Dissertação apresentada para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores, na Faculdade de Engenharia da Universidade do Porto, sob a orientação dos Profs. Drs. José António Tenreiro Machado e Martins de Carvalho

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major de Automação. Faculdade de Engenharia. Universidade do Porto. 2008

Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Nuno Lau / Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 2008

Tese de doutoramento. Faculdade de Engenharia. Universidade do Porto. 1998

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 2010

Dissertação apresentada para obtenção do grau de Doutor em Engenharia Electrotécnica e de Computadores, na Faculdade de Engenharia daUniversidade do Porto, sob a orientação dos Professores Doutores Adriano da Silva Carvalho e Paulo José Cerqueira Gomes da Costa

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 2009

SLAM (Simulatenous Localization and Mapping) es quizá el problema más importante a solucionar en robótica para construir robots móviles verdaderamente autónomos. El SLAM es acerca de cómo un robot móvil opera en un entorno a priori desconocido, utilizando únicamente sus sensores de abordo, mientras construye un mapa de dicho entorno que al mismo tiempo utiliza para localizarse. Los sensores del robot tienen un gran impacto en los algoritmos usados en SLAM. Los primeros enfoques se centraron en el uso de sensores de rango como sonares o láseres. Sin embargo hay algunas desventajas relacionadas con su utilización: La asociación de datos es difícil, son costosos, habitualmente están limitados a mapas 2D y tienen alto costo computacional debido al gran número de características (features) que producen. Lo anterior ha propiciado que enfoques recientes se estén moviendo hacia el uso de cámaras como sensor principal. Estas se han vuelto muy atractivas para los investigadores de la robótica, dado que generan mucha información, facilitan la asociación de datos, están bien adaptadas para sistemas embebidos: son ligeras, baratas y ahorran energía. Usando visión, un robot puede localizarse así mismo usando objetos comunes como landmarks. Sin embargo, a diferencia de los sensores de rango, que proveen información angular y de rango, una cámara es un sensor proyectivo que mide el bearing (ángulo) respecto a objetos de la imagen. Por lo que la profundidad (range) no puede ser obtenida en una sola toma. Este hecho ha motivado la aparición de una nueva familia de métodos de SLAM: Los Bearing-Only SLAM methods, los cuales están basados en técnicas especiales para la inicialización de features, permitiendo el uso de sensores de bearing en SLAM. Esta tesis se centra en el estudio de la problemática del Bearing-Only SLAM: da una descripción extensa del tema, recapitula los retos actuales a resolver y propone nuevos métodos y algoritmos enfocados a tratar diferentes sub problemas concernientes esta problemática en general. Estos sub problemas deben de ser tratados, de manera que sea posible construir sistemas capaces de operar en entornos diversos y complejos. La investigación descrita en esta disertación ha sido dividida en tres partes: 3DOF Bearing-Only SLAM: El proceso de inicialización de nuevas features es quizá el sub problema más importante a tratar en Bearing-Only SLAM. En esta parte de la tesis se introduce un nuevo método llamado Delayed Inverse Depth Features Initialization (para 3DOF y asumiendo odometría). Este método utiliza una parametrización inversa, donde la profundidad e incertidumbre iníciales de cada feature son dinámicamente estimadas previamente a que una feature sea declarada como un nuevo landmark en el mapa estocástico. También se presenta un sistema de SLAM basado en sonido, llamado SSLAM el cual usa fuentes de sonido como features del mapa. La contribución del SSLAM es demostrar la viabilidad de la inclusión del sentido auditivo en SLAM y mostrar que es factible utilizar sensores alternativos en Bearing-Only SLAM. Métodos de asociación de datos para SLAM basado en visión: El problema de la asociación de datos es quizá uno de los problemas más difíciles en robótica y también uno de los sub problemas más importantes a tratar en SLAM. Consiste en determinar si las mediciones de un sensor tomadas en tiempos diferentes, corresponden al mismo objeto físico del mundo. En esta parte de la tesis, se proponen diferentes métodos que tratan el problema de la asociación de datos en un contexto de SLAM basado en visión. SLAM monocular de 6DOF: El SLAM monocular de 6DOF quizá representa la variante más extrema del SLAM, dado que una cámara en mano es utilizada como la única entrada sensorial del sistema. En esta parte de la tesis, se extiende el algoritmo de 2DOF Bearing-Only SLAM para ser aplicado en un contexto de SLAM monocular. También se propone un nuevo esquema llamado SLAM Monocular Distribuido, enfocado en el problema de construir y mantener mapas consistentes de grandes entornos en tiempo real. La idea es dividir la estimación total del sistema en dos procesos de estimación concurrentes. Primero un método actual de SLAM monocular (Virtual Sensor) es modificado como un complejo sensor virtual que emula sensores típicos, como el laser para medición de rango y encoders para odometría. Después otro método tradicional de SLAM (Global SLAM) es acoplado para construir y mantener el mapa final. Numerosas referencias bibliográficas, graficas, comparaciones, simulaciones y experimentos con datos reales de sensores, son presentador con el fin de mostrar el desempeño de los métodos propuestos. / Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. SLAM is about on how can a mobile robot operate in an a priori unknown environment and use only onboard sensors to simultaneously build a map of its surroundings and use it to track its position. The robot’s sensors have a large impact on the algorithm used for SLAM. Early SLAM approaches focused on the use of range sensors as sonar rings or lasers. Nevertheless there are some disadvantages with the use of range sensors in SLAM: Correspondence or data association is difficult. They are expensive. They are generally limited to 2D maps and computational overhead due to large number of features. The aforementioned issues have propitiated that recent work is moving towards the use of cameras as the primary sensing modality. Cameras have become more and more interesting for the robotic research community, because it yield a lot of information allowing reliable data association. Cameras are well adapted for embedded systems: they are light, cheap and power saving. Using vision, a robot can localize itself using common objects as landmarks. On the other hand, at difference of range sensors (i.e. sonar or laser) which provides range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single frame. This fact has propitiated the emergence of a new family of SLAM methods: The Bearing-Only SLAM methods, which mainly relies in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. This thesis is focused on the study of the Bearing-Only SLAM problematic: It gives an extensive overview of the subject. It point out the principal challenges nowadays. And it presents new methods and algorithms which address different sub problems concerning to the Bearing-Only SLAM problematic. These sub problems must be solved, in order to build systems capable of operating in extremely diverse and complex environments. The research described in this dissertation has been divided into three parts: 3DOF Bearing-Only SLAM: The initialization process for new features is perhaps the most important sub problem for addressing in Bearing-Only SLAM. In this part of the thesis we introduce a novel method called Delayed Inverse Depth Features Initialization for a 3DOF odometry-available context. In this method, which uses an inverse depth parameterization, initial depth and uncertainty of each feature are dynamically estimated priors to add the new landmark in the stochastic map. We also present a Sound-based SLAM system, called SSLAM, which uses “Sound Sources” as map’s features. The main contributions of the SSLAM are demonstrating the viability on the inclusion of the hearing sense in SLAM and show that is straightforward to use alternative bearing in SLAM systems. Data association methods for camera-based SLAM: the data association problem is possibly one of the hardest problems in robotic and also one of the most important sub problems to solve in SLAM. The correspondence problem is the problem of determining if sensor measurements taken at different points in time correspond to the same physical object in the world. In this part of the thesis, we propose different methods for addressing the data association problem in a context of vision-based SLAM. 6DOF Monocular SLAM: 6-DOF monocular SLAM possibly represents the harder variant of SLAM, since a low cost hand-held camera is used as the only sensory input to the system. In this part of the thesis, we extend our 2DOF Bearing-Only SLAM algorithm for being used in a monocular SLAM context. Also a novel framework called Distributed Monocular SLAM is proposed for addressing the problem of building and maintaining a global and consistent map of large environments at real time. The key idea is to divide the whole estimation into two concurrent estimation processes. First a state of the art monocular SLAM method (Called Virtual Sensor) is modified as a complex virtual sensor that emulates typical sensors such as laser for range measurement and encoders for dead reckoning. Afterward, a classic SLAM method (called Global SLAM) is plugged in for building and maintaining the final map. Several references, graphics, comparisons, simulations and experiments with real data are presented in order to demonstrate the performance of the methods.

