Orientador: Paulo Roberto Gardel Kurka / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-26T21:38:28Z (GMT). No. of bitstreams: 1 Araujo_DarlaCarolinedaSilva_M.pdf: 5678583 bytes, checksum: a6ed9886369705a8853f15d431565a3d (MD5) Previous issue date: 2015 / Resumo: O presente trabalho descreve um método de odometria visual empregando a técnica de fluxo óptico, para estimar o movimento de um robô móvel, através de imagens digitais capturadas de duas câmeras estereoscópicas nele fixadas. Busca-se assim a construção de um mapa para a localização do Robô. Esta proposta, além de alternativa ao cálculo autônomo de movimento realizado por outros tipos de sensores como GPS, laser, sonares, utiliza uma técnica de processamento óptico de grande eficiência computacional. Foi construído um ambiente 3D para simulação do movimento do robô e captura das imagens necessárias para estimar sua trajetória e verificar a acurácia da técnica proposta. Utiliza-se a técnica de fluxo óptico de Lucas Kanade na identificação de características em imagens. Os resultados obtidos neste trabalho são de grande importância para os estudos de navegação robótica / Abstract: This work describes a method of visual odometry using the optical flow technique to estimate the motion of a mobile robot, through digital images captured from two stereoscopic cameras fixed on it, in order to obtain a map of location of the robot. This proposal is an alternative to the autonomous motion calculation performed by other types of sensors such as GPS, laser, sonar, and uses an optical processing technique of high computational efficiency. To check the accuracy of the technique it was necessary to build a 3D environment to simulate the robot performing a trajectory and capture the necessary images to estimate the trajectory. The optical flow technique of Lucas Kanade was used for identifying features in the images. The results of this work are of great importance for future robotic navigation studies / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestra em Engenharia Mecânica

Orientador: Paulo Roberto Gardel Kurka / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-28T12:48:00Z (GMT). No. of bitstreams: 1 DelgadoVargas_JaimeArmando_D.pdf: 6984853 bytes, checksum: 4647b847de6d3abb110923a021a6f376 (MD5) Previous issue date: 2015 / Resumo: A localização de robôs móveis é foco de estudo em diferentes grupos de pesquisa ao redor do mundo. Robôs móveis são equipados com diferentes sensores, utilizando uma variedade de métodos de localização para as tarefas de exploração de ambientes desconhecidos ou para seguir uma trajetória predefinida. Este trabalho apresenta a investigação e implementação de um método robusto e eficiente da estimativa de movimento utilizando visão computacional, conhecido como odometria visual. Também, é estudada a fusão de estimativas de movimento de diferentes sensores através da técnica do filtro de Kalman. Neste trabalho utilizam-se câmeras estereoscópicas com lentes fixas de 9mm e simulações do movimento de uma câmera no ambiente 3D-Max. A validação experimental dos algoritmos é feita em uma plataforma robótica tipo Seekur Jr equipada com Lasers, GPS, encoders e câmeras estereoscópicas. O movimento do robô é estimado pelos diferentes sensores gerando redundância de localização Os algoritmos de odometria visual são validados em ambientes de interiores e exteriores. A velocidade de processamento dos métodos é comparada usando em diferentes processadores de tipo CPU e GPU, indicando a possibilidade um sistema de realização de odometria visual em tempo real / Abstract: The localization of mobile robots problem is addressed to a number of research groups around the world. Mobile robots are equipped with different sensors, using a variety of methods of localization in the exploration of unknown environments or following a pre-defined trajectory. The present work investigates and implements a robust method of estimation of movement using computer vision, known as visual odometry. The work investigates also the results of fusion of the estimates of movement obtained from different sensors, using the Kalman filter technique. Visual odometry uses stereoscopic vision techniques with real time computing in graphic processing units (GPU). Stereoscopic cameras with fixed 9mm lens and movement simulations in the 3d-Max computer environment are used in the present work. Experimental validation of the visual odometry algorithms is made in a Sekur Jr mobile robot platform, equipped with lasers, GPS, wheel encoders and stereoscopic cameras. Movements of the robot are estimated from the different sensors, yielding redundant localization information. The information from such sensors are fused together through the Kalman filter. Visual odometry algorithms are tested in indoors and outdoors navigation experiments. Processing speed of the methods is compared using different processing units: CPU and GPU, indicating the possibility of performing real time visual odometry / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica

