Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2009. / Submitted by Raquel Viana (tempestade_b@hotmail.com) on 2010-03-29T14:47:02Z No. of bitstreams: 1 2009_GlaucoGarciaScandaroli.pdf: 3618541 bytes, checksum: f9331bd3203fbf3e1f36d037b475814a (MD5) / Approved for entry into archive by Lucila Saraiva(lucilasaraiva1@gmail.com) on 2010-05-21T16:06:39Z (GMT) No. of bitstreams: 1 2009_GlaucoGarciaScandaroli.pdf: 3618541 bytes, checksum: f9331bd3203fbf3e1f36d037b475814a (MD5) / Made available in DSpace on 2010-05-21T16:06:39Z (GMT). No. of bitstreams: 1 2009_GlaucoGarciaScandaroli.pdf: 3618541 bytes, checksum: f9331bd3203fbf3e1f36d037b475814a (MD5) Previous issue date: 2009 / Esta dissertação é desenvolvida na área de estimação de estados em sistemas com restrições de igualdade. É feito um estudo de sistemas com restrições de igualdade e este trabalho apresenta uma forma de se utilizar sistemas descritores para realizar a estimação neste tipo de sistema. Também é apresentado um novo método para fusão de dados em sistemas com restrições de igualdade e correlação desconhecida. Para a validação destes estudos são desenvolvidas duas aplicações de robótica. Na área de robótica de reabilitação é feita a estimação de postura do pé de uma prótese robótica de perna. São apresentadas formas de se realizar essa tarefa utilizando mínimos quadrados não-lineares e os métodos de estimação com restrição. Os resultados obtidos comprovam que a utilização de estimadores que consideram as restrições melhora o processo de estimação. Na área de robótica móvel é proposto um método eficiente de estimação de localização utilizando teoria Bayesiana e dado as restrições existentes entre as estruturas de um mapa geométrico, avalia-se o método apresentado para fusão de dados em sistemas com restrições de igualdade e correlação desconhecida. Os resultados descritos corroboram que o mapa resultante é melhorado com o uso das restrições de igualdade e do método de localização proposto. ________________________________________________________________________________________ ABSTRACT / This dissertation is developed in the field of state estimation in non-linear systems with equality constraints. Systems with equality constraints are studied, and a method that employs descriptor systems to accomplish the task of estimation in this kind of system is proposed. This manuscript also presents a method for data fusion in systems subject to equality constraints, and unknown correlation. For the validation of the developed studies, this manuscript also describes two applications in the field of robotics. In the field of rehabilitation robotics, this dissertation presents the estimation of a robotic leg prosthesis’ foot with respect to ground. There are presented solutions to perform this task using non-linear least squares, and constrained estimation methods. The obtained results establish that the implementation using estimators that employ equality constraints improves the process of estimation. In the field of mobile robotics, this work proposes an efficient estimation method to accomplish the problem of localization based on the Bayesian theory. Also, given the existing constraints between the geometric map’s structures, the method for data fusion in systems subject to equality constraints and unknown correlation is evaluated. Results described corroborate that the resulting map is improved with the application of such equality constraints and the presented localization method.

Orientador: Carlos Alberto dos Reis Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-05T17:36:56Z (GMT). No. of bitstreams: 1 Salustiano_RogerioEsteves_M.pdf: 3698724 bytes, checksum: a5c6d59ec19db38f5a0324243ddb1eb5 (MD5) Previous issue date: 2006 / Resumo: Este trabalho propõe um sistema computacional no qual são aplicadas técnicas de Fusão de Sensores no monitoramento de ambientes. O sistema proposto permite a utilização e incorporação de diversos tipos de dados, incluindo imagens, sons e números em diferentes bases. Dentre os diversos algoritmos pertinentes a um sistema como este, foram implementados os de Sensores em Consenso que visam a combinação de dados de uma mesma natureza. O sistema proposto é suficientemente flexível, permitindo a inclusão de novos tipos de dados e os correspondentes algoritmos que os processem. Todo o processo de recebimento dos dados produzidos pelos sensores, configuração e visualização dos resultados é realizado através da Internet / Abstract: This work proposes a computer system in which Sensor Fusion techniques are applied to monitoring the environment. The proposed system allows the use and incorporation of different data types, including images, sounds and numbers in different bases. Among the existing algorithms that pertain to a system like this, those, which aim to combine data of the same nature, called Consensus Sensors, have been particularly implemented. The proposed system is flexible enough and allows the inclusion of new data types and their corresponding algorithms. The whole process of receiving the data produced by the sensors, configuration of produced results as well as their visualization is performed through the Internet / Mestrado / Eletrônica, Microeletrônica e Optoeletrônica / Mestre em Engenharia Elétrica

