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International audience; L'un des enjeux les plus importants des technologies impliquées dans l'ingénierie des systèmes complexes concerne aujourd'hui le diagnostic temps réel. Cette discipline repose principalement sur les algorithmes de détection et de localisation de défauts. Dans le présent papier, nous présentons une méthode générique permettant d'améliorer la robustesse de la procédure de détection de défauts. Cette méthode procède en deux étapes distinctes. Dans un premier temps, l'approche des Bond Graphs est utilisée pour générer, sur la base d'un modèle graphique, un ensemble d'indicateurs de défauts appelés résidus. Dans un second temps, les seuils de détectabilité permettant d'évaluer ces résidus sont déterminés grâce à l'analyse par intervalles et aux techniques de satisfaction de contraintes dans le but de réduire au maximum le taux de fausses alarmes et de non détection. Les performances de la méthode proposée sont démontrées par des données expérimentales provenant d'un robot omnidirectionnel.

It can be noticed that the visibility relation is a binary relation when considering points, but a ternary relation when considering sets. The partially-visible space corresponds classically to the twilight (compared to the light (visible space) and the shade (non-visible space)). Figure 1.3 presents an example of visible, non-visible and partially-visible spaces.

This thesis deals with mobile robot localization problems, and more precisely with the interest of a set-membership approach to those problems. Nowadays most of the approaches to localize a robot are probabilistics. This document proposes alternatives based on interval analysis. First a set-membership approach dealing with the global localization problem is presented. The global localization problem aims to localize a robot without any knowledge of its initial pose (position and orientation). The presented method casts the localization problem into a constraint satisfaction problem (CSP). It allows to localize the robot by using the knowledge of the environment and LIDAR sensor measurements. This approach is validated by several experimentations and is compared to the well known Monte Carlo Localization (MCL). Then a visibility relation is studied. Two points are visible if the segment defined by those two points does not intersect any obstacle, otherwise they are non-visible. By using interval analysis, contractors associated to the visibility and non-visibility relations are presented. After a theoretical presentation of the visibility, two applications of those contractors are illustrated: the pose tracking of a team of robots using a boolean information, and a new constraint for the global localization algorithm.; Cette thèse s'intéresse aux problèmes de localisation en robotique mobile, et plus particulièrement à l'intérêt d'une approche ensembliste pour ces problèmes. Actuellement les méthodes probabilistes sont les plus utilisées pour localiser un robot dans son environnement, cette thèse propose des approches alternatives basées sur l'analyse par intervalles. Dans un premier temps, une méthode ensembliste s'intéressant au problème de localisation globale est proposée. Le problème de localisation globale correspond à la localisation d'un robot dans son environnement, sans connaissance à priori sur sa posture (position et orientation) initiale. La méthode proposée associe le problème de localisation à un problème de satisfaction de contraintes (CSP). Elle permet de localiser le robot en utilisant la connaissance de l'environnement, ainsi qu'un jeu de mesures LIDAR. Cette méthode est validée à l'aide de différentes expérimentations et est comparée à une approche probabiliste classique : la Localisation Monte Carlo (MCL). Dans un second temps une notion de visibilité est étudiée. Deux points sont supposés visibles, si le segment défini par ces deux points n'intersecte pas d'obstacle, autrement ils sont dit non-visibles. À l'aide de l'analyse par intervalles, des contracteurs associés à cette notion de visibilité sont développés. Après une présentation théorique de la visibilité, deux applications de ces contracteurs à la localisation en robotique mobile sont présentées : le suivi de posture d'une meute de robots à l'aide d'une information booléenne, et la prise en compte d'une contrainte supplémentaire dans le CSP associé à la localisation globale.

International audience; The aim of the Cart-O-matic project was to design and build a multi-robot system able to autonomously map an unknown building. This work has been done in the framework of a French robotics contest called Defi CAROTTE organized by the General Delegation for Armaments (DGA) and the French National Research Agency (ANR). The scientific issues of this project deal with Simultaneous Localization And Mapping (SLAM), multi-robot collaboration and object recognition. In this paper, we will mainly focussed on the two first topics : after a general introduction, we will briefly describe the innovative simultaneous localization and mapping algorithm used during the competition. We will next explain how this algorithm can deal with multi-robots systems and 3D mapping. The next part of the paper will be dedicated to the multi-robot pathplanning and exploration strategy. The last section will illustrate the results with 2D and 3D maps, collaborative exploration strategies and example of planned trajectories.

International audience

Visibility is studied and used in several fields: computer graphics, telecommunication, robotics... For instance, in Computer-aided design (CAD) synthesis images are created by simulating light propagation in a scene. Visibility notions are then necessary to compute the visible objects from a point of view, and the shadow of those objects. In mobile robotics the visibility is used for path planning (visibility graph) and localization problems. This presentation is about visibility information for mobile robot localization. The objective is twofold. First a visibility notion based on segment intersections is presented. By considering a set-membership approach it is possible to develop contractors associated to this visibility relation. Then two applications of those visibility contractors to mobile robot localization are presented. The first one corresponds to the pose tracking of a team of robots. The idea is to use a Boolean information (the visibility between two robots: two robots are visible or not) in order to avoid the drifting of those robots (in order to maintain the precision of their position estimations). The second application corresponds to the processing of an original constraint for a set-membership global localization algorithm. This global localization algorithm is based on a CSP approach (Constraint Satisfaction Problem). Adding a visibility constraint to this CSP improves the accuracy of the algorithm.

International audience; This paper proposes a set-membership method based on interval analysis to solve the pose tracking problem. The originality of this approach is to consider weak sensors data: the visibility between two robots. By using a team of robots and this boolean information (two robots see each other or not), the objective is to compensate the odometry errors and be able to localize, in a guaranteed way, the robots in an indoor environment. This environment is supposed to be defined by two sets, an inner and an outer characterizations. Simulated results allow to evaluate the efficiency and the limits of the proposed method.

International audience; This paper presents a set membership method to deal with the kidnapping problem in mobile robotics. By using a range sensor, the odometry and a discrete map of an indoor environment, a robot has to detect a kidnapping situation, while performing a pose tracking, and then perform a global localization to estimate its new pose (position and orientation). In a bounded error context the IAL (Interval Analysis Localization) algorithm searches a small box (interval vector) that includes the robot's pose, using interval analysis and constraint propagation tools. This algorithm allows to perform a pose tracking and a global localization. Using this algorithm, it is possible to deal with the kidnapping problem. This method is tested using real data, recorded during the CAROTTE challenge, organized by the French ANR (National Research Agency) and the French Army. As it is shown in this paper with the IAL algorithm, interval analysis is an efficient tool to solve the kidnapping problem.

International audience