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Résumé

Cet article présente une extension d'une approche de planification hiérarchique pour la gestion des problèmes multi-agents et, plus spécialement, pour les problèmes d'interaction Homme-Robot dans lesquels un robot et un humain doivent coopérer pour atteindre un but commun. La méthode proposée permet de raisonner et de planifier pour des agents qui ont des croyances différentes ou incomplètes. Elle s'appuie sur une nouvelle description des croyances des agents et sur un mécanisme permettant de produire et d'insérer des actions de communication dans le plan courant. Ces actions de communication ont pour fonction de transmettre des informations d'un agent à l'autre.

Abstract

Over the last two years, the Modular OpenRobots Simulation Engine (MORSE) project1 went from a simple extension plugged on the Blender's Game Engine to a full-fledged simulation environment for academic robotics. Driven by the requirements of several of its developers, tools dedicated to Human-Robot interaction simulation have taken a prominent place in the project. This late breaking report discusses some of the recent additions in this domain, including the immersive experience provided by the integration of the Kinect device as input controller. We also give an overview of the experiences we plan to complete in the coming months.

Abstract

On the 14th of October 2011, we performed for a general public audience (over 300 persons) a 18 min long live theatre play, acted by professional actor Xavier Brossard and the LAAS/CNRS PR2 robot. The play was created and directed by Nicolas Darrot, a mixed-media artist from Paris. The PR2 has been programmed in a 2-months course, reusing several software components developed at LAAS/CNRS, including the 3D environment for situation assessment SPARK, the ontology-based knowledge base ORO and the natural-language processor Dialogs. This video abstract presents the storyline of the play, and underlines some of the significant outcome for the human-robot interaction community. The full-length version of the video, along with downloads of the open-source components, is available from www.laas.fr/roboscopie.

Abstract

This paper presents how extraction, representation and use of symbolic knowledge from real-world perception and human-robot verbal and non-verbal in-teraction can actually enable a grounded and shared model of the world that is suitable for later high-level tasks such as dialogue understanding. We show how the anchoring process itself relies on the situated nature of human-robot interactions. We present an integrated approach, including a specialized symbolic knowledge representation system based on Description Logics, and case studies on several robotic platforms that demonstrate these cognitive capabilities.

Abstract

In order to autonomously navigate in an environment, a robot has to perceive its environment correctly. Rich perception information from the environment enables the robot to perform tasks like avoiding obstacles, building terrain maps, and localizing itself. Classically, outdoor robots have perceived their environment using vision or 2D lidar sensors. The introduction of novel 3D lidar sensors such as the Velodyne device has enabled the robots to rapidly acquire rich 3D data about their surroundings. These novel sensors call for the development of techniques that efficiently exploit their capabilities for autonomous navigation. The first part of this thesis presents a technique for the calibration of 3D lidar devices. The calibration technique is based on the comparison of acquired 3D lidar data to a ground truth model in order to estimate the optimal values of the calibration parameters. The second part of the thesis presents a technique for qualitative localization and loop closure detection for autonomous mobile robots, by extracting and indexing small-sized signatures from 3D lidar data. The signatures are based on histograms of local surface normal information that is efficiently extracted from the lidar data. Experimental results illustrate the developments throughout the manuscript.

Résumé

Afin de permettre une navigation autonome d'un robot dans un environnement, le robot doit être capable de percevoir son environnement. Dans la littérature, d'une manière générale, les robots perçoivent leur environnement en utilisant des capteurs de type sonars, cameras et lidar 2D. L'introduction de nouveaux capteurs, nommés lidar 3D, tels que le Velodyne HDL-64E S2, a permis aux robots d'acquérir plus rapidement des données 3D à partir de leur environnement. La première partie de cette thèse présente une technique pour la calibrage des capteurs lidar 3D. La technique est basée sur la comparaison des données lidar à un modèle de vérité de terrain afin d'estimer les valeurs optimales des paramètres de calibrage. La deuxième partie de la thèse présente une technique pour la localisation et la détection de fermeture de boucles pour les robots autonomes. La technique est basée sur l'extraction et l'indexation des signatures de petite-taille à partir de données lidar 3D. Les signatures sont basées sur les histogrammes de l'information de normales de surfaces locale extraite à partir des données lidar en exploitant la disposition des faisceaux laser dans le dispositif lidar.

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