This thesis addresses the problems of registration, localisation and simultaneous localisation and mapping (SLAM), relying particularly on local visual features extracted from camera images. These fundamental problems in mobile robot navigation are tightly coupled. Localisation requires a representation of the environment (a map) and registration methods to estimate the pose of the robot relative to the map given the robot’s sensory readings. To create a map, sensor data must be accumulated into a consistent representation and therefore the pose of the robot needs to be estimated, which is again the problem of localisation. The major contributions of this thesis are new methods proposed to address the registration, localisation and SLAM problems, considering two different sensor configurations. The first part of the thesis concerns a sensor configuration consisting of an omni-directional camera and odometry, while the second part assumes a standard camera together with a 3D laser range scanner. The main difference is that the former configuration allows for a very inexpensive set-up and (considering the possibility to include visual odometry) the realisation of purely visual navigation approaches. By contrast, the second configuration was chosen to study the usefulness of colour or intensity information in connection with 3D point clouds (“coloured point clouds”), both for improved 3D resolution (“super resolution”) and approaches to the fundamental problems of navigation that exploit the complementary strengths of visual and range information. Considering the omni-directional camera/odometry setup, the first part introduces a new registration method based on a measure of image similarity. This registration method is then used to develop a localisation method, which is robust to the changes in dynamic environments, and a visual approach to metric SLAM, which does not require position estimation of local image features and thus provides a very efficient approach. The second part, which considers a standard camera together with a 3D laser range scanner, starts with the proposal and evaluation of non-iterative interpolation methods. These methods use colour information from the camera to obtain range information at the resolution of the camera image, or even with sub-pixel accuracy, from the low resolution range information provided by the range scanner. Based on the ability to determine depth values for local visual features, a new registration method is then introduced, which combines the depth of local image features and variance estimates obtained from the 3D laser range scanner to realise a vision-aided 6D registration method, which does not require an initial pose estimate. This is possible because of the discriminative power of the local image features used to determine point correspondences (data association). The vision-aided registration method is further developed into a 6D SLAM approach where the optimisation constraint is based on distances of paired local visual features. Finally, the methods introduced in the second part are combined with a novel adaptive normal distribution transform (NDT) representation of coloured 3D point clouds into a robotic difference detection system.

A navegação autônoma é uma tarefa fundamental na robótica móvel. Para que esta tarefa seja realizada corretamente é necessário um sistema inteligente de controle e navegação associado ao sistema sensorial. Este projeto apresenta o desenvolvimento de um sistema de controle para a navegação de veículos e robôs móveis autônomos. A abordagem utilizada neste trabalho utiliza Redes Neurais Artificiais para o aprendizado de Autômatos Finitos de forma que os robôs possam lidar com os dados provenientes de seus sensores mesmo estando sujeitos a imprecisões e erros e ao mesmo tempo permite que sejam consideradas as diferentes situações e estados em que estes robôs se encontram (contexto). Dessa forma, é possível decidir como agir para realizar o controle da sua movimentação, e assim executar tarefas de controle e navegação das mais simples até as mais complexas e de alto nível. Portanto, esta dissertação visa utilizar Redes Neurais Artificiais para reconhecer o estado atual (contexto) do robô em relação ao ambiente em que está inserido. Uma vez que seja identificado seu estado, o que pode inclusive incluir a identificação de sua posição em relação aos elementos presentes no ambiente, o robô será capaz de decidir qual a ação/comportamento que deverá ser executado. O sistema de controle e navegação irá implementar um Autômato Finito que a partir de um estado atual define uma ação corrente, sendo capaz de identificar a mudança de estados, e assim alternar entre diferentes comportamentos previamente definidos. De modo a validar esta proposta, diversos experimentos foram realizados através do uso de um simulador robótico (Player-Stage), e através de testes realizados com robôs reais (Pioneer P3-AT, SRV-1 e veículos automatizados) / Autonomous navigation is a fundamental task in mobile robotics. In order to accurately perform this task it is necessary an intelligent navigation and control system associated to the sensorial system. This project presents the development of a control system for autonomous mobile robots and vehicles navigation. The adopted approach uses