Cette thèse traite du problème de la localisation et de la navigation de robots humanoïdes à bas coût dans un environnement dynamique non contraint. Elle a été réalisée en collaboration entre le laboratoire de robotique CAOR de Mines ParisTech et Aldebaran, dont les robots NAO et Pepper sont utilisés comme plateformes.On verra ici comment il est possible de déduire des informations d'orientation et de position du robot malgré les fortes contraintes de puissance de calcul, de champ de vision et de généricité de l'environnement. L'environnement est représenté sous une forme topologique : les lieux sont stockés dans des nœuds, reliés par des transitions. On apprend l'environnement dans une phase préalable permettant de construire une référence. Les contributions principales de la thèse reposent sur les méthodes de calcul de l'orientation et d'une mesure de position du robot à l'aide des caméras monoculaires à faible champ de vision,et leur intégration dans une structure topologique. Pour se localiser dans le graphe, on utilise principalement les données de vision fournies par les caméras monoculaires du robot, tout en laissant la possibilité de compléter à l'aide de caméras 3D. Les différentes méthodes de localisation sont combinées dans une structure hiérarchique qui permet à la fois d'améliorer la robustesse et de fusionner les données de localisation. Un contrôle de la trajectoire est également mis en place pour permettre d'effectuer de façon fiable les transitions d'un nœud à l'autre, et accessoirement fournir un système de retour pour la marche du robot.Les travaux de cette thèse ont été intégrés dans la suite logicielle d'Aldebaran, et testés intensivement dans différents environnements afin de valider les résultats obtenus et préparer une livraison aux clients. / This thesis covers the topic of low cost humanoid robots localization and navigation in a dynamic unconstrained environment. It is the result of a collaboration between the Centre for Robotics of Mines ParisTech and Aldebaran, whose robots, NAO and Pepper, are used as experimental platforms.We will describe how to derive information on the orientation and the position of the robot, under high constraints on computing power, sensor field of view and environment genericity. The environment is represented using a topological formalism : places are stored in vertices, and connected by transitions. The environment is learned in a preliminary phase, which allows the robot to construct a reference.The main contribution of this PHD thesis lies in orientation and approximate position measurement methods, based on monocular cameras with a restricted field of view, and their integration into a topological structure. To localize the robot in the robot, we use mainly data providing by the monocular cameras of the robot, while also allowing extensions, for example with a 3D camera. The different localization methods are combined into a hierarchical structure, which makes the whole process more robust and merges the estimations. A trajectory control has also been developped in order to transition accurately from one vertex to another, and incidently to provide a feedback on the walk of the robot.The results of this thesis have been integrated into Aldebaran software suite, and thoroughly tested in various conditions, in order to validate the conclusions and prepare a client delivery.

Em interações humano-robô ainda existem muitas limitações a serem superadas referentes à provisão de uma comunicação natural quanto aos sentidos humanos. A capacidade de interagir com os seres humanos de maneira natural em contextos sociais (pelo uso da fala, gestos, expressões faciais, movimentos do corpo) é um ponto fundamental para garantir a aceitação de robôs em uma sociedade de pessoas não especialistas em manipulação de engenhos robóticos. Outrossim, a maioria dos robôs existentes possui habilidades limitadas de percepção, cognição e comportamento em comparação com seres humanos. Nesse contexto, este projeto de pesquisa investigou o potencial da arquitetura robótica do humanoide NAO, no tocante à capacidade de realizar interações com seres humanos através de imitação de movimentos do corpo de uma pessoa e pelo controle do robô. Quanto a sensores, foi utilizado um sensor câmera não-intrusivo de profundidade incorporado no dispositivo Kinect. Quanto às técnicas, alguns conceitos matemáticos foram abordados para abstração das configurações espaciais de algumas junções/membros do corpo humano essas configurações foram capturadas por meio da utilização da biblioteca OpenNI. Os experimentos realizados versaram sobre a imitação e o controle do robô por meio da avaliação de vários usuários. Os resultados desses experimentos revelaram um desempenho satisfatório quanto ao sistema desenvolvido / In human-robot interactions there are still many restrictions to overcome regarding the provision of a communication as natural to the human senses. The ability to interact with humans in a natural way in social contexts (the use of speech, gestures, facial expressions, body movements) is a key point to ensure the acceptance of robots in a society of people not specialized in manipulation of robotic devices. Moreover, most existing robots have limited abilities of perception, cognition and behavior in comparison with humans. In this context, this research project investigated the potential of the robotic architecture of the NAO humanoid robot, in terms of ability to perform interactions with humans through imitation of body movements of a person and the robot control. As for sensors, we used a non-intrusive sensor depth-camera built into the device Kinect. As to techniques, some mathematical concepts were discussed for abstraction of the spatial configurations of some joints/members of the human body these configurations were captured through the use of the OpenNI library. The performed experiments were about imitation and the control of the robot through the evaluation of various users. The results of these experiments showed a satisfactory performance for the developed system