Dans ces travaux, nous proposons un système sûr pour la localisation de robot mobile en milieu intérieur structuré. Le principe repose sur l’utilisation de deux capteurs (lidar et caméra monoculaire) combinés astucieusement pour assurer une rapidité de calcul et une robustesse d’utilisation. En choisissant des capteurs reposant sur des principes physiques différents, les chances qu'ils se retrouvent simultanément perturbés sont minimes. L’algorithme de localisation doit être rapide et efficient tout en conservant la possibilité de fournir un mode dégradé dans éventualité où l’un des capteurs serait endommagé. Pour atteindre cet objectif de localisation rapide, nous optimisons le traitement des données à divers niveaux tels que la quantité de données à traiter ou l’optimisation algorithmique. Nous opérons une approximation polygonale des données du lidar 2D ainsi qu’une détection des segments verticaux dans l’image couleur. Le croisement de ces deux informations, à l’aide d’un filtre de Kalman étendu, nous donne alors une localisation fiable. En cas de perte du lidar, le filtre de Kalman peut toujours fonctionner et, en cas de perte de la caméra, le robot peut faire un recalage laser avec le lidar. Les données des deux capteurs peuvent également servir à d’autres objectifs. Les données lidar permettent d’identifier les portes (points de collision potentiels avec des humains), les données caméra peuvent permettre la détection et le suivi des piétons. Les travaux ont été majoritairement menés et validés avec un simulateur robotique avancé (4DV-Sim) puis ont été confirmés par des expériences réelles. Cette méthodologie permet à la fois de développer nos travaux et de valider et améliorer le caractère fonctionnel de cet outil de robotique. / In this work, we propose a safe system for robot navigation in an indoor and structured environment. The main idea is the use of two combined sensors (lidar and monocular camera) to ensure fast computation and robustness. The choice of these sensors is based on the physic principles behind their measures. They are less likely to go blind with the same disturbance. The localization algorithm is fast and efficient while keeping in mind the possibility of a downgraded mode in case of the failure of one sensor. To reach this objective, we optimized the data processing at different levels. We applied a polygonal approximation to the 2D lidar data and a vertical contour detection to the colour image. The fusion of these data in an extended Kalman filter provides a reliable localization system. In case of a lidar failure, the Kalman filter still works, in case of a camera failure the robot can rely upon a lidar scan matching. Data provided by these sensors can also deserve other purposes. The lidar provides us the localization of doors, potential location for encounter with humans. The camera can help to detect and track humans. This work has been done and validated using an advanced robotic simulator (4DV-Sim), then confirmed with real experiments. This methodology allowed us to both develop our ideas and confirm the usefulness of this robotic tool.

This master‘s thesis deals with development and realisation of robotic lawn mower with satelite navigation. It begins with preparation of a platform for outdoor mobile robot navigation and it’s control HW and SW. There are discussed different options of navigation both commercial and experimental. Further on I have chosen the right GNSS receiver based on market research and user experience. The GNSS receiver’s parameters are measured with different antennas. Following with the choice of suitable open-source control unit and it’s software implementation. Furthermore control from a companion computer is described and physical realisation is done. In the end of the thesis activation of the whole mower is performed and described. Lastly there are discussed possible ways of future development.

No description available.