Artificial Neural Networks for Finite State Machine learning, allowing the robots to deal with sensorial data even when this data is not precise and correct. Simultaneously, it allows the robots to consider the different situations and states they are inserted in (context detection). This way, it is possible to decide how to proceed with motion control and then execute navigation and control tasks from the most simple ones until the most complex and high level tasks. So, this work uses Artificial Neural Networks to recognize the robots current state (context) at the environment where it is inserted. Once the state is detected, including identification of robots position according to environment elements, the robot will be able to determine the action/- behavior to be executed. The navigation and control system implements a Finite State Machine deciding the current action from current state, being able to identify state changes, alternating between different previously defined behaviors. In order to validade this approach, many experiments were performed with the use of a robotic simulator (Player-Stage), and carrying out tests with real robots (Pioneer P3-AT, SRV-1 and autonomous vehicles)

A segurança no tráfego de carros é um assunto em foco nos dias de hoje e, dentro dele, podem-se citar os sistemas de auxílio ao motorista que vêm sendo desenvolvidos com a finalidade de reduzir o grande número de fatalidades em acidentes de trânsito. Tais sistemas de auxílio buscam mitigar falhas humanas como falta de atenção e imprudência. Visto isso, o projeto SENA, desenvolvido pelo Laboratório de Robótica Móvel da Escola de Engenharia de São Carlos, busca contribuir com a evolução dessa assistência ao motorista. O presente trabalho realiza um estudo sobre uma técnica de inteligência artificial chamada de Redes Bayesianas. Essa técnica merece atenção em virtude de sua capacidade de tratar dados incertos em forma de probabilidades. A rede desenvolvida por esse trabalho utiliza, como dados de entrada, os classificadores em desenvolvimento no projeto SENA e tem como resposta um comportamento que o veículo deve executar, por um ser humano ou por um planejador de trajetórias. Em função da alta dimensionalidade do problema abordado, foram realizados dois experimentos em ambiente simulado de duas situações distintas. A primeira, um teste de frenagem próximo a um ponto de intersecção e a segunda, um cenário de entroncamento. Os testes feitos com a rede indicam que classificadores pouco discriminantes deixam o sistema mais propenso a erros e que erros na localização do ego-veículo afetam mais o sistema se comparado a erros na localização dos outros veículos. Os experimentos realizados mostram a necessidade de um sistema de tempo real e um hardware mais adequado para tratar as informações mais rapidamente / The safety of cars in traffic scenarios is being addressed on the past few years. One of its topics is the Advanced Driver-Assistance Systems which have been developed to reduce the fatality numbers of traffic accidents. These systems try to decrease human failures, such as imprudence and lack of attention while driving. For these reasons, the SENA project, in progress on the Mobile Robotics Laboratory at the Sao Carlos School of Engineering (EESC), aims to contribute for the evolution of these assistance systems. This work studies an artificial intelligence technique called Bayesian Networks. It deserves our attention due to its capability of handling uncertainties with probability distributions. The network developed in this Masters Thesis has, as input, the result of the classifiers used on SENA project and has, as output, a behavior which has to be performed by the vehicle with a driver or autonomously by the means of a path planner. Due to the high dimensionality of this issue, two different tests have been carried out. The first one was a braking experiment near a intersection point and the other one was a T-junction scenario. The tests made indicate that weak classifiers leaves the system more instable and error-prone and localization errors of the ego-vehicle have a stronger effect than just localization errors of other traffic participants. The experiments have shown that there is a necessity for a real-time system and a hardware more suitable to deal quickly with the information

Este projeto apresenta uma metodologia aplicada à análise da viabilidade de se otimizar código fonte C para o processador embarcado Nios II. Esta metodologia utiliza ferramentas de análise de código que traçam o perfil da aplicação, identificando suas partes críticas em relação ao tempo de execução, as quais são o gprof e o performance counter. Para otimizar o código para o processador Nios II, são utilizadas tanto instruções customizadas quanto uma ferramenta automática de aceleração de código, o compilador C2H. Como casos de estudo, foram escolhidos três algoritmos devido à sua importância no campo da robótica móvel, sendo eles o gaxpy, o EKF e o SIFT. A partir da aplicação da metodologia para se otimizar cada um dos casos, foi comparada a eficiência tanto das ferramentas de análise de código, quanto das ferramentas de otimização, bem como a validade da metodologia proposta / This project presents a methodology applied to analyze the viability of C source code optimization for the Nios II embedded processor. This methodology utilizes the gprof and performance counter source code analysis tools to profile the source code of an application, and identify its critical time consuming parts. The optimization of C source code for the Nios II processor was performed using custom instructions and an automatic source code acceleration tool, the C2H compiler. Three algorithms were chosen as study cases, based on their importance to mobile robotics. Those were the gaxpy, EKF and SIFT algorithms. After applying the presented methodology to optimize each study case, efficiency comparisons were made between the source code analysis tools, as well between the optimization tools, in order to validate the presented methodology

