<p dir="ltr">Thermal radiation is a naturally abundant form of light that is continuously emitted from objects above absolute zero. Because this form of electromagnetic radiation is invisible to the human eye, much of human and machine perception neglects the rich information that is present in infrared energy. By harvesting the spectral and polarimetric characteristics of thermal signals, thermal imaging can deliver an enormous impact to remote sensing, machine perception, autonomous navigation, and biomedical applications. The goal of this thesis is to present numerous techniques that enable the extraction of the vast information available via thermal radiation.</p><p dir="ltr">This thesis investigates a more robust and approachable method of providing spectral and polarimetric resolution to short-wave infrared cameras. Through the application of a liquid crystal interferometer, this research demonstrates an electrically-tunable spectral imaging platform that is compact, robust, cost-effective, and accurate, offering a durable solution for remote sensing and autonomous navigation. This thesis also examines the design of filters specific to the short-wave infrared signature of greenhouse gasses, enabling aerial detection and measurement of greenhouse gas sources via a single filtered image, which can drastically improve the speed and accuracy of monitoring greenhouse gas emissions. In the long-wave infrared regime, this research explores a four-color imaging thermometer, capitalizing on the resolution provided by four spectral bands—in conjunction with the <i>TeX-</i><i>Vision</i><i> </i>temperature-estimation algorithm—to yield unprecedented temperature estimation accuracy that can advance current medical diagnostic practices.</p><p dir="ltr">The examples described in this thesis reveal the breadth of untapped information that is present in thermal radiation, which carries the ability to enhance the way we perceive our surroundings.</p>

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail.

No description available.

<p dir="ltr">The concept of reinforcement learning has become increasingly relevant in learning- based applications, especially in the field of autonomous navigation, because of its fundamental nature to operate without the necessity of labeled data. However, the infeasibility of training reinforcement learning based autonomous navigation applications in a real-world setting has increased the popularity of researching and developing on autonomous navigation systems by creating simulated environments in game engine platforms. This thesis investigates the comparative performance of Unity and Unreal Engine 5 within the framework of a reinforcement learning system applied to autonomous race car navigation. A rudimentary simulated setting featuring a model car navigating a racetrack is developed, ensuring uniformity in environmental aspects across both Unity and Unreal Engine 5. The research employs reinforcement learning with genetic algorithms to instruct the model car in race track navigation; while the tools and programming methods for implementing reinforcement learning vary between the platforms, the fundamental concept of reinforcement learning via genetic algorithms remains consistent to facilitate meaningful comparisons. The implementation includes logging of key performance variables during run times on each platform. A comparative analysis of the performance data collected demonstrates Unreal Engine's superior performance across the collected variables. These findings contribute insights to the field of autonomous navigation systems development and reinforce the significance of choosing an optimal underlying simulation platform for reinforcement learning applications.</p>