This thesis has explored if it is feasible to produce a good estimation of the rotational position of a stepper motor by using sensor fusion schemes to merge a sensorless position estimation (based on the back electromotive force) with the measurement from a magnetic rotational position sensor. The purpose was to find a cheaper alternative for position feedback in closed loop control from conventionally used rotational encoders and resolvers. Beyond the sensor fusion a suitable position control logic was also developed to verify the concept of a low cost closed loop hybrid stepper motor solution for high precision position applications. The sensor fusion and position control were simulated offline to first test the feasibility of the implementation, after which laboratory tests were performed to assess online performance. The extended Kalman filter implemented improved the performance of the magnetic rotational position sensor which was used exclusively at lower speeds (between 0-75 rpm) by decreasing its root-mean-square error by almost half from 0.0733 unfiltered to 0.0370 filtered (in mechanical degrees). When fusing both position signals at higher rotational speeds (75-400rpm) did the extended Kalman filter clearly improve position estimation accuracy compared to the single sources. It is not meaningful however to discuss the numeric improvement of the filter at these working points as this result is not conclusive but based on some fortunate conditions. This is because the two signals used for the fusion is diverging towards positive and negative error respectively for increasing rotational speeds making the fused estimate result in between. This basically means that the result from the fusion is outperforming two very bad signals, and is then not meaningful to use as a measure of how well the fusion is actually performing. Further work on the raw signals used for fusion need to be performed before a proper assessment on the fusion performance could be made.

Cette thèse a pour but d'étudier et de quantifier la contribution de la perception multimodale assistée par le contexte pour détecter et suivre des objets en mouvement. Cette étude sera appliquée à la détection et la reconnaissance des objets pertinents dans les environnements de la circulation pour les véhicules intelligents (VI). Les résultats à obtenir devront permettre de transposer le concept proposé à un ensemble plus large de capteurs et de classes d'objets en utilisant une approche système intégrative qui implique des méthodes d'apprentissage. En particulier, ces méthodes d'apprentissage vont examiner comment l'implantation dans un système intégré, qui prévoie une multitude des sources de données différentes, peut conduire à apprendre 1) sans ou avec une supervision limitée, réduite en exploitant des corrélations 2) de façon incrémentale à la connaissance stockée au lieu de faire un entraînement complet à chaque fois qu’une nouvelle donnée arrive 3) collectivement à chaque instant d'apprentissage dans le système entraîné d'une manière qui assure approximativement une fusion optimale. Concrètement, le couplage fort entre les classifier des objets en modalités multiples aussi bien que l'extraction du contexte de la géométrie de la scène sont à étudier: d'abord en théorie, après en application du trafic routier. La nouveauté de l'approche d'intégration envisagée se pose dans le couplage fort entre les composants du système, tels que la segmentation, le suivi des objets, l'estimation de la géométrie de la scène et la catégorisation des objets basée sur la stratégie de l'inférence probabiliste. Une telle stratégie caractérise des systèmes où toutes les composants de perception émettent et reçoivent les distributions des résultats possibles avec leur score de croyance probabiliste attribué. De cette façon, chaque composant de traitement peut prendre en compte les résultats des autres composants au niveau plus bas par rapport aux combinaisons des résultats finaux. Cela diminue beaucoup le temps et les ressources pour le calcul, quand les techniques de l'application de l'inférence Bayésienne garantissent que les données d'entrée peu plausible n'apportent pas des impacts négatifs. / This thesis project will investigate and quantify the contribution of context-aided multimodal perception for detecting and tracking moving objects. This research study will be applied to the detection and recognition ofrelevant objects in road traffic environments for Intelligent Vehicles (IV). The results to be obtained will allow us to transpose the proposed concept to a wide range of state-of-the-art sensors and object classes by means of an integrative system approach involving learning methods. In particular, such learning methods will investigate how the embedding into an embodied system providing a multitude of different data sources, can be harnessed to learn 1) without, or with reduced, explicit supervision by exploiting correlations 2) incrementally, by adding to existing knowledge instead of complete retraining every time new data arrive 3) collectively, each learning instance in the system being trained in a way that ensures approximately optimal fusion. Concretely, a tight coupling between object classifiers in multiple modalities as well as geometric scene context extraction will be studied, first in theory, then in the context of road traffic. The novelty of the envisioned integration approach lies in the tight coupling between system components such as object segmentation, object tracking, scene geometry estimation and object categorization based on a probabilistic inference strategy. Such a strategy characterizes systems where all perception components broadcast and receive distributions of multiple possible results together with a probabilistic belief score. In this way, each processing component can take into account the results of other components at a much earlier stage (as compared to just combining final results), thus hugely increasing its computation power, while the application of Bayesian inference techniques will ensure that implausible inputs do not cause negative effects.

