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The paper presents a new approach to design a control law that realizes a main task with a robotic system and simultaneously takes supplementary constraints into account. Classically, this is done by using the redundancy formalism. If the main task does not constrain all the motions of the robot, a secondary task can be achieved by using only the remaining degrees of freedom (DOF). We propose a new general method that frees up some of the DOF constrained by the main task in addition of the remaining DOF. The general idea is to enable the motions produced by the secondary control law that help the main task to be completed faster. The main advantage is to enhance the performance of the secondary task by enlarging the number of available DOF. In a formal framework, a projection operator is built which ensures that the secondary control law does not delay the completion of the main task. A control law can then be easily computed from the two tasks considered. Experiments that implement and validate this approach are presented. The visual servoing framework is used to position a six-DOF robot while simultaneously avoiding occlusions and joint limits. Index Terms--Gradient projection method, redundancy, robot control, sensor-feedback control, visual servoing.

In this paper, we investigate a range of image-based visual servo control algorithms for regulation of the position of a quadrotor aerial vehicle. The most promising control algorithms have been successfully implemented on an autonomous aerial vehicle and demonstrate excellent performance. Index Terms--Aerial robotic vehicle, visual servoing.

In this paper, we consider the problem of controlling a 6 DOF holonomic robot and a nonholonomic mobile robot from the projection of 3-D straight lines in the image plane of central catadioptric systems. A generic central catadioptric interaction matrix for the projection of 3-D straight lines is derived using an unifying imaging model valid for an entire class of cameras. This result is exploited to design an image-based control law that allows us to control the 6 DOF of a robotic arm. Then, the projected lines are exploited to control a nonholonomic robot. We show that as when considering a robotic arm, the control objectives are mainly based on catadioptric image feature and that local asymptotic convergence is guaranteed. Simulation results and real experiments with a 6 DOF eye-to-hand system and a mobile robot illustrate the control strategy. Index Terms--3-D straight lines, omnidirectional vision, visual servoing.

This paper focuses on the way to achieve accurate visual servoing tasks when the shape of the object being observed as well as the desired image are unknown. More precisely, we want to control the camera orientation with respect to the tangent plane at a certain object point corresponding to the center of a region of interest. We also want to observe this point at the principal point to fulfil a fixation task. A 3-D reconstruction phase must, therefore, be performed during the camera motion. Our approach is then close to the structure-from-motion problem. The reconstruction phase is based on the measurement of the 2-D motion in a region of interest and on the measurement of the camera velocity. Since the 2-D motion depends on the shape of the objects being observed, we introduce a unified motion model to cope both with planar and nonplanar objects. However, since this model is only an approximation, we propose two approaches to enlarge its domain of validity. The first is based on active vision, coupled with a 3-D reconstruction based on a continuous approach, and the second is based on statistical techniques of robust estimation, coupled with a 3-D reconstruction based on a discrete approach. Theoretical and experimental results compare both approaches. Index Terms--Active vision, 3-D reconstruction, dynamic vision, M-estimators, robust estimation, structure-from-motion, visual servoing.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2006.10.002 Byline: Anthony Remazeilles, Francois Chaumette Abstract: This paper addresses the problem of vision-based navigation and proposes an original control law to perform such navigation. The overall approach is based on an appearance-based representation of the environment, where the scene is directly defined in the sensor space by a database of images acquired during a learning phase. Within this context, a path to follow is described by a set of images, or image path extracted from the database. This image path is designed so as to provide enough information to control the robotic system. The central contribution of this paper is the closed-loop control law that drives the robot to its desired position using this image path. This control does not require either a global 3D reconstruction or a temporal planning step. Furthermore, the robot is not constrained to converge directly upon each image of the path, but chooses its trajectory automatically. We propose a process of qualitative visual servoing, enabling us to enlarge the convergence space towards positioning in a range within a confidence interval. We propose and use specific visual features which ensure that the robot navigates within the visibility path. Experimental simulations are given to show the effectiveness of this method for controlling the motion of a camera in three-dimensional environments (free-flying camera, or camera moving on a plane). In addition, experiments realized with a robotic arm observing a planar scene are also presented. Author Affiliation: IRISA, Campus de Beaulieu, 35042 Rennes Cedex, France Article History: Received 28 November 2005; Revised 2 October 2006; Accepted 9 October 2006

Classical sensor-based approaches tend to constrain all the degrees of freedom of a robot during the execution of a task. In this paper, a new solution is proposed. The key idea is to divide the global full-constraining task into several subtasks, which can be applied or inactivated to take into account potential constraints of the environment. Far from any constraint, the robot moves according to the full task. When it comes closer to a configuration to avoid, a higher level controller removes one or several subtasks, and activates them again when the constraint is avoided. The last controller ensures the convergence at the global level by introducing some look-ahead capabilities when a local minimum is reached. The robot accomplishes the global task by automatically sequencing sensor-based tasks, obstacle avoidance, and short deliberative phases. In this paper, a complete solution to implement this idea is proposed, along with several experiments that prove the validity of this approach. Index Terms--Avoidance, planning, redundancy, sensor-based control, tasks sequencing, visual servoing.