Finding lost persons, collecting information in disturbed communities, efficiently traversing urban areas after a blast or similar catastrophic events have motivated researchers to develop intelligent sensor frameworks to aid law enforcement, first responders, and military personnel with situational awareness. This dissertation consists of a two-part framework for providing situational awareness using both acoustic ground sensors and aerial sensing modalities. Ground sensors in the field of data-driven detection and classification approaches typically rely on computationally expensive inputs such as image or video-based methods [6, 91]. However, the information given by an acoustic signal offers several advantages, such as low computational needs and possible classification of occluded events including gunshots or explosions. Once an event is identified, responding to real-time events in urban areas is difficult using an Unmanned Aerial Vehicle (UAV) especially when GPS is unreliable due to coverage blackouts and/or GPS degradation [10]. Furthermore, if it is possible to deploy multiple in-situ static intelligent acoustic autonomous sensors that can identify anomalous sounds given context, then the sensors can communicate with an autonomous UAV that can navigate in a GPS-denied urban environment for investigation of the event; this could offer several advantages for time-critical and precise, localized response information necessary for life-saving decision-making. Thus, in order to implement a complete intelligent sensor framework, the need for both an intelligent static ground acoustic autonomous unattended sensors (AAUS) and improvements to GPS-degraded localization has become apparent for applications such as anomaly detection, public safety, as well as intelligence surveillance and reconnaissance (ISR) operations. Distributed AAUS networks could provide end-users with near real-time actionable information for large urban environments with limited resources. Complete ISR mission profiles require a UAV to fly in GPS challenging or denied environments such as natural or urban canyons, at least in a part of a mission. This dissertation addresses, 1) the development of intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification and 2) GPS impaired localization through a formal framework for trajectory-based flight navigation for unmanned aircraft systems (UAS) operating BVLOS in low-altitude urban airspace. Our AAUS sensor method utilizes monophonic sound event detection in which the sensor detects, records, and classifies each event utilizing supervised machine learning techniques [90]. We propose a simulated framework to enhance the performance of localization in GPS-denied environments. We do this by using a new representation of 3D geospatial data using planar features that efficiently capture the amount of information required for sensor-based GPS navigation in obstacle-rich environments. The results from this dissertation would impact both military and civilian areas of research with the ability to react to events and navigate in an urban environment. / Doctor of Philosophy / Emergency scenarios such as missing persons or catastrophic events in urban areas require first responders to gain situational awareness motivating researchers to investigate intelligent sensor frameworks that utilize drones for observation prompting questions such as: How can responders detect and classify acoustic anomalies using unattended sensors? and How do they remotely navigate in GPS-denied urban environments using drones to potentially investigate such an event? This dissertation addresses the first question through the development of intelligent WSN systems that can provide time-critical and precise, localized environmental information necessary for decision-making. At Virginia Tech, we have developed a static ground Acoustic Autonomous Unattended Sensor (AAUS) capable of machine learning for audio feature classification. The prior arts of intelligent AAUS and network architectures do not account for network failure, jamming capabilities, or remote scenarios in which cellular data wifi coverage are unavailable [78, 90]. Lacking a framework for such scenarios illuminates vulnerability in operational integrity for proposed solutions in homeland security applications. We address this through data ferrying, a communication method in which a mobile node, such as a drone, physically carries data as it moves through the environment to communicate with other sensor nodes on the ground. When examining the second question of navigation/investigation, concerns of safety arise in urban areas regarding drones due to GPS signal loss which is one of the first problems that can occur when a drone flies into a city (such as New York City). If this happens, potential crashes, injury and damage to property are imminent because the drone does not know where it is in space. In these GPS-denied situations traditional methods use point clouds (a set of data points in space (X,Y,Z) representing a 3D object [107]) constructed from laser radar scanners (often seen in a Microsoft Xbox Kinect sensor) to find itself. The main drawback from using methods such as these is the accumulation of error and computational complexity of large data-sets such as New York City. An advantage of cities is that they are largely flat; thus, if you can represent a building with a plane instead of 10,000 points, you can greatly reduce your data and improve algorithm performance. This dissertation addresses both the needs of an intelligent sensor framework through the development of a static ground AAUS capable of machine learning for audio feature classification as well as GPS-impaired localization through a formal framework for trajectory-based flight navigation for UAS operating BVLOS in low altitude urban and suburban environments.