A maior parte dos mapas empregados em tarefas de navegação por robôs móveis representam apenas informações espaciais do ambiente. Outros tipos de informações, que poderiam ser obtidos dos sensores do robô e incorporados à representação, são desprezados. Hoje em dia é comum um robô móvel conter sensores de distância e um sistema de visão, o que permitiria a princípio usá-lo na realização de tarefas complexas e gerais de maneira autônoma, dada uma representação adequada e um meio de extrair diretamente dos sensores o conhecimento necessário. Uma representação possível nesse contexto consiste no acréscimo de informação semântica aos mapas métricos, como por exemplo a segmentação do ambiente seguida da rotulação de cada uma de suas partes. O presente trabalho propõe uma maneira de estruturar a informação espacial criando um mapa semântico do ambiente que representa, além de obstáculos, um vínculo entre estes e as imagens segmentadas correspondentes obtidas por um sistema de visão omnidirecional. A representação é implementada por uma descrição relacional do domínio, que quando instanciada gera um campo aleatório condicionado, onde são realizadas as inferências. Modelos que combinam probabilidade e lógica de primeira ordem são mais expressivos e adequados para estruturar informações espaciais em semânticas. / Most maps used in navigational tasks by mobile robots represent only environmental spatial information. Other kinds of information, that might be obtained from the sensors of the robot and incorporated in the representation, are negleted. Nowadays it is common for mobile robots to have distance sensors and a vision system, which could in principle be used to accomplish complex and general tasks in an autonomously manner, given an adequate representation and a way to extract directly from the sensors the necessary knowledge. A possible representation in this context consists of the addition of semantic information to metric maps, as for example the environment segmentation followed by an attribution of labels to them. This work proposes a way to structure the spatial information in order to create a semantic map representing, beyond obstacles, an anchoring between them and the correspondent segmented images obtained by an omnidirectional vision system. The representation is implemented by a domains relational description that, when instantiated, produces a conditional random field, which supports the inferences. Models that combine probability and firstorder logic are more expressive and adequate to structure spatial in semantic information.

In this thesis, various control problems originating from the field of mobile robotics are considered. In particular, the thesis deals with problems that are related to the interaction and coordination of multiple mobile units. The scientific contributions are presented in five papers that together constitute the main part of the thesis. The papers are preceded by a longer introductory part, in which some important results from control theory, data processing and robotics are reviewed. In the first of the appended papers, two stabilizing tracking controls are proposed for a non-holonomic robot platform of unicycle type. Tolerance to errors and other properties of the controllers are discussed and a reactive obstacle avoidance control, that can easily be incorporated with the proposed tracking controls, is suggested. In Paper B, the results from Paper~A are extended to multi-agent systems. It is demonstrated how the tracking controls from Paper A can be used as building blocks when putting together formations of robots, in which each robot maintains a fixed position relative its neighbors during translation. In addition, switching between the different control functions is shown to be robust, implying that it is possible to change the shape of a formation on-line. In the first two papers, the tracking problem is facilitated by the assumption that the approximate velocity of the target/leader is known to the tracking robot. Paper C treats the the case where the target velocity is neither directly measurable with the available sensor setup, nor possible to obtain through communication with neighboring agents. Straight-forward computation of the target velocity from available sensor data unfortunately tend to enhance measurement errors and give unreliable estimates. To overcome the difficulties, an alternative approach to velocity estimation is proposed, motivated by the local observability of the given control system. Paper D deals with another problematic