Localização em espaços interiores por luz visível para robôs móveis

Esta dissertação investiga a localização em espaços interiores através da comunicação por luz visível para robôs móveis, com base nos LEDs fixos nos edifícios, dando particular atenção à simulação e desenho do sensor, com vista ao desenvolvimento de um sensor de localização. Explica-se o crescimento da tecnologia LED e da constante necessidade de localização do homem em espaços interiores. Apresentado algumas características do LED e dos foto-detetores existentes. Com uma breve referencia a algumas das comunicações por luz visível de baixo débito possíveis de implementar. O desenvolvimento do protótipo do sensor inicia-se, principalmente, pela simulação de alguns dispositivos essenciais e das suas caraterísticas, como o emissor LED no controlo do ^angulo de meia potência (HPA) e a altura a que se encontra, e no recetor foto-díodo e a sua restrição de campo de visão (FOV). Simula-se o sensor pretendido com o número de foto-díodos necessários otimizando o espaço físico disponível e fazendo não só um refinamento no FOV mas também na distribuição espacial dos foto-díodos com funções predefinidas para a redução de incertezas de decisão de localização do robô. Estes resultados permitiram a construção física do sensor, desde o suporte para os foto-díodos, tendo em conta todas as medidas durante as simulações, e terminando com o desenvolvimento dos sensores e a sua integração completa. O tratamento de dados da leitura dos sinais recebidos do sensor são tratados por um microcontrolador, permitindo calcular parâmetros fundamentais no cálculo da posição. No final, os resultados teóricos bem como os práticos obtidos ao longo do desenvolvimento e possíveis propostas para trabalhos futuros que beneficiam desta investigação

Automatic information and safety systems for driving assistance

O objeto principal desta tese é o estudo de algoritmos de processamento e representação automáticos de dados, em particular de informação obtida por sensores montados a bordo de veículos (2D e 3D), com aplicação em contexto de sistemas de apoio à condução. O trabalho foca alguns dos problemas que, quer os sistemas de condução automática (AD), quer os sistemas avançados de apoio à condução (ADAS), enfrentam hoje em dia. O documento é composto por duas partes. A primeira descreve o projeto, construção e desenvolvimento de três protótipos robóticos, incluindo pormenores associados aos sensores montados a bordo dos robôs, algoritmos e arquitecturas de software. Estes robôs foram utilizados como plataformas de ensaios para testar e validar as técnicas propostas. Para além disso, participaram em várias competições de condução autónoma tendo obtido muito bons resultados. A segunda parte deste documento apresenta vários algoritmos empregues na geração de representações intermédias de dados sensoriais. Estes podem ser utilizados para melhorar técnicas já existentes de reconhecimento de padrões, deteção ou navegação, e por este meio contribuir para futuras aplicações no âmbito dos AD ou ADAS. Dado que os veículos autónomos contêm uma grande quantidade de sensores de diferentes naturezas, representações intermédias são particularmente adequadas, pois podem lidar com problemas relacionados com as diversas naturezas dos dados (2D, 3D, fotométrica, etc.), com o carácter assíncrono dos dados (multiplos sensores a enviar dados a diferentes frequências), ou com o alinhamento dos dados (problemas de calibração, diferentes sensores a disponibilizar diferentes medições para um mesmo objeto). Neste âmbito, são propostas novas técnicas para a computação de uma representação multi-câmara multi-modal de transformação de perspectiva inversa, para a execução de correcção de côr entre imagens de forma a obter mosaicos de qualidade, ou para a geração de uma representação de cena baseada em primitivas poligonais, capaz de lidar com grandes quantidades de dados 3D e 2D, tendo inclusivamente a capacidade de refinar a representação à medida que novos dados sensoriais são recebidos.