Em interações humano-robô ainda existem muitas limitações a serem superadas referentes à provisão de uma comunicação natural quanto aos sentidos humanos. A capacidade de interagir com os seres humanos de maneira natural em contextos sociais (pelo uso da fala, gestos, expressões faciais, movimentos do corpo) é um ponto fundamental para garantir a aceitação de robôs em uma sociedade de pessoas não especialistas em manipulação de engenhos robóticos. Outrossim, a maioria dos robôs existentes possui habilidades limitadas de percepção, cognição e comportamento em comparação com seres humanos. Nesse contexto, este projeto de pesquisa investigou o potencial da arquitetura robótica do humanoide NAO, no tocante à capacidade de realizar interações com seres humanos através de imitação de movimentos do corpo de uma pessoa e pelo controle do robô. Quanto a sensores, foi utilizado um sensor câmera não-intrusivo de profundidade incorporado no dispositivo Kinect. Quanto às técnicas, alguns conceitos matemáticos foram abordados para abstração das configurações espaciais de algumas junções/membros do corpo humano essas configurações foram capturadas por meio da utilização da biblioteca OpenNI. Os experimentos realizados versaram sobre a imitação e o controle do robô por meio da avaliação de vários usuários. Os resultados desses experimentos revelaram um desempenho satisfatório quanto ao sistema desenvolvido / In human-robot interactions there are still many restrictions to overcome regarding the provision of a communication as natural to the human senses. The ability to interact with humans in a natural way in social contexts (the use of speech, gestures, facial expressions, body movements) is a key point to ensure the acceptance of robots in a society of people not specialized in manipulation of robotic devices. Moreover, most existing robots have limited abilities of perception, cognition and behavior in comparison with humans. In this context, this research project investigated the potential of the robotic architecture of the NAO humanoid robot, in terms of ability to perform interactions with humans through imitation of body movements of a person and the robot control. As for sensors, we used a non-intrusive sensor depth-camera built into the device Kinect. As to techniques, some mathematical concepts were discussed for abstraction of the spatial configurations of some joints/members of the human body these configurations were captured through the use of the OpenNI library. The performed experiments were about imitation and the control of the robot through the evaluation of various users. The results of these experiments showed a satisfactory performance for the developed system

The objective of this study was to determine the effectiveness of a 6 week afterschool nutrition and physical activity intervention administered by a registered dietitian with the help of a humanoid robot targeting elementary school aged children aged 6-12 years. The study was conducted across four Young Men’s Christian’s Association (YMCA) sites in Miami-Dade County, Florida (N= 114, Mean age: 8.16 ±1.57 years) using a pretest-posttest quasi-experimental design via randomly assigned intervention (two sites; n=63) and comparison groups (two sites; n=51). The validated Coordinated Approach to Child Health (CATCH) kids club questionnaire and the validated Previous Day Physical Activity Recall (PDPAR) were used to assess nutrition and physical activity knowledge, attitudes/beliefs and behavior change. The Inbody 230 instrument (Biospace, California) was used to calculate body composition and weight. Body Mass Index (BMI) percentiles and associated BMI z-scores for age and gender were calculated based on the Center for Disease Control and Prevention (CDC) growth charts. Data measures were collected at baseline (week 0) and one-week post intervention (week 7). Statistical analysis included independent t-test, paired t-test, chi-squared test, Wilcoxon signed ranks test and logistic regression. Results indicated that nutrition knowledge score significantly increased from 67.43% ±21.03 to 81.31% ±18.47 in the intervention group (p