Autonomous robot systems and competitions: proceedings of the 12th International Conference

This is the 2012’s edition of the scientific meeting of the Portuguese Robotics Open (ROBOTICA’ 2012). It aims to disseminate scientific contributions and to promote discussion of theories, methods and experiences in areas of relevance to Autonomous Robotics and Robotic Competitions. All accepted contributions are included in this proceedings book. The conference program has also included an invited talk by Dr.ir. Raymond H. Cuijpers, from the Department of Human Technology Interaction of Eindhoven University of Technology, Netherlands.The conference is kindly sponsored by the IEEE Portugal Section / IEEE RAS ChapterSPR-Sociedade Portuguesa de Robótic

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

An effective scene recognition strategy for biomimetic robotic navigation

Master'sMASTER OF ENGINEERIN

Systematic design of biologically-inspired engineering solutions

Biological organisms, phenomena and strategies, herein referred to as biological systems, provide a rich set of analogies that can be used to inspire engineering innovation. Biologically-inspired, or biomimetic, designs are publicly viewed as creative and novel solutions to human problems. Moreover, some biomimetic designs have become so commonplace that it is hard to image life without them (e.g. velcro, airplanes). Although the biologically- inspired solutions are innovative and useful, the majority of inspiration taken from nature has happened by chance observation, dedicated study of a specific biological entity (e.g., gecko), or asking a biologist to explain the biology in simple terms. This reveals a fundamental problem of working across the engineering and biological domains. The effort and time required to become a competent engineering designer creates significant obstacles to becoming sufficiently knowledgeable about biological systems (the converse can also be said). This research aims to remove the element of chance, reduce the amount of time and effort required to developing biologically-inspired solutions, and bridge the seemingly immense disconnect between the engineering and biological domains. To facilitate systematic biologically-inspired design, a design methodology that relies on a framework of tools and techniques that bridge the two domains is established. The design tools and techniques that comprise the framework achieve: Identification of relevant biological solutions based on function; translation of identified biological systems of interest; functional representation of biological information such that it can be used for engineering design activities; and conceptualization of biomimetic engineering designs. Using functional representation and abstraction to describe biological systems presents the natural designs in an engineering context and allows designers to make connections between biological and engineered systems. Thus, the biological information is accessible to engineering designers with varying biological knowledge, but a common understanding of engineering design methodologies. This work has demonstrated the feasibility of using systematic design for the discovery of innovative engineering designs without requiring expert-level knowledge, but rather broad knowledge of many fields

Modelling active bio-inspired object recognition in autonomous mobile agents

Object recognition is arguably one of the main tasks carried out by the visual cortex. This task has been studied for decades and is one of the main topics being investigated in the computer vision field. While vertebrates perform this task with exceptional reliability and in very short amounts of time, the visual processes involved are still not completely understood. Considering the desirable properties of the visual systems in nature, many models have been proposed to not only match their performance in object recognition tasks, but also to study and understand the object recognition processes in the brain. One important point most of the classical models have failed to consider when modelling object recognition is the fact that all the visual systems in nature are active. Active object recognition opens different perspectives in contrast with the classical isolated way of modelling neural processes such as the exploitation of the body to aid the perceptual processes. Biologically inspired models are a good alternative to study embodied object recognition since animals are a working example that demonstrates that object recognition can be performed with great efficiency in an active manner. In this thesis I study biologically inspired models for object recognition from an active perspective. I demonstrate that by considering the problem of object recognition from this perspective, the computational complexity present in some of the classical models of object recognition can be reduced. In particular, chapter 3 compares a simple V1-like model (RBF model) with a complex hierarchical model (HMAX model) under certain conditions which make the RBF model perform as the HMAX model when using a simple attentional mechanism. Additionally, I compare the RBF and HMAX model with some other visual systems using well-known object libraries. This comparison demonstrates that the performance of the implementations of the RBF and HMAX models employed in this thesis is similar to the performance of other state-of-the-art visual systems. In chapter 4, I study the role of sensors in the neural dynamics of controllers and the behaviour of simulated agents. I also show how to employ an Evolutionary Robotics approach to study autonomous mobile agents performing visually guided tasks. In addition, in chapter 5 I investigate whether the variation in the visual information, which is determined by simple movements of an agent, can impact the performance of the RBF and HMAX models. In chapter 6 I investigate the impact of several movement strategies in the recognition performance of the models. In particular I study the impact of the variation in visual information using different movement strategies to collect training views. In addition, I show that temporal information can be exploited to improve the object recognition performance using movement strategies. In chapter 7 experiments to study the exploitation of movement and temporal information are carried out in a real world scenario using a robot. These experiments validate the results obtained in simulations in the previous chapters. Finally, in chapter 8 I show that by exploiting regularities in the visual input imposed by movement in the selection of training views, the complexity of the RBF model can be reduced in a real robot. The approach of this work proposes to gradually increase the complexity of the processes involved in active object recognition, from studying the role of moving the focus of attention while comparing object recognition models in static tasks, to analysing the exploitation of an active approach in the selection of training views for a object recognition task in a real world robot

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described