Navegação autônoma é um tópico abrangente cuja atenção por parte da comunidade de robôs móveis vemaumentando ao longo dos anos. O problema consiste em guiar um robô de forma inteligente por um determinado percurso sem ajuda humana. Esta dissertação apresenta um sistema de navegação para ambientes abertos baseado em visão estéreo. Uma câmera estéreo é utilizada na captação de imagens do ambiente e, utilizando o mapa de disparidades gerado por um método estéreo semi-global, dois métodos de detecção de obstáculos são utilizando para segmentar as imagens em regiões navegáveis e não navegáveis. Posteriormente esta classificação é utilizada em conjunto com um método de desvio de obstáculos, resultando em um sistema completo de navegação autônoma. Os resultados obtidos por está dissertação incluem a avaliação de dois métodos estéreo, esta sendo favorável ao método estéreo empregado (semi-global). Foram feitos testes visando avaliar a qualidade e custo computacional de dois métodos para detecção de obstáculos, um baseado em plano e outro baseado em cone. Tais testes deixaram claras as limitações de ambos os métodos e levaram a uma implementação paralela do método baseado em cone. Utilizando uma unidade de processamento gráfico, a versão paralelizada do método baseado em cone atingiu um ganho no tempo computacional de aproximadamente dez vezes. Por fim, os resultados demonstrarão o sistema completo em funcionamento, onde a plataforma robótica utilizada, um veículo elétrico, foi capaz de desviar de pessoas e cones alcançando seu objetivo seguramente / Autonomous navigation is a broad topic that has received increasing attention from the community of mobile robots over the years. The problem is to guide a robot in a smart way for a certain route without human help. This dissertation presents a navigation system for open environments based on stereo vision. A stereo camera is used to capture images of the environment and based on the disparity map generated by a semi-global stereo method, two obstacle detection methods are used to segment the images into navigable and non-navigable regions. Subsequently, this classification is employed in conjunction with a obstacle avoidance method, resulting in a complete autonomous navigation system. The results include an evaluation two stereo methods, this being favorable to the employed stereo method (semi-global). Tests were performed to evaluate the quality and computational cost of two methods for obstacle detection, a plane based one and a cone based. Such tests have left clear the limitations of both methods and led to a parallel implementation of the cone based method. Using a graphics processing unit, a parallel version of the cone based method reached a gain in computational time of approximately ten times. Finally, the results demonstrate the complete system in operation, where the robotic platform used, an electric vehicle, was able to dodge people and cones reaching its goal safely

Este projeto consiste em um sistema de navegação autônomo baseado em redes neurais nebulosas modulares capacitando o robô a alcançar alvos, ou pontos metas, em ambientes desconhecidos. Inicialmente, o sistema não tem habilidade para a navegação, após uma fase de experimentos com algumas colisões, o mecanismo de navegação aprimora-se guiando o robô ao alvo de forma eficiente. Uma arquitetura híbrida inteligente é apresentada para este sistema de navegação, baseada em redes neurais artificiais e lógica nebulosa. A arquitetura é hierárquica e costitiui-se de dois módulos responsáveis por gerar comportamentos inatos de desvio de obstáculos e de busca ao alvo. Um mecanismo de aprendizagem por reforço, baseada em uma extensão da lei de Hebb, pondera os comportamentos inatos conflitantes ajustando os pesos sinápticos das redes neurais nos instantes de captura do alvo e de colisão contra obstáculos. A abordagem consolidada em simulação é validada em ambientes reais neste trabalho. Para tanto, este sistema foi implementado e testado no simulador Saphira, ambiente de simulação que acompanha o robô Pioneer I e que denota um estágio anterior aos testes em ambientes reais por apresentar comportamentos do robô similares aos comportamentos do robô móvel. Modificações na arquitetura híbrida foram necessárias para adaptar o sistema de navegação simulado ao sistema incorporado no Pioneer I. Experimentos em ambientes reais demonstraram a eficiência e a capacidade de aprendizagem do sistema de navegação, validando a arquitetura híbrida inteligente para aplicação em robôs móveis / This project consists in a autonomous navigation system based on modular neuro-fuzzy networks that is able to guide the robot in unknown environments from a initial point to the goal. Initially, the system is not able to navigate, but after a trial and error period and some collisions, it improves in guiding the robot to the goal efficiently. A intelligent hybrid architecture is presented for this naviga tion system based on artificial neural networks and fuzzy logic. This architecture is hierarquical and consists in two modules that generate innate behaviors, like obstacles avoiding and target reaching. A reinforcement learning mecanism, based on the extended Hebb law, balances this conflicting innate behaviors adjusting the neural network synaptic weights as obstacle and collision avoidance and target reaching takes place. In this project, the approach is consolidated in simulation and validated in real environments. To this end, this system has been implemented by using Saphira simulator and Pioneer I simulation environment. This simulated evironment is a previous stage of tests performed real time and presents simulated robot behaviors similar to real mobile robot behaviors. The hybrid architecture was modified to adapt the simulated navigation system into Pioneer I software. Experiments in a real environments show the efficiency and learning capabilities of the navigation system, validating the intelligent hybrid architecture for mobile robots applications

