A pose estimation method applied to an indoor mobile robot equipped with a laser range-finder that periodically delivers a range scan of the environment is presented. The method is based on the fusion between the range readings delivered by the laser and the feedback control input computed to constrain the robot to move in its free workspace. The relative motion of the robot between two successive sampling times of the control loop is estimated:(1) computing a prediction by comparing the local maps built at these instants, (2) integrating the feedback control input computed from the laser readings to produce the desired motion and (3) performing a fusion between these two predictions. These computations are performed at sampling rate during the robot's evolution and are embedded in a probabilistic approach taking into account the uncertainties associated to the laser readings. Experimental results are presented validating the proposed method.

In this paper we propose a new approach for visual-servoing with respect to a set of 3D straight lines. The main difference with respect to previous approaches is that the new scheme can be used with a zooming camera or even if the reference image has been learned with a different camera. The zoom is particularly useful in order to keep the visual features in the camera field of view and/or to bound their size in the image reducing the influence of noise on features extraction. Experiments with a zooming camera have validated the vision-based control law.

A bounded-error methodology applied to estimate relative displacements of an indoor mobile robot equipped with a laser range-finder that periodically delivers a range scan of the environment is presented. The method is based on the fusion between the range readings delivered by the laser and the feedback control input used to constraint the robot to move in its free workspace. A weighted least square estimation method, previously published (Victorino, Rives, and Borrelly, 2002), is recalled and a comparison between the proposed bounded-error methodology and the least square method is presented. Experimental results validate the application of the bounded-error estimation method.

This work presents a new methodology for navigating mobile robots in unknown environments based on the robot's perception. The interaction between the robot and the environment is modelled, and control laws are designed so that the robot is constrained to move on the Voronoi diagram of the environment. The proposed method enables the robot to explore an unknown scene without any reference trajectory or any prior knowledge about the environment and avoiding the obstacles. We apply this approach on an indoor mobile robot using a 2D laser range finder. We show that the displacement errors remain bounded when the movements of the robot are constrained by sensor based control laws, that results in a precise self-localization and reliable map building. Experimental results shown in this article validate this methodology.

Addresses the problem of simultaneous localization and map building for a mobile robot moving in an unknown environment. We show that the robustness of the techniques currently used can be notably enhanced when they are embedded in a sensor-based control scheme. We are developing such an approach applied to a navigation task of an indoor mobile robot using a 2-D laser range finder. The control objectives related to the sensor based control loop are defined thanks to the properties of the generalized Voronoi diagram. A roadmap of the environment is incrementally built from the laser range information. In this way the robot can explore an unknown scene without any reference trajectory or any prior knowledge about the environment. The localization and mapping process is performed in a probabilistic sense by fusing laser and odometry data thanks to an extended Kalman filter.

We deal with the control of an autonomous underwater vehicle by means of visual servoing techniques. Both theoretical and implementation issues are addressed. After having briefly presented the visual servoing framework used, we focus on its application to a pipe following task. The last part of the paper presents results obtained both in simulation and on the Vortex underwater vehicle which is an experimental ROV built by the Ifremer company.

We present a visual servoing approach applied to the control of an autonomous underwater vehicle executing a pipe inspection task. After having briefly recalled the theoretical framework used, we design a closed loop control scheme using visual data as feedback. This scheme is analyzed using the facilities of our homemade Simparc simulation package which can handle both control and sensory aspects. The last part of the paper presents results obtained on, the Vortex underwater vehicle which is an experimental ROV built by the Ifremer company.

In a visual servoing approach, the control objective is stated in terms of regulating an output function directly expressed in the image space. This approach is particularly usefull when the robotic task can be naturally specified as a relative positioning of a sensor frame handled by the robot with regard to a peculiar part of the environment. In our previous works (Espiau et al. (1992), Rives et al. (1996)), we developed a framework which allows us to handle the different steps of a visual servoing application from the specification level to the synthesis of a robust controller. In this paper, we focus on the evaluation of such a framework applied to a specific pipe inspection task using the Ifremer's ROV Vortex. We show simulation results which validate the approach in presence of modeling errors and measurement noises. We also present experimental issues both in terms of implementation and performances.

A description is given of the SIMPARC software, which consists of a set of C++ classes designed to build simulators for multiprocessor robot controllers. It is built on an event-driven simulator for program execution simulation, and a numerical integration package for robots behavior simulation. It allows describing the hardware of the target architecture in terms of communicating devices. This software allows the user to define his own robot models and actually runs user programs on the simulated architecture. It is mainly concerned with timing aspects of multiprocessor applications and takes into account the sharing of communication resources and processor interrupts.>