892 research outputs found
Percepção do ambiente urbano e navegação usando visĂŁo robĂłtica : concepção e implementação aplicado Ă veĂculo autĂ´nomo
Orientadores: Janito Vaqueiro Ferreira, Alessandro CorrĂŞa VictorinoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O desenvolvimento de veĂculos autĂ´nomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefĂcios na redução de acidentes, no aumentando da qualidade de vida e tambĂ©m na redução de custos. VeĂculos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera tĂŞm recebido grande atenção pelo motivo de que eles sĂŁo de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas tambĂ©m desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veĂculos podem gerar observações parciais e tambĂ©m estas observações sĂŁo muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veĂculo. Nesta tese, este problema de percepção Ă© analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisĂŁo em navegação autĂ´noma. Projeta-se um sistema de percepção que permita veĂculos robĂłticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prĂ©vio do ambiente e considerando a presença de objetos dinâmicos tais como veĂculos. Propõe-se um novo mĂ©todo baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estĂ©reo, a qual Ă© vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisĂŁo no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possĂveis caminhos a partir do centro de referencia do veĂculo e com base nisto, duas novas estratĂ©gias sĂŁo propostas. Em primeiro, uma nova estratĂ©gia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual Ă© modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle sĂŁo implementados e validados experimentalmente em condições reais usando um veĂculo autĂ´nomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmeraAbstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensorsDoutoradoMecanica dos SĂłlidos e Projeto MecanicoDoutor em Engenharia Mecânic
RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach
The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology
Viewfinder: final activity report
The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources.
The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation.
The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein
Learning cognitive maps: Finding useful structure in an uncertain world
In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg
Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly
An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.
In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made
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