High accuracy localization of objects is a crucial function for many modern applications, such as virtual and augmented reality, robotics, and self-driving cars, among others. This requires determining precise location of objects indoors, which is a challenging task. In recent years, Ultra-Wideband technology has seen increasing interest as a potential solution to this problem by the research community. This is mainly due to its innate capabilities of high update frequency and low power consumption which makes it a suitable technology for precise distance measurement and location determination. This study has aimed to answer what the state-of-the-art in the field of trilateration in Ultra-Wideband based indoor positioning systems utilizing other complementary technologies is. This was done by conducting a document survey using a Grounded theory approach for the analysis. To ensure validity and reliability of the study, the sample was collected through searching IEEE Xplore using different sets of keywords, and the potential samples was then checked using a data quality form. The analysis consisted of identifying categories and concepts in the sample. The analysis found that the Ultra-wideband based systems can achieve high positioning accuracy, but limitations such as non-line-of-sight disturbance must still be overcome for the technology to consistently achieve centimetre accuracy. These limitations are being mitigated using filtering, machine learning, and multi-sensory fusion. With these complementary technologies researchers can eliminate some of the limitations. The field does however seem to be in an exploratory stage where best practices for overcoming the current limitations are yet established.

Indiana University-Purdue University Indianapolis (IUPUI) / A key component in tracking is identifying relevant data and combining the data in an effort to provide an accurate estimate of both the location and the orientation of an object marker as it moves through an environment. This thesis proposes an enhancement to an existing tracking system, the enhanced distributed tracking system (e-DTS), in the form of the e-DTS 2.0 and provides an empirical analysis of these enhancements. The thesis also provides suggestions on future enhancements and improvements. When a Camera identifies an object within its frame of view, it communicates with a JINI-based service in an effort to expose this information to any client who wishes to consume it. This aforementioned communication utilizes the JINI Multicast Lookup Protocol to provide the means for a dynamic discovery of any sensors as they are added or removed from the environment during the tracking process. The client can then retrieve this information from the service and perform a fusion technique in an effort to provide an estimation of the marker's current location with respect to a given coordinate system. The coordinate system handoff and transformation is a key component of the e-DTS 2.0 tracking process as it improves the agility of the system.