Os métodos de Aprendizagem por Reforço (AR) se mostram adequados para problemas de tomadas de decisões em diversos domínios por sua estrutura flexível e adaptável. Apesar de promissores, os métodos AR frequentemente tem seu campo de atuação prático restrito a problemas com espaço de estados de pequeno ou médio porte devido em muito à forma com que realizam a estimativa da função de avaliação. Nesta tese, uma nova abordagem de AR, denominada de Agente Topológico de Aprendizagem por Reforço (ATAR), inspirada em aprendizagem latente, é proposta para acelerar a aprendizagem por reforço através de um mecanismo alternativo de seleção dos pares estado-ação para atualização da estimativa da função de avaliação. A aprendizagem latente refere-se à aprendizagem animal que ocorre na ausência de reforço e que não é aparente até que um sinal de reforço seja percebido pelo agente. Este aprendizado faz com que um agente aprenda parcialmente uma tarefa mesmo antes que este receba qualquer sinal de reforço. Mapas Cognitivos são usualmente empregados para codificar a informação do ambiente em que o agente está imerso. Desta forma, o ATAR usa um mapa topológico, baseado em Mapas Auto-Organizáveis, para realizar as funções do mapa cognitivo e permitir um mecanismo simples de propagação das atualizações. O ATAR foi testado, em simulação, para planejamento de navegação de um robô móvel em ambientes inicialmente desconhecidos e não-estruturados. Comparações com outros seis algoritmos AR avaliaram comparativamente o desempenho do agente proposto na navegação. Os resultados obtidos são promissores e comparáveis com os algoritmos AR mais rápidos testados, alcançando em alguns ensaios desempenho superior aos dos demais algoritmos - principalmente nas simulações que consideram situações observadas em ambientes não-estruturados. Três características do ATAR original foram alteradas para tornar ainda mais viável sua aplicação prática: (i) mudanças no mapa topológico para reduzir o número de vértices, (ii) mudança na heurística usada na seleção das ações do agente e (iii) variações na estratégia de exploração do ATAR. Do ponto (i), foi proposto e implementado um novo mapa topológico, o Mapa Topológico Incremental Classificador MTIC, que a partir da classificação dos estados do ambiente gera os vértices de uma triangularização de Watson. O ponto (ii) criou um método aplicável a outros problemas de planejamento de trajetória em grafos denominado de Melhoria das trajetórias por detecção de ponto interior. O terceiro estudou estratégias direcionadas de exploração como uma opção para acelerar o aprendizado do ATAR. / Reinforcement Learning (RL) methods have shown to be a good choice for decision-making problems due to their flexible and adaptive characteristics. Despite such promising features, RL methods often have their practical application restricted to small or medium size (at state, or state-action, space) problems mainly because of their standard strategies for value function estimation. In this thesis, a new RL approach, called \"Topological Reinforcement Learning Agent\" - TRLA, is proposed to accelerate learning through an alternative mechanism to update the state-action value function. TRLA is inspired in latent learning, which refers to animal learning that occurs in the absence of reinforcements and that is not visible until an environmental reinforcement is perceived. This concept considers that part of a task can be learned even before the agent receives any indication of how to perform such a task. Cognitive Maps are usually used to encode information about the environment where the agent is immersed. Thus, the TRLA uses a topological map, based on Self-Organized Maps, to implement cognitive map functions and permit a new simple mechanism to execute the propagation of state-action updates. The chosen problem to test TRLA is the simulation of a mobile robot navigation in some initially unknown and unstructured environments. Performance comparisons of the TRLA with six other RL algorithms were carried out to the execution of the navigation task. The obtained results are very promising and comparable with some of faster RL algorithms simulated. In some experiments, the TRLA\'s performance overcomes the others especially in simulations with unstructured environments. Three characteristics of the original TRLA were modified to make it more suitable for real implementations: (i) changes in the topological map construction to reduce the vertices number, (ii) changes in the agents heuristic for action selection, and (iii) variations on the TRLAs strategy for exploration of the state-action space. In (i), a new procedure to construct topological maps was proposed and implemented, the Incremental Classifier Topological Map ICTM, which generates the vertices for a Watsons triangulation from the classification of the input states. In (ii), it was proposed a method to optimize trajectory planning problems based on graphs, denoted \"trajectory improvement from inner point detection\". The third point considers directed exploration strategies as an option for TRLA\'s learning acceleration.

This thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen. Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events. In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly. A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation. In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction. <b>Keywords:</b>mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction

We design and analyze algorithms for the following two robot navigation problems: 1. TARGET SEARCH. Given a robot located at a point s in the plane, how will a robot navigate to a goal t in the presence of unknown obstacles ? 2. LOCALIZATION. A robot is "lost" in an environment with a map of its surroundings. How will it find its true location by traveling the minimum distance ? Since efficient algorithms for these two problems will make a robot completely autonomous, they have held the interest of both robotics and computer science communities. Previous work has focussed mainly on designing competitive algorithms where the robot's performance is compared to that of an omniscient adversary. For example, a competitive algorithm for target search will compare the distance traveled by the robot with the shortest path from s to t. We analyze these problems from the worst-case perspective, which, in our view, is a more appropriate measure. Our results are : 1. For target search, we analyze an algorithm called Dynamic A*. The robot continuously moves to the goal on the shortest path which it recomputes on the discovery of obstacles. A variant of this algorithm has been employed in Mars Rover prototypes. We show that D* takes O(n log n) time on planar graphs and also show a comparable bound on arbitrary graphs. Thus, our results show that D* combines the optimistic possibility of reaching the goal very soon while competing with depth-first search within a logarithmic factor. 2. For the localization problem, worst-case analysis compares the performance of the robot with the optimal decision tree over the set of possible locations. No approximation algorithm has been known. We give a polylogarithmic approximation algorithm and also show a near-tight lower bound for the grid graphs commonly used in practice. The key idea is to plan travel on a "majority-rule map" which eliminates uncertainty and permits a link to the half-Group Steiner problem. We also extend the problem to polygonal maps by discretizing the domain using novel geometric techniques.

<p>This thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen.</p><p>Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events.</p><p>In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly.</p><p>A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation.</p><p>In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction.</p><p><b>Keywords:</b>mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction</p>

The navigation tasks in advanced home robotic applications incorporating reliable revisiting strategies are dependent on very low cost but nevertheless rather accurate localization systems. In this paper a localization system based on the principle of trilateration is described. The proposed system uses only a single small base station, but achieves accuracies comparable to systems using spread beacons and it performs sufficiently for map building. Thus it is a standalone system and needs no odometry or other auxiliary sensors. Furthermore a new approach for the problem of the reliably detection of areas without direct line of sight is presented. The described system is very low cost and it is designed for use in indoor service robotics. The paper gives an overview on the system concept and special design solutions and proves the possible performances with experimental results.