Comunicação humano-robô através de linguagem falada

Nos últimos anos, as tecnologias que dão suporte à robótica avançaram expressivamente. É possível encontrar robôs de serviço nos mais variados campos. O próximo passo é o desenvolvimento de robôs inteligentes, com capacidade de comunicação em linguagem falada e de realizar trabalhos úteis em interação/cooperação com humanos. Torna-se necessário, então, encontrar um modo de interagir eficientemente com esses robôs, e com agentes inteligentes de maneira geral, que permita a transmissão de conhecimento em ambos os sentidos. Partiremos da hipótese de que é possível desenvolver um sistema de diálogo baseado em linguagem natural falada que resolva esse problema. Assim, o objetivo principal deste trabalho é a definição, implementação e avaliação de um sistema de diálogo utilizável na interação baseada em linguagem natural falada entre humanos e agentes inteligentes. Ao longo deste texto, mostraremos os principais aspectos da comunicação por linguagem falada, tanto entre os humanos, como também entre humanos e máquinas. Apresentaremos as principais categorias de sistemas de diálogo, com exemplos de alguns sistemas implementados, assim como ferramentas para desenvolvimento e algumas técnicas de avaliação. A seguir, entre outros aspectos, desenvolveremos os seguintes: a evolução levada a efeito na arquitetura computacional do Carl, robô utilizado neste trabalho; o módulo de aquisição e gestão de conhecimento, desenvolvido para dar suporte à interação; e o novo gestor de diálogo, baseado na abordagem de “Estado da Informação”, também concebido e implementado no âmbito desta tese. Por fim, uma avaliação experimental envolvendo a realização de diversas tarefas de interação com vários participantes voluntários demonstrou ser possível interagir com o robô e realizar as tarefas solicitadas. Este trabalho experimental incluiu avaliação parcial de funcionalidades, avaliação global do sistema de diálogo e avaliação de usabilidade.

Navigation in dynamic environments taking advantage of moving agents

Esta tese propõe uma forma diferente de navegação de robôs em ambientes dinâmicos, onde o robô tira partido do movimento de pedestres, com o objetivo de melhorar as suas capacidades de navegação. A ideia principal é que, ao invés de tratar as pessoas como obstáculos dinâmicos que devem ser evitados, elas devem ser tratadas como agentes especiais com conhecimento avançado em navegação em ambientes dinâmicos. Para se beneficiar do movimento de pedestres, este trabalho propõe que um robô os selecione e siga, de modo que possa mover-se por caminhos ótimos, desviar-se de obstáculos não detetados, melhorar a navegação em ambientes densamente populados e aumentar a sua aceitação por outros humanos. Para atingir estes objetivos, novos métodos são desenvolvidos na área da seleção de líderes, onde duas técnicas são exploradas. A primeira usa métodos de previsão de movimento, enquanto a segunda usa técnicas de aprendizagem por máquina, para avaliar a qualidade de candidatos a líder, onde o treino é feito com exemplos reais. Os métodos de seleção de líder são integrados com algoritmos de planeamento de movimento e experiências são realizadas para validar as técnicas propostas.

Grounding human vocabulary in robot perception through interaction

This thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.

Architecture for real-time coordination of multiple autonomous mobile units

Interest on using teams of mobile robots has been growing, due to their potential to cooperate for diverse purposes, such as rescue, de-mining, surveillance or even games such as robotic soccer. These applications require a real-time middleware and wireless communication protocol that can support an efficient and timely fusion of the perception data from different robots as well as the development of coordinated behaviours. Coordinating several autonomous robots towards achieving a common goal is currently a topic of high interest, which can be found in many application domains. Despite these different application domains, the technical problem of building an infrastructure to support the integration of the distributed perception and subsequent coordinated action is similar. This problem becomes tougher with stronger system dynamics, e.g., when the robots move faster or interact with fast objects, leading to tighter real-time constraints. This thesis work addressed computing architectures and wireless communication protocols to support efficient information sharing and coordination strategies taking into account the real-time nature of robot activities. The thesis makes two main claims. Firstly, we claim that despite the use of a wireless communication protocol that includes arbitration mechanisms, the self-organization of the team communications in a dynamic round that also accounts for variable team membership, effectively reduces collisions within the team, independently of its current composition, significantly improving the quality of the communications. We will validate this claim in terms of packet losses and communication latency. We show how such self-organization of the communications can be achieved in an efficient way with the Reconfigurable and Adaptive TDMA protocol. Secondly, we claim that the development of distributed perception, cooperation and coordinated action for teams of mobile robots can be simplified by using a shared memory middleware that replicates in each cooperating robot all necessary remote data, the Real-Time Database (RTDB) middleware. These remote data copies, which are updated in the background by the selforganizing communications protocol, are extended with age information automatically computed by the middleware and are locally accessible through fast primitives. We validate our claim showing a parsimonious use of the communication medium, improved timing information with respect to the shared data and the simplicity of use and effectiveness of the proposed middleware shown in several use cases, reinforced with a reasonable impact in the Middle Size League of RoboCup.

