GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

An Evaluation Schema for the Ethical Use of Autonomous Robotic Systems in Security Applications

We propose a multi-step evaluation schema designed to help procurement agencies and others to examine the ethical dimensions of autonomous systems to be applied in the security sector, including autonomous weapons systems

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

WEHST: Wearable Engine for Human-Mediated Telepresence

This dissertation reports on the industrial design of a wearable computational device created to enable better emergency medical intervention for situations where electronic remote assistance is necessary. The design created for this doctoral project, which assists practices by paramedics with mandates for search-and-rescue (SAR) in hazardous environments, contributes to the field of human-mediated teleparamedicine (HMTPM). Ethnographic and industrial design aspects of this research considered the intricate relationships at play in search-and-rescue operations, which lead to the design of the system created for this project known as WEHST: Wearable Engine for Human-Mediated Telepresence. Three case studies of different teams were carried out, each focusing on making improvements to the practices of teams of paramedics and search-and-rescue technicians who use combinations of ambulance, airplane, and helicopter transport in specific chemical, biological, radioactive, nuclear and explosive (CBRNE) scenarios. The three paramedicine groups included are the Canadian Air Force 442 Rescue Squadron, Nelson Search and Rescue, and the British Columbia Ambulance Service Infant Transport Team. Data was gathered over a seven-year period through a variety of methods including observation, interviews, examination of documents, and industrial design. The data collected included physiological, social, technical, and ecological information about the rescuers. Actor-network theory guided the research design, data analysis, and design synthesis. All of this leads to the creation of the WEHST system. As identified, the WEHST design created in this dissertation project addresses the difficulty case-study participants found in using their radios in hazardous settings. As the research identified, a means of controlling these radios without depending on hands, voice, or speech would greatly improve communication, as would wearing sensors and other computing resources better linking operators, radios, and environments. WEHST responds to this need. WEHST is an instance of industrial design for a wearable “engine” for human-situated telepresence that includes eight interoperable families of wearable electronic modules and accompanying textiles. These make up a platform technology for modular, scalable and adaptable toolsets for field practice, pedagogy, or research. This document details the considerations that went into the creation of the WEHST design

The Nexus between Artificial Intelligence and Economics

This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers artificial happiness and the likelihood that artificial intelligence might increase human happiness. Section 5 discusses some prominent related concepts and issues. Section 6 describes the use of artificial agents in economic modeling, and section 7 considers some ways in which economic analysis can offer some hints about what the advent of artificial intelligence might bring. Chapter 8 presents some thoughts about the current state of AI and its future prospects.