Dans cette thèse, nous nous intéressons au problème du suivi en convoi, désigné en anglais par le terme platooning, où un train de robots essaie de suivre un chemin décrit par le leader. Ce chemin, n'étant pas prédéfini mais généré au cours du suivi, est inconnu de tous les robots suiveurs. Dans ce travail, nous choisissons une approche décentralisée locale où chaque robot du convoi observe son voisinage et calcule son contrôle de façon à avoir un suivi stable (absence d'oscillations) et précis (erreur latérale aussi faible que possible). Cette thèse étudie plus précisément le comportement latéral d'un robot du convoi et propose deux contrôleurs s'appuyant sur la mémorisation du chemin suivi par son prédécesseur. Un premier algorithme de contrôle Memo-LAT (Memorization and Look-Ahead Target) calcule une commande latérale continue en utilisant une loi de contrôle analytique. La stabilité de Memo-LAT n'étant pas toujours garantie, nous proposons l'algorithme de contrôle NOC (Non-Oscillatory Convergence) qui prend en compte la courbure du chemin à suivre dans le calcul du comportement latéral. NOC combine une approche géométrique avec une recherche heuristique pour calculer une commande discrète permettant au robot de suivre avec précision le chemin de son prédécesseur sans oscillation. / This thesis deals with the platooning problem which aims to concieve a control algorithm allowing a convoy of vehicles to follow their leader's path. This path, which is initially undefined and unknown to all the following robots, is generated as the leader moves. In this thesis, we choose a local decentralized approach in which each robot of the platoon uses its local perceptions to compute its own commands aiming to achieve a stable (no oscillations) and precise (with a lateral error as small as possible) platooning. More precisely, this thesis studies the lateral behavior of a platoon's robot and introduces two controllers based on the memorization of the robot's predecessor's path. The first algorithm, Memo-LAT (Memorization and Look-Ahead Target), computes a continuous lateral command using an analytic control law. As the stability of Memo-LAT is not always guaranteed, we present NOC (Non-Oscillatory Convergence), a control algorithm which takes into account the path's curvature in the robot's lateral behavior's computation. NOC combines a geometric approach to a heuristic search method to compute a discrete command allowing the robot to follow precisely and without oscillations its predecessor's path

