Innovative examination of augmentation technologies in terms of technical, social, and ethical considerations Usable as a supplemental text for a variety of courses, and also of interest to researchers and professionals in fields including: technical communication, digital communication, UX design, information technology, informatics, human factors, artificial intelligence, ethics, philosophy of technology, and sociology of technology First major work to combine technological, ethical, social, and rhetorical perspectives on human augmentation Additional cases and research material available at the authors' Fabric of Digital Life research database at https://fabricofdigitallife.com/

This volume Future Control and Automation- Volume 2 includes best papers from 2012 2nd International Conference on Future Control and Automation (ICFCA 2012) held on July 1-2, 2012, Changsha, China. Future control and automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. This volume can be divided into six sessions on the basis of the classification of manuscripts considered, which is listed as follows: Mathematical Modeling, Analysis and Computation, Control Engineering, Reliable Networks Design, Vehicular Communications and Networking, Automation and Mechatronics.

Revised and updated, the second edition includes several new chapters with projects and applications. The authors keep pace with the ever-growing and rapidly expanding field of robotics. The new edition reflects technological developments and includes programs and activities for robot enthusiasts. Using photographs, illustrations, and informative text, Mobile Robots guides the reader through the step-by-step process of constructing two different and inexpensive yet fully functional robots.

This text discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the health care system and compare the results to better understand the nature and progress of the disease in the human body that leads to early diagnosis and better cure of disease and treatment with the help of distributed technology. Covers the implementation models using technologies such as artificial intelligence, machine learning, deep learning with distributed systems for better diagnosis and treatment of diseases. Gives in-depth review of the technological advancements like advanced sensing technologies like Plasmonic sensors, usage of RFIDs and electronic diagnostic tools in the field of healthcare engineering Discusses possibilities of augmented reality and virtual reality interventions for providing unique solutions in medical science, clinical research, psychology, and neurological disorders Highlights the future challenges and risks involved in the application of smart technologies like Cloud computing, fog computing, IOT and distributed computing in heathcare. Confers to utilize the AI and ML and associated aids in healthcare sectors in the post Covid 19 to revitalize the medical set up Contributions included in the book will motivate the technological developers and researchers to develop new algorithms and protocols in healthcare field. It will serve as the vast place for knowledge regarding healthcare health care delivery, health care management, health care in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical.

Decentralized Estimation and Control for
Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia.
Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted.
Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources.
Decentralized Estimation and Control for
Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation.
The text discusses:
Generalizing the linear Information filter to the problem of estimation for nonlinear systems
Developing a decentralized form of the algorithm
Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states
Reducing computational requirements by using smaller local model sizes
Defining internodal communication
Developing estimation algorithms for different models
Applying the decentralized algorithms to the problem of decentralized control
Demonstrating the theory to a modular wheeled mobile robot, a vehicle system with nonlinear kinematics and distributed means of acquiring information
Extending the applications to other robotic systems and large scale systems
Decentralized Estimation and Control for
Multisensor Systems addresses how decentralized estimation and control systems are rapidly becoming indispensable tools in a diverse range of applications - such as process control systems, aerospace, and mobile robotics - providing a self-contained, dynamic resource concerning electrical and mechanical engineering.

Indoor Navigation Strategies for Aerial Autonomous Systems presents the necessary and sufficient theoretical basis for those interested in working in unmanned aerial vehicles, providing three different approaches to mathematically represent the dynamics of an aerial vehicle. The book contains detailed information on fusion inertial measurements for orientation stabilization and its validation in flight tests, also proposing substantial theoretical and practical validation for improving the dropped or noised signals. In addition, the book contains different strategies to control and navigate aerial systems. The comprehensive information will be of interest to both researchers and practitioners working in automatic control, mechatronics, robotics, and UAVs, helping them improve research and motivating them to build a test-bed for future projects.

Human Modelling for Bio-inspired Robotics: Mechanical Engineering in Assistive Technologies presents the most cutting-edge research outcomes in the area of mechanical and control aspects of human functions for macro-scale (human size) applications. Intended to provide researchers both in academia and industry with key content on which to base their developments, this book is organized and written by senior experts in their fields. Human Modeling for Bio-Inspired Robotics: Mechanical Engineering in Assistive Technologies offers a system-level investigation into human mechanisms that inspire the development of assistive technologies and humanoid robotics, including topics in modelling of anatomical, musculoskeletal, neural and cognitive systems, as well as motor skills, adaptation and integration. Each chapter is written by a subject expert and discusses its background, research challenges, key outcomes, application, and future trends. This book will be especially useful for academic and industry researchers in this exciting field, as well as graduate-level students to bring them up to speed with the latest technology in mechanical design and control aspects of the area. Previous knowledge of the fundamentals of kinematics, dynamics, control, and signal processing is assumed.

