Localisation d'une cible pour véhicule suiveur

L'invention concerne un dispositif de localisation d'une cible mobile (A) pour un véhicule suiveur (B), comportant un premier module de communication (S1) en un premier emplacement sur ledit véhicule suiveur (B) pour déterminer une première mesure de distance (d1) entre le premier emplacement et la cible mobile (A) en un premier instant, et un second module de communication (S2) en un second emplacement sur le véhicule suiveur (B) pour déterminer une seconde mesure de distance (d2) entre le second emplacement et la cible mobile (A) en un second instant, et un module de calcul pour déterminer une prévision du déplacement du véhicule suiveur entre les premier et second instants, et déterminer une localisation de ladite cible mobile par rapport au dit véhicule suiveur à partir des première et seconde mesures de distance, en prenant en compte la prévision, de sorte à compenser le déplacement entre les premier et second instants

Vehicle localization on a map using particles filtering

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Commande de véhicule utilisant la reconnaissance d'indices image

Since several years, researchers attempt to design algorithms able to control a vehicle by using computer vision. Several approaches are now available but more often the vehicle control is achieved in separate way from the perception task. Indeed the vehicle state is combined with perception parameters to feed the regulation task. This paper presents the reliability of an original approach which combines perception and control task. The system we have designed is able to recognize roadsides from images cues in different situations (highway, road, etc), and to compute the vehicle wheel angle and several location parameters too. In particular, we present the need for achieving a training stage (given either by a control law or by a experimented human driver) to provide a good control law. The algorithm behavior is then presented in simulated and real situations as well in order to prove the reliability of the approach on an agricultural vehicle.Depuis plusieurs années, les chercheurs essayent de concevoir des algorithmes capables de commander un véhicule en employant la vision par ordinateur. Plusieurs approches sont maintenant disponibles mais le plus souvent, la commande du véhicule est réalisée en dehors de la tâche de perception. En effet l'état du vehicule est associé avec des paramètres de perception pour alimenter la tâche de régulation. Cet article présente la robustesse d'une approche originale qui combine la tâche de perception et la tâche de commande. Le système que nous avons conçu peut reconnaître les bords de la route à partir d'indices d'images en différentes situations (autoroute, route, etc..), et calculer l'angle des roues du véhicule ainsi que plusieurs paramètres de localisation. En particulier, nous présentons le besoin de la réalisation d'une étape d'apprentissage (réalisée par une loi de commande ou par un conducteur humain expérimenté) pour permettre une bonne loi de commande. Le comportement de l'algorithme est présenté dans des situations simulées et réelles sur un véhicule agricole

Robot localization and navigation with a ground-based microwave radar

International audienceGlobal Positioning System (GPS) is intensively used for localization and navigation in mobile robotics due to its ease of use, its precision, and its worldwide accessibility. But in presence of tall obstacles (buildings) or vegetation (trees), the reception of GPS signals can be poor or impossible. Such a situation is not compatible with autonomous mobile robotics applications, and redundant localization tools need to be implemented. We present in this paper a solution for localization in mobile robotics based on PELICAN radar. PELICAN is a K-band panoramic radar, whose images areused to build radar maps of the traveled environments. The proposed solution realizes a real-time 3D matching between the current radar image and the pre-existing radar map, in order to estimate the position and the orientation of the radar within the map. Results obtained in localization and navigation are illustrated with a path-following applicatio

Accurate vision based road tracker

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Cooperative localization based on range-only measurements from robots and infrastructure

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Close coordination of mobile robots using radio beacons: a new concept aimed at smart spraying in agriculture

International audienceMany agricultural tasks are known to be dangerous for human operators, the environment, and human health in general. The increasing pressure both on safety and on production levels motivates the development of new methodologies and technologies. The rising of off-road mobile robots for agricultural application appears to be a promising contribution to required innovations. It both permits to limit the exposure of people to hazardous products and to achieve difficult and repetitive tasks. Nevertheless, to be fully efficient, autonomous robots have to ensure a high level of accuracy, while carrying potentially heavy tools, possibly in harsh conditions. It is especially the case of spraying, for which accuracy is a key challenge for reducing environmental impacts. The use of huge robots for spraying might seem to be a straightforward solution, by simply automating existing machines. Nevertheless, a simple automation does not reduce directly the environmental impact of human activities (soil compaction, energy, reduction of the use of chemical products). Moreover, huge machines are not necessarily an advantage when considering safety aspects (rollover risk and maneuverability). As a result, a solution based on the cooperation of at least two mobile robots, moving from either side of a vine row, is investigated in this paper thanks to Ultra Wide Band (UWB) technology