pose, where the translational term is known only up to a scale factor, is deduced, from which can be designed a closed-loop control law controlling the six camera DOF. Contrarily to the position-based visual servoing, our scheme does not need any geometric 3D model of the object. Furthermore and contrarily to the image-based visual servoing, our approach ensures the convergence of the control law in all the task space.

We propose a simple and efficient control algorithm that combines visual servo control and force feedback within the impedance control approach. The control scheme involves, at the low level, a position based impedance controller with an external force sensor feedback loop. The reference trajectory fed to this impedance controller is generated online by a vision based control loop. In spite of its simplicity, this approach provides satisfactory experimental behavior. Peg in hole insertion experiments involving large initial errors, are performed using a 7 axis robot manipulator without any computation of the peg trajectory.

We have developed a robotic system to assist doctors when they are moving ultrasonic probes on the patient's skin while exerting a given effort. The probes are used to monitor arteries for cardiovascular disease prevention, namely to reconstruct the 3D profile of arteries. A preliminary feasibility study making use of an industrial robot has been made to validate the force control scheme. It has proven the interest of the robotized approach for such a medical application. In order to comply with safety constraints, a dedicated robotic system HIPPOCRATE has been designed. The paper describes the arm and the controller architectures, with emphasis on design strategies selected to meet safety requirements.

In this paper, the robustness of a new visual servoing scheme with respect to camera calibration errors is analyzed. This scheme, called 2D 1/2 visual servoing, is based on the estimation of the partial camera displacement from the current to the desired camera poses at each iteration of the control law. Visual features and data extracted from the partial displacement allow as to design a decoupled control law controlling the six camera DOF. The necessary and sufficient conditions for local asymptotic stability in presence of camera calibration errors are easily obtained. Then, thanks to the simple structure of the system, sufficient conditions for global asymptotic stability are proposed. Finally, experimental results show the validity of our approach and its robustness not only with respect camera calibration errors but also to robot calibration errors.

We study the income of an integrated robotics and medical device, using a force-control law to measure arterial distensibility and to reconstruct in 3D the atheromatous plaques in the arteries. During the medical control, sonographer holds the ultrasound probe attached on the end-effector of the robot and brings it onto the skin of the patient. Then, he can simply get an accurate measure of the arterial distension with a given applied force. He can also teach the robot a scan trajectory in order to reconstruct in 3D the artery. For this reconstruction the robot executes the learnt trajectory step by step, with a programmable and constant force on the artery, recording the Cartesian coordinates of the artery sections. At the same time, the control device synchronizes the recorded images on the patient heartbeat. The very precise set of images and the medical control can be reproduced later under the same conditions. The results are very encouraging and promising.