Perception and software architecture for mobile robotics

When developing software for autonomous mobile robots, one has to inevitably tackle some kind of perception. Moreover, when dealing with agents that possess some level of reasoning for executing their actions, there is the need to model the environment and the robot internal state in a way that it represents the scenario in which the robot operates. Inserted in the ATRI group, part of the IEETA research unit at Aveiro University, this work uses two of the projects of the group as test bed, particularly in the scenario of robotic soccer with real robots. With the main objective of developing algorithms for sensor and information fusion that could be used e ectively on these teams, several state of the art approaches were studied, implemented and adapted to each of the robot types. Within the MSL RoboCup team CAMBADA, the main focus was the perception of ball and obstacles, with the creation of models capable of providing extended information so that the reasoning of the robot can be ever more e ective. To achieve it, several methodologies were analyzed, implemented, compared and improved. Concerning the ball, an analysis of ltering methodologies for stabilization of its position and estimation of its velocity was performed. Also, with the goal keeper in mind, work has been done to provide it with information of aerial balls. As for obstacles, a new de nition of the way they are perceived by the vision and the type of information provided was created, as well as a methodology for identifying which of the obstacles are team mates. Also, a tracking algorithm was developed, which ultimately assigned each of the obstacles a unique identi er. Associated with the improvement of the obstacles perception, a new algorithm of estimating reactive obstacle avoidance was created. In the context of the SPL RoboCup team Portuguese Team, besides the inevitable adaptation of many of the algorithms already developed for sensor and information fusion and considering that it was recently created, the objective was to create a sustainable software architecture that could be the base for future modular development. The software architecture created is based on a series of di erent processes and the means of communication among them. All processes were created or adapted for the new architecture and a base set of roles and behaviors was de ned during this work to achieve a base functional framework. In terms of perception, the main focus was to de ne a projection model and camera pose extraction that could provide information in metric coordinates. The second main objective was to adapt the CAMBADA localization algorithm to work on the NAO robots, considering all the limitations it presents when comparing to the MSL team, especially in terms of computational resources. A set of support tools were developed or improved in order to support the test and development in both teams. In general, the work developed during this thesis improved the performance of the teams during play and also the e ectiveness of the developers team when in development and test phases.

Dynamic set pieces for CAMBADA team

Robotics is an emergent branch of engineering that involves the conception, manufacture, and control of robots. It is a multidisciplinary field that combines electronics, design, computer science, artificial intelligence, mechanics and nanotechnology. Its evolution results in machines that are able to perform tasks with some level of complexity. Multi-agent systems is a researching topic within robotics, thus they allow the solving of higher complexity problems, through the execution of simple routines. Robotic soccer allows the study and development of robotics and multiagent systems, as the agents have to work together as a team, having in consideration most problems found in our quotidian, as for example adaptation to a highly dynamic environment as it is the one of a soccer game. CAMBADA is the robotic soccer team belonging to the group of research IRIS from IEETA, composed by teachers, researchers and students of the University of Aveiro, which annually has as main objective the participation in the RoboCup, in the Middle Size League. The purpose of this work is to improve the coordination in set pieces situations. This thesis introduces a new behavior and the adaptation of the already existing ones in the offensive situation, as well as the proposal of a new positioning method in defensive situations. The developed work was incorporated within the competition software of the robots. Which allows the presentation, in this dissertation, of the experimental results obtained, through simulation software as well as through the physical robots on the laboratory.

Goal detection using laser range finder for goalkeeper and striker from CAMBADA team

For a robot be autonomous and mobile, it requires being attached with a set of sensors that helps it to have a better perception of the surrounding world, to manage to localize itself and the surrounding objects. CAMBADA is the robotic soccer team of the IRIS research group, from IEETA, University of Aveiro, that competes in the Middle-Size League of RoboCup. In competition, in order to win, the main objective of the game it's to score more goals than the conceded, so not conceding goals, and score as much as possible it's desirable, thus, this thesis focus on adapt an agent with a better localization capacity in defensive and offensive moments. It was introduced a laser range finder to the CAMBADA robots, making them capable of detecting their own and the opponent goal, and to detect the opponents in specific game situations. With the new information and adapting the Goalie and Penalty behaviors, the CAMBADA goalkeeper is now able to detect and track its own goal and the CAMBADA striker has a better performance in a penalty situation. The developed work was incorporated within the competition software of the robots, which allows the presentation, in this thesis, of the experimental results obtained with physical robots on the laboratory field.