The book explores the concepts and challenges in developing novel approaches using the Internet of Things, intelligent systems, machine intelligence systems, and data analytics in various industrial sectors such as manufacturing, smart agriculture, smart cities, food processing, environment, defense, stock market and healthcare. Further, it discusses the latest improvements in the industrial sectors using machine intelligence learning and intelligent systems techniques, especially robotics. Features: * Highlights case studies and solutions to industrial problems using machine learning and intelligent systems. * Covers applications in smart agriculture, smart healthcare, intelligent machines for disaster management, and smart manufacturing. * Provides the latest methodologies using machine intelligence systems in the early forecasting of weather. * Examines the research challenges and identifies the gaps in data collection and data analysis, especially imagery, signal, and speech. * Provides applications of digitization and smart processing using the Internet of Things and effective intelligent agent systems in manufacturing. * Discusses a systematic and exhaustive analysis of intelligent software effort estimation models. It will serve as an ideal reference text for graduate students, post-graduate students, IT Professionals, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

On the one side, Industrial competitiveness today means shorter product lifecycles, increased product variety, and shorter times to market and customized tangible products and services. To face these challenges, the manufacturing industry is forced to move from traditional management, control, and automation approaches towards industrial cyber-physical systems. On the other side, several emergent engineering approaches and related Information-Communication-Control-Technologies, such as Multi-Agent-Systems, Service-Oriented Architecture, Plug-and-Produce Systems, Cloud and Fog Technologies, Big Data and Analytics, among others, have been researched during the last years. The confluence of those results with the latest developments in Industrial Digitalization, Systems-of-Cyber-Physical-Systems Engineering, Internet-of-Things, Internet-of-Services, and Industry 4.0 is opening a new broad spectrum of innovation possibilities. The PERFoRM (Production-harmonizEd-Reconfiguration of Flexible Robots and Machinery) approach is one of them. It teaches the reader what it means when production machines and systems are digitalized and migrated into Industrial Cyber-Physical Systems and what happens when they are networked and start collaborating with each other and with the human, using the internet. After a Technology Trend Screening and beyond a comprehensive state-of-the-art analysis about Industrial Digitalization and Industry 4.0-compliant solutions, the book introduces methods, architectures, and technologies applicable in real industrial use cases, explained for a broad audience of researchers, practitioners, and industrialists.

Mobile Robotics presents the different tools and methods that enable the design of mobile robots; a discipline booming with the emergence of flying drones, underwater robots mine detectors, sailboats robots and robot vacuum cleaners. Illustrated with simulations, exercises and examples, this book describes the fundamentals of modeling robots, developing the actuator concepts, sensor, control and guidance. Three-dimensional simulation tools are also explored, as well as the theoretical basis for reliable localization of robots within their environment.

This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible. It is aimed at people who want to get their teeth into self-driving vehicle technology, by providing genuine technical insights where other books just skim the surface. The book tackles everything from sensors and perception to functional safety and cybersecurity. It also passes on some practical know-how and discusses concrete SDV applications, along with a discussion of where this technology is heading. It will serve as a good starting point for software developers or professional engineers who are eager to pursue a career in this exciting field and want to learn more about the basics of SDV algorithms. Likewise, academic researchers, technology enthusiasts, and journalists will also find the book useful. Key Features: Offers a comprehensive technological walk-through of what really matters in SDV development: from hardware, software, to functional safety and cybersecurity Written by an active practitioner with extensive experience in series development and research in the fields of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Covers theoretical fundamentals of state-of-the-art SLAM, multi-sensor data fusion, and other SDV algorithms. Includes practical information and hands-on material with Robot Operating System (ROS) and Open Source Car Control (OSCC). Provides an overview of the strategies, trends, and applications which companies are pursuing in this field at present as well as other technical insights from the industry.

This book attempts to treat line design and its related subjects in a cohesive manner, with an emphasis on design applications. It discusses general guidelines for setting up assumptions and determining line performance parameters, based on empirical data from literature reports.

A reference guide for professionals or text for graduate and postgraduate students, this volume emphasizes practical designs and applications of distributed computer control systems. It demonstrates how to improve plant productivity, enhance product quality, and increase the safety, reliability, and

Robots, autonomous vehicles, unmanned aerial vehicