Visible light communication systems for road safety applications

Esta tese apresenta um estudo exploratório sobre sistemas de comunicação por luz visível e as suas aplicações em sistemas de transporte inteligentes como forma a melhorar a segurança nas estradas. Foram desenvolvidos neste trabalho, modelos conceptuais e analíticos adequados à caracterização deste tipo de sistemas. Foi desenvolvido um protótipo de baixo custo, capaz de suportar a disseminação de informação utilizando semáforos. A sua realização carece de um estudo detalhado, nomeadamente: i) foi necessário obter modelos capazes de descrever os padrões de radiação numa área de serviço pré-definida; ii) foi necessário caracterizar o meio de comunicações; iii) foi necessário estudar o comportamento de vários esquemas de modulação de forma a optar pelo mais robusto; finalmente, iv) obter a implementação do sistema baseado em FPGA e componentes discretos. O protótipo implementado foi testado em condições reais. Os resultados alcançados mostram os méritos desta solução, chegando mesmo a encorajar a utilização desta tecnologia em outros cenários de aplicação.

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.