aspect of data acquisition. When using range sensors, one often obtains a mixed data set with measurements originating from many different sources. This problem would, for instance, be encountered by a robot moving in a formation, where it was surrounded by other agents. There exist established techniques for sorting mixed data sets off-line, but for time-depending systems where data need to be sorted on-line and only small time delays can be tolerated, established methods fail. The solution presented in the paper is a prediction-correction type algorithm, referred to as CCIA (Classification Correction and Identification algorithm). Finally, in Paper E, we consider the problem of maintaining connectivity in a multi-agent system. Often inter-agent communication abilities are associated with some proximity constraints, so when the robots move in relation to each other, communication links both break and form. In the paper we present a framework for analysis that makes it possible to compute a set of general constraints which, if satisfied, are sufficient to guarantee maintained communication for a given multi-agent system. Constraints are computed for two sorts of consensus-based systems and the results are verified in simulations. / QC 20100715

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Orientador: Prof. Dr. Wagner Tanaka Botelho / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2016. / A robotica movel tem se desenvolvido fortemente nas ultimas decadas. Os estudos de robôs com pernas, em especial, ganham destaque pela capacidade de transpor obstaculos com maior efetividade em relação aos demais meios de locomoção. Aliado a este estudo, encontra-se a robotica bioinspirada, que faz uso de elementos funcionais da natureza como inspiração para a robótica. A construção do prototipo do TB-Horse II, objetivo principal deste trabalho, é um robô quadrupede bioinspirado no cavalo. Este robô possui diversas aplicações, como por exemplo, no resgate de feridos, no transporte de cargas frágeis, entre outras. Entretanto, antes do seu desenvolvimento, foi realizado o estudo, análise e simulação do projeto em CAD-3D proposto na primeira vers~ao do robô, conhecida como TB-Horse I. Após a análise, juntamente com o estudo da biodinâmica do cavalo, foi poss'vel propor um novo projeto mecânico estrutural, simulado no Virtual Robot Experimentation Platform (V-REP) e desenvolvido no Autodesk Inventor, conhecido como TB-Horse II. A estabilidade do TB-Horse II foi analisada e validada no V-REP. O tamanho das pernas foi investigado e dois métodos matematicos foram propostos com base nos dados reais da locomoção do cavalo. O cavalo possui diversos tipos de andamentos, sendo a marcha a locomoção o utilizada neste trabalho. Neste estudo, pode-se concluir que o TB-Horse II teve maior estabilidade quando as pernas da frente são maiores que as traseiras. Além disso, o projeto eletrînico foi simulado no Proteus. Finalmente, o protótipo do TB-Horse II foi construído e validado no mundo real, em um terreno plano e sem obstáculos, juntamente com os circuitos eletrônicos. Vale ressaltar que uma estrutura de apoio foi construção para auxiliar na validação do TB-Horse II durante os experimentos. Este robô tem como pontos fortes uma estrutura mais parecida com o cavalo real e aliado a bioinspirac~ao o movimento, possibilita um controle maior da sua estabilidade. / The mobile robotics has been strongly developed in recent decades. The robots with legs are highlighted by the ability to overpass obstacles more eectively compared with other types of locomotion. The bio-inspired robotics use functional elements of natures for inspiration. The development of the TB-Horse II prototype is the main target of this work. It is a bio-inspired quadruped robot with biological features of horse locomotion. The robot can be used to rescue injured people, to carry fragile loads, among others applications. However, before its construction, it was necessary to analyze and simulate the CAD-3D structural mechanical design already developed in the rst version of the robot, called TB-Horse I. After that, and also with the study of horse biodynamic, it was possible to propose the TB-Horse II. The mechanical design of this robot has similarity with real horse, and also the stability is controlled because of its bio-inspiration. This robot was simulated in the Virtual Robot Experimentation Platform (V-REP), the mechanical structure was designed in the Autodesk Inventor and the electronic project was proposed and simulated using the Proteus software, before its implementation. The stability analysis of the robot was validated in V-REP. The leg length was investigated and two methods were proposed based on the real data of the horse's locomotion. It is important to point out that horse has dierent types of locomotion. However, the gait is used in the simulation and real experiment. Based on the results obtained, it is possible to conclude that the TB-Horse II had more stability when the front legs are longer than the rear legs. Finally, the robot prototype was developed and the experimental validation was realized on a at ground without obstacles. In order to avoid the robot to fall over unsafe and prevent it from being damaged in the experiments, a support structure was developed.