For complex satellite missions that rely on agile and high-precision manoeuvres, the low-friction aspect of the space environment is a critical component in understanding the attitude control dynamics of the spacecraft. The Kinesthetic Node and Autonomous Table-Top Emulator (KNATTE) is a three-degree-of-freedom frictionless vehicle that serves as the foundation of a multipurpose platform for real-time spacecraft hardware-in-the-loop experiments, and allows emulation of these conditions in two dimensions with the purpose of validating various guidance, navigation, and control algorithms. The data acquisition of the vehicle depends on a computer vision system (CVS) that yields position and attitude data, but also suffers from unpredictable blackout events. To complement such measurements, KNATTE incorporates an inertial measurement unit (IMU) that yields accelerometer, gyroscope, and magnetometer data. This study describes a multisensor data fusion approach to obtain accurate attitude information by combining the measurements from the CVS and the IMU using nonlinear Kalman filter algorithms. To do this, the data fusion algorithms are developed and tested in a Matlab/Simulink environment. After that, the algorithms are adapted to the KNATTE platform and their performance is confirmed in various conditions. Through this work, the accuracy and efficiency of the approach can be checked by numerical simulation and real-time experiments. In addition, the quality of the CVS measurements are further improved by the introduction of a neural network to the image processing pipeline of the original system.

No / The square array was initially tested in the UK during the 1960s by Anthony Clark. However, since the development of the twin probe system, the square array has been seldom used in the UK, although greater use has been reported elsewhere, especially in France (Panissod et al., 1998). In the last few years, re-investigation of the square array¿s potential in an archaeological context has reignited interest and led to the development of a hand-pulled cart system by Dr. Roger Walker (Geoscan Research). This cart system incorporates earth resistance and gradiometer instruments to allow simultaneous surveys with a lightweight device. The main objective of the project is to test the application of the MSP40 on a variety of sites and to encourage the use of appropriate geophysical survey equipment to minimize the impact and therefore protect even the most vulnerable of archaeological sites.

Este trabalho apresenta um sistema completo para simulação e avaliação do uso de filtros estocásticos para combinar medidas de posição feitas por um sistema inercial, composto de girômetros e acelerômetros, com medidas de posição de um sistema GPS, de modo que possamos extrair uma estimativa do erro de posição acumulado por integração das medidas dos sensores inerciais e corrigir a leitura do mesmo. Para tal, foram desenvolvidos em detalhes e validados um modelo de navegação e um modelo de espaço de estados onde o vetor de variáveis ocultas é a combinação dos erros de posição, velocidade, atitude e fator de escala dos sensores inerciais e deriva de ambos os sensores, inerciais e do GPS. Ainda foram implementados e analisados em sua performance três tipos de Filtros aplicados quando o modelo de observações é não-linear: o Filtro Estendido de Kalman (EKF), o Filtro de Kalman Unscented (UKF) e o Filtro de partículas com Função de importância ótima e Reamostragem. Resultados da Integração Inercial-GPS em diversas trajetórias e configurações de parâmetros são apresentados, bem como os problemas e as soluções na implementação são discutidos.

As perspectivas de expansão no volume de tráfego aéreo global para os próximos anos criam importantes desafios para os setores de controle e monitoramento. Nesse cenário surge um novo conceito para controle de tráfego aéreo baseado na cooperação das aeronaves denominado ADS-B. A técnica vem sendo difundida com a ajuda de órgãos homologadores de todo o mundo tornando-se popular gradativamente. Baseada na utilização de informações provenientes de sistemas de posicionamento e navegação por satélites, a mesma busca uma integração com os atuais sensores radar para o provimento de informações de maior precisão para monitoramento de tráfego. O presente trabalho avalia a utilização eficiente dos dados de ambos os sensores - radar e ADS-B - através de técnicas de fusão de dados. Fusões do tipo centralizada e descentralizadas são avaliadas mostrando ganhos na estimação de rotas de aeronaves, em ambos os casos, quando da utilização de informações de um sistema GPS simulado. As conclusões apresentadas apontam para o fato de que o sistema pode ser capaz de acomodar o crescimento do tráfego com reduções da distância de separação entre as aeronaves.