Construir um robô capaz de realizar tarefas sem qualquer interferência humana é um dos maiores desafios da Robótica Move!. Dispondo apenas de sensores, um robô autônomo precisa explorar ambientes desconhecidos e, simultaneamente, construir um mapa confiável a fim de se localizar e realizar a tarefa. Na presença de erros de odometria, o robô não consegue se auto-localizar corretamente em seu mapa interno e acaba por construir um mapa deformado e não condizente com a realidade. Este trabalho apresenta uma solução para o problema da auto-localização de robô moveis autônomos. Esta solução faz use de um método linear de calculo de estimativas chamado Filtro de Kalman para corrigir a posição do robô em seu mapa intern° do ambiente enquanto realiza a exploração. A proposta leva em consideração que toda entidade que se movimenta em um ambiente conta sempre com alguns pontos de referencia para se localizar. Estes pontos são implementados como objetos especiais chamados marcas de Kalman. Em simulação, o reconhecimento das marcas pode ser feito de duas maneiras: através de sua posição no mapa ou através de sua identidade. Nos experimentos realizados em simulação, o método é testado para diferentes erros no angulo de orientação do robô. Os resultados são comparados levando em consideração as deformações no mapa gerado, com e sem marcas de Kalman, e o erro médio da posição do robô durante todo o processo exploratório. / Build a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.

Avec la demande croissante d'espace urbain, de plus en plus de parkings à plusieurs étages sont nécessaires. Bien que ces parkings contribuent à une utilisation plus efficace de l'espace urbain, ils introduisent également un nouveau problème. Les rapports suggèrent environ 70 millions d'heures de recherche d'emplacements de stationnement chaque année, soit une perte équivalente de 700 millions d'euros pour la seule France. En outre, les utilisations des parkings vont au-delà de leurs objectifs initiaux. Des fonctionnalités exigeantes telles que le chargeur électrique, la réservation en ligne de places de stationnement, le guidage dynamique ou le paiement mobile, etc. transforment un parking en un environnement intelligent et compétitif. Une solution à ce problème consiste à développer un système de navigation autonome pour les véhicules intelligents en situation de parking. La thèse identifiera une de ces sous-tâches, à savoir la localisation dans des environnements non autorisés par GPS. Cette thèse présentera une nouvelle méthode pour résoudre le problème indiqué tout en maintenant le système en respectant quatre critères: disponibilité, évolutivité, universalité et précision. Il y a deux étapes principales: (1) une solution permettant de reproduire le comportement du GPS pour un environnement refusé par GPS, et (2) un cadre permettant la fusion de systèmes de type GPS avec d'autres méthodes de localisation pour obtenir une précision de localisation élevée. Tout d'abord, un système de localisation Wi-Fi Fingerprinting est utilisé. Une approche utilisant un réseau de neurones d'ensemble sur une base de données d'empreintes hybrides Wi-Fi est proposée dans cette thèse. Des expériences menées sur une durée d'un an montrent que ce système est capable de localiser des véhicules présentant une erreur moyenne de 2,25 m dans le repère global (WGS84). Deuxièmement, une solution de localisation complète doit être une fusion de plusieurs techniques. Cela permet aux niveaux de localisation global et local de fonctionner ensemble. Parallèlement, la redondance dans le système améliore la précision et la fiabilité. Dans cette thèse, un cadre de fusion flexible pour plusieurs capteurs de localisation est proposé. Ce cadre de fusion traitera non seulement de l'environnement refusé par le GPS, mais pourrait également être utilisé dans l'environnement assisté par GPS et assurer une transition en douceur entre les deux zones. Pour accomplir cette tâche exigeante, un filtre à particules modèle de mélange gaussien est développé. Alors que le modèle de mouvement de ce filtre à particules intègre des données provenant de l'IMU (unité de mesure inertielle) ou du laser-SLAM, le modèle de correction est un modèle de mélange gaussien de plusieurs observations obtenues à partir du système de localisation d'empreintes digitales Wi-Fi. Avec deux véhicules intelligents (une Cybercar et une Citroen C1), 64 expériences ont été réalisées pour valider le cadre. Une erreur de localisation moyenne de 0,5 m est obtenue dans un cadre de coordonnées global. Comparez avec d'autres solutions avec une erreur de localisation moyenne de 0,2 m dans les cadres de coordonnées locales; Cette solution proposée présente également des avantages en termes d'évolutivité, de disponibilité et d'universalité. / With the increasing demand for urban space, more and more multistory carparks are needed. Although these carparks help to utilize