Made available in DSpace on 2017-07-21T14:19:40Z (GMT). No. of bitstreams: 1 Marinaldo Gaspareto.pdf: 1456191 bytes, checksum: ffaf0b449c6b9d107bdf1946a4619315 (MD5) Previous issue date: 2013-09-10 / A problem regarding the implementation of navigation systems for autonomous moving robots is to detect the objects of interest and obstacles which are in the environment. This study considers the detection of walls / low walls of agricultural greenhouses in digital images obtained without illumination control. The proposed approach employs techniques of digital image processing and digital classification to detect the object of interest. The classifier has been developed digital type Naive Bayes. Two important issues when employing classification methods in computer vision is the accuracy of the classifier and the complexity of computing time. The selection of attributes descriptors that comprise a classifier has great impact on these two factors, generally the fewer attributes are required, the lower the computational cost. Regarding it, this study compared the performance of two methods of feature selection based on principal component analysis, named B2 and B4 in two cases. In the first scenario the feature selection was conducted on all the data extracted from all images. The second selection was performed for images grouped by similarity. After selection, the selected attributes for each approach was used to construct the type Naive Bayes classifier with 12, 17, 22 and 27 input variables. The results indicate that the grouping of images is useful when: (a) the distance from the center of the group to the center of the original database exceeds a threshold and (b) a correlation among the descriptors variables and the target variable is greater than in the group as a whole complete data. Keywords: Greenhouses, Autonomous navigation, Selection attributes, Naive Bayes classifiers. / Um problema relativo à implementação de sistemas de navegação para robôs autônomos móveis é a detecção dos objetos de interesse e dos obstáculos que estão no ambiente. Este trabalho considera a detecção das paredes/muretas de estufas agrícolas em imagens digitais adquiridas sem controle de iluminação. A abordagem proposta emprega técnicas de processamento digital de imagens e classificação digital para detectar o objeto de interesse. O classificador digital desenvolvido foi do tipo Naive Bayes. Duas questões importantes quando do emprego de métodos de classificação em visão computacional são a acurácia do classificador e a complexidade de tempo de computação. A seleção dos atributos descritores que compõem um classificador tem grande impacto sobre estes dois fatores, de um modo geral, quanto menos atributos forem necessários, menor o custo computacional. Considerando isso, este trabalho comparou o desempenho de dois métodos de seleção de atributos baseados na análise de componentes principais, chamados B2 e B4 em duas situações. Na primeira situação, a seleção de atributos foi realizada sobre o conjunto dos dados extraídos de todas as imagens. Na segunda, a seleção foi realizada para imagens agrupadas por similaridade. Após a seleção, os atributos selecionados em cada uma das abordagens foram usados para construir classificadores do tipo Naive Bayes com 12, 17, 22 e 27 variáveis de entrada. Os resultados indicam que o agrupamento de imagens é útil quando: (a) a distância do centro do grupo ao centro da base original ultrapassa um limiar e (b) a correlação entre as variáveis descritoras e a variável meta é maior no grupo do que no conjunto completo de dados.

A navegação autônoma é um dos problemas fundamentais na área da robótica móvel. Algoritmos capazes de conduzir um robô até o seu destino de maneira segura e eficiente são um pré-requisito para que robôs móveis possam executar as mais diversas tarefas que são atribuídas a eles com sucesso. Dependendo da complexidade do ambiente e da tarefa que deve ser executada, a programação de algoritmos de navegação não é um problema de solução trivial. Esta tese trata do desenvolvimento de sistemas de navegação autônoma baseados em técnicas de aprendizado supervisionado. Mais especificamente, foram abordados dois problemas distintos: a navegação de robôs/- veículos em ambientes urbanos e a navegação de robôs em ambientes não estruturados. No primeiro caso, o robô/veículo deve evitar obstáculos e se manter na via navegável, a partir de exemplos fornecidos por um motorista humano. No segundo caso, o robô deve identificar e evitar áreas irregulares (maior vibração), reduzindo o consumo de energia. Nesse caso, o aprendizado foi realizado a partir de informações obtidas por sensores. Em ambos os casos, algoritmos de aprendizado supervisionado foram capazes de permitir que os robôs navegassem de maneira segura e eficiente durante os testes experimentais realizados / Autonomous navigation is a fundamental problem in the field of mobile robotics. Algorithms capable of driving a robot to its destination safely and efficiently are a prerequisite for mobile robots to successfully perform different tasks that may be assigned to them. Depending on the complexity of the environment and the task to be executed, programming of navigation algorithms is not a trivial problem. This thesis approaches the development of autonomous navigation systems based on supervised learning techniques. More specifically, two distinct problems have been addressed: a robot/vehicle navigation in urban environments and robot navigation in unstructured environments. In the first case, the robot/vehicle must avoid obstacles and keep itself in the road based on examples provided by a human driver. In the second case, the robot should identify and avoid unstructured areas (higher vibration), reducing energy consumption. In this case, learning was based on information obtained by sensors. In either case, supervised learning algorithms have been capable of allowing the robots to navigate in a safe and efficient manner during the experimental tests