This is a thesis on outdoor monocular visual SLAM in natural environments. The techniques proposed herein aim at estimating camera pose and 3D geometrical structure of the surrounding environment. This problem statement was motivated by the GPS-denied scenario for a sea-surface vehicle developed at Plymouth University named Springer. The algorithms proposed in this thesis are mainly adapted for the Springer’s environmental conditions, so that the vehicle can navigate on a vision based localization system when GPS is not available; such environments include estuarine areas, forests and the occasional semi-urban territories. The research objectives are constrained versions of the ever-abiding problems in the fields of multiple view geometry and mobile robotics. The research is proposing new techniques or improving existing ones for problems such as scene reconstruction, relative camera pose recovery and filtering, always in the context of the aforementioned landscapes (i.e., rivers, forests, etc.). Although visual tracking is paramount for the generation of data point correspondences, this thesis focuses primarily on the geometric aspect of the problem as well as with the probabilistic framework in which the optimization of pose and structure estimates takes place. Besides algorithms, the deliverables of this research should include the respective implementations and test data for these algorithms in the form of a software library and a dataset containing footage of estuarine regions taken from a boat, along with synchronized sensor logs. This thesis is not the final analysis on vision based navigation. It merely proposes various solutions for the localization problem of a vehicle navigating in natural environments either on land or on the surface of the water. Although these solutions can be used to provide position and orientation estimates when GPS is not available, they have limitations and there is still a vast new world of ideas to be explored.

Made available in DSpace on 2014-06-11T19:29:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-08-02Bitstream added on 2014-06-13T19:38:55Z : No. of bitstreams: 1 jodas_ds_me_sjrp.pdf: 1056058 bytes, checksum: eb5f8e6ec063430577edbf081cc017c4 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A utilização de robôs móveis mostra-se importante em atividades onde a atuação do ser humano é difícil ou perigosa. A exploração em locais de difícil acesso, como por exemplo em operações de resgate e em missões espaciais, é uma situação onde robôs móveis são frequentemente utilizados para evitar exposição dos especialistas humanos a situações de riscos. Na agricultura, robôs móveis são utilizados em tarefas de cultivo e na aplicação de agrotóxicos em quantidades mínimas para reduzir a poluição do meio ambiente. Neste trabalho é apresentado o desenvolvimento de um sistema para controlar a navegação de um robô móvel autônomo por caminhos em plantações. O controle da direção do robô é realizado com base em imagens das trilhas as quais, após um processamento prévio, para extração de características, são submetidas a máquinas de vetores de suporte, para a definição da rota a ser seguida. O objetivo do projeto no qual este trabalho se insere é o controle do robô em tempo real, para tanto, o sistema foi implementado em hardware para mostrar que o ganho de desempenho pode ser melhor em relação à execução em software. Neste trabalho, relata-se a implementação de uma máquina de vetores de suporte a qual apresentou uma precisão em torno de 93% da rota adequada / The use of mobile robots turns out to be interesting in activities where the actions of human beings is difficult or dangerous. The exploration in areas of difficult access, such as in rescue operations and in space missions, is a situation where mobile robots are often used to avoid exposure of human experts to risky situations. In agriculture, mobile robots are used in tasks of cultivation and application of pesticides in minute quantities to reduce environmental pollution. In this paper we present the development of a system to control an autonomous mobile robot navigation through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features, and then submitting such characteristic features to a support vector machine to find out the most appropriate route. As the overall goal of the project to which this work is connected is the robot control in real time, the system was embedded onto a hardware platform to show that the performance gain can be better if compared to execution in software. However, in this paper we report the software implementation of a support vector machine, which presented around 93% accuracy in predicting the appropriate route