Objectives: In order to allow humanoid robots to collaborate with humans and free them from dangerous or unpleasant activities, they must be able to perform reliable gaits. In last decades, the balance of humanoid robots has attracted an increasing attention of the scientific community. However, even though important results have been achieved in the theoretical level and in simulations, the balance control of physical robots still remains an open issue. Since humanoid robots are intended to operate in everyday environments, it is fundamental to validate balance control approaches in physical platforms and to face them to stressing conditions. Methodology: In this work we propose a balance control system that improves walking robustness and disturbance rejection. The approach combines the zero moment point stability criterion with the control of angular momentum and step timing. Results: The experiments demonstrate that the combined use of angular momentum and timing controllers enhances notably the stability of the gait and allows the robot to perform stable motions and to recover from impacts. / Objetivos: Para lograr que los robots humanoides puedan colaborar con los seres humanos y liberarlos de actividades peligrosas o desagradables, hemos de conseguir primero que marchen de forma fiable. En las últimas décadas, el equilibrio de robots humanoides ha atraido una atención creciente de la comunidad científica. Sin embargo, a pesar de que se han logrado importantes resultados en el plano teórico y en simulaciones, el equilibrio de robots humanoides reales continúa siendo objeto de debate. Puesto que estos robots están diseñados para funcionar en ambientes cotidianos, es fundamental que los métodos de control sean validados en plataformas físicas y expuestos a condiciones exigentes. Metodología: En este trabajo proponemos un sistema de control del equilibrio que mejora la robustez de la marcha y la resistencia frente a perturbaciones. Este método combina el criterio de estabilidad del punto de momento cero con el control del momento angular y de la temporización del paso. Resultados: Los experimentos demuestran que el uso combinado de controladores de momento angular y de temporización de los pasos mejoran notablemente la estabilidad de la marcha y permiten al robot ejecutar movimientos estables y recuperarse de los impactos.

In recent years, control system in the automation industries has become more and more useful, covering a wide range of fields, for example, industrial instrumentation, control and monitoring systems. Vision systems are used nowadays to improve products quality control, saving costs, time, and obtaining a better accuracy than a human operator in the manufacturing process of companies. Combining a vision system with a suitable automated system allow companies to cover a wide range of products and rapid production. All these factors are considered in this project. The aim of this project is to upgrade the functionality of a Nokia-Cell, which was used in a quality control process for the back shells of Nokia cell phones. The project includes design, upgrade and implementation of a new system in order to make the cell work properly. The Nokia-Cell is composed of the following basic modules: vision and image recognition system, automation system devices (PC and PLC, robot), and other mechatronics devices. The new system will consist of a new camera, due to the poor connectivity and quality of the old camera. For the same reason, a new PC will replace two older ones for communication and vision recognition. The new system will also include a new PLC of Beckhoff to replace the aging one of Omron so as to facilitate the connections using the same language. In addition, IEC-61499 Function Blocks standard is adopted for programming the Nokia-Cell. It is expected that the results of this project will contribute to both research and education in the future. In addition, it would be correctly to apply the results to industries in vision-based quality control systems. / Programming and Image Processing in a Compact Production Cell