urban space more efficient, they also introduce a new problem. Reports suggest approximately 70 million hours of parking slot searching each year, equivalently 700 million euros loss for France alone. In addition, carparks uses are exceeding their original purposes. Demanding features such as electric charger, online booking of parking spaces, dynamic guidance or mobile payment etc. turn a carpark into a competitive smart environment. One solution to this problem is to develop an autonomous navigation system for intelligent vehicles in the carpark situation. The thesis will identify one of these sub-tasks namely localization in GPS-denied environments. This thesis will present a novel method to solve the indicated problem while keeping the system follows four criteria: availability, scalability, universality and accuracy. There are two main steps: (1) a solution to replicate the GPS behaviour for the GPS-denied environment, and (2) a framework that allows the fusion of GPS-like systems with other localization methods to achieve a high localization accuracy. First, a Wi-Fi Fingerprinting localization system is employed. An approach using an ensemble neural network on a hybrid Wi-Fi fingerprinting database is proposed in this thesis. Experiments in a year-long duration show that this system is capable of localizing vehicles with 2.25m of mean error in the global coordinate frame (WGS84). Second, a complete localization solution must be a fusion of multiple techniques. This allows global as well as local levels of localization to function together. At the same time, having redundancy in the system boosts accuracy and reliability. In this thesis, a flexible fusion framework for multiple localization sensors is proposed. This fusion framework will not only deal with the GPS-denied environment but could be potentially used in the GPS-aided environment and provide a smooth transition between the two areas. To accomplish this demanding task, a Gaussian Mixture Model Particle Filter is developed. While the motion model of this particle filter incorporates data from the IMU (Inertial Measurement Unit) or laser-SLAM, the correction model is a Gaussian mixture model of multiple observations obtained from the Wi-Fi fingerprinting localization system. With two intelligent vehicles (a Cybercar and a Citroen C1 car), 64 experiments were carried out to validate the framework. A mean localization error of 0.5m is achieved in a global coordinate frame. Compare to other solutions with 0.2m of mean localization error in local coordinate frames; this proposed solution has advantages in terms of scalability, availability and universality as well.

Philosophiae Doctor - PhD / Type 2 diabetes mellitus (T2DM) and pre-diabetes contribute increasingly to the global burden of disease. Along with other behavioural risk factors, diet plays a key role in the onset and management of the disease, in turn largely determined by what foods are immediately accessible in local food environments. With this in mind, this thesis aims to answer the research question: What role do local food environments play in promoting or inhibiting access to healthy foods as part of the self - management of T2DM in urban and rural communities in South Africa, and what can be learned from an international perspective? Specific research objectives include, to: 1. Understand the current national-level policy context with regard to the observed rise in NCDs, their proximal determinants (specifically an observed change in diet patterns), and contributing environmental factors; 2. Identify the current food-related environmental factors associated with the onset and/or management of T2DM in an urban and a rural setting (as well as in four additional international settings in order to provide an international perspective); 3. Explore community perspectives of the role the local food environment plays in the self-management of T2DM in an urban and a rural setting; and, consequently 4. Recommend intervention- and/or policy-related actions that can be implemented based on study findings. A review of the literature and relevant policies was conducted towards achieving the first research objective. Quantitative data were systematically collected at an urban and rural site in South Africa through the creation of an ‘environmental profile’ in an attempt to achieve the second objective – comparable urban and rural data was also collected as part of a larger study at two other international sites (Kampala, Uganda and Stockholm, Sweden) to provide an international perspective. Included in the third objective is the collection of qualitative data through a community based participatory research method at the same urban and rural sites in South Africa. Finally, intervention and/or policy-related recommendations are developed based on study findings and in consultation with relevant stakeholders through interviews. / 2018-12-14