Hybrid intelligent control for smart grid functionalities integration

Orientador: Alexandre Rasi AokiCoorientador: Germano Lambert-TorresTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 31/07/2020Inclui referências: p. 108-117Resumo: Ao longo dos anos, as redes de distribuição de energia estão ficando mais inteligentes e automatizadas, consequentemente problemas complexos emergem, onde estes são os gatilhos para melhorar antigos estudos e iniciar novas linhas de pesquisa. A Rede Elétrica Inteligente é o conceito abrangente para entender os novos problemas e alterar o comportamento tradicional do sistema para uma nova abordagem, partindo para uma rede com mais intercomunicação entre os elementos ativos. Para contribuir com avanços, a ideia principal desta tese é iniciar uma nova linha de pesquisa para combinar diferentes funcionalidades do Sistema Avançado de Gerenciamento da Distribuição (ADMS), a serem resolvidas por apenas um algoritmo ao mesmo tempo. Para iniciar os estudos dessa linha de desenvolvimento, foram selecionados os problemas mais comuns que causam grande impacto nas redes de distribuição, as interrupções inesperadas e as sobrecargas, resolvidas pelos algoritmos de Auto-Recuperação e Descarte de Carga, respectivamente. Os estudos atuais concentram-se em resolver o problema de Auto-Recuperação primeiro e depois, se o sistema iniciar ou manter uma sobrecarga, executar o descarte de carga para reduzir a carga e manter o sistema no modo operacional. No entanto, em vez de ter as duas funcionalidades trabalhando em um modo sequencial, por que não desenvolver um algoritmo exclusivo para processar o problema e resolvê-lo ao mesmo tempo, de forma simultânea? Assim, esta tese traz exatamente esse novo tipo de abordagem por meio da metodologia de Aprendizado por Reforço (um algoritmo de Machine Learning para tomar decisões) através do algoritmo Q-Learning. Em que os elementos do Q-Learning foram adaptado para reproduzir o ambiente como a rede de distribuição, a recompensa como a maximização da carga e as ações como a troca de posição das chaves (Auto- Recuperação) e a porcentagem de reduções de carga (Descarte da carga), a interagir no sistema para determinar o próximo estado (topologia). Para provar o algoritmo desenvolvido, foi utilizado um sistema urbano real com cinco alimentadores interconectados, onde o sistema foi dividido em um caso de três alimentadores, para determinar a escolha da política (a ?-greed foi a selecionada), criar alguns casos básicos e ser comparada com outras abordagens sequenciais. O caso completo foi usado para sobrecarregar o sistema e analisar os resultados para casos complexos. Em todas as simulações, os resultados encontraram uma boa solução após o estado de isolamento para maximizar a restauração da carga, e em alguns casos em que o sistema foi acionado por uma sobrecarga, o algoritmo pode, no mesmo momento, reconfigurar o sistema para evitar a sobrecarga e aplicar a redução de carga. Portanto, este trabalho forneceu uma nova linha de estudo e contribuir com uma nova linha de pesquisas a ser aprofundado em trabalhos futuros. Palavras-chave: ADMS. Auto-Recuperação. Descarte de Carga. Aprendizado por Reforço. Q-Learning. Rede de Distribuição. Abordagem Simultânea.Abstract: Along the year, the distribution networks are getting more intelligent and automated, consequently complex problems emerge, where these are the triggers to improve old studies or start new lines of researches. The Smart Grid is the broad concept to understand the new problems and change the traditional system behavior for a new approach, where more intelligence and intercommunication is improved to solve the several distribution problems. To contribute on the network enhancements, the main idea of this thesis is to start a new line of research to combine different Advanced Distribution Management System (ADMS) functionalities to be solved by only one algorithm at the same time. To start the studies on this line of strategy, it was selected the most usual problems that has a big impact in distribution networks, the unexpected outages and the overloads, which are solved by Self-Healing and Load Shedding algorithms respectively. The current studies focus to solve the Self-Healing problem first and after, if the system initiate or maintain an overload, executes the Load Shedding to reduce the load and keeps the system in an operative mode. However, instead of having both functionalities working in a sequential mode, why not developed a unique algorithm to process both problem and solve them at the same time? Thus, this thesis brings exactly this new type of approach through the Reinforcement Learning methodology (a Machine Learning algorithm to take decisions) using the Q-Learning algorithm. The Q-Learning elements were adapted to reproduces environment as the distribution network, the reward as the maximization of load and the actions as the switch commutation (Self-Healing) and percentual of load reductions (Load Shedding) to be selected and interact on the system to determine the next state (topology). To prove the algorithm developed, it was used a real urban system with five interconnected feeders, where the system was divided in a three-feeder case, to determine the policy choice (?-greed was selected), create some basic cases and be compared with other Self-Healing + Load Shedding sequential approaches. The complete case was used to overload the system and analyze the results for complex cases. In all simulations the results could find a good solution after the isolation state to maximizes the load restoration, and some cases where the system was trigger by an overload the algorithm could at the same moment reconfigure the system to avoid the overload and apply the load curtailment. Thus, this work provided a new line of study and contribute for new researches on this area to go deeper and improve ADMS algorithms. Keyworlds: ADMS. Self-Healing. Load Shedding. Reinforcement Learning. Q-Learning. Distribution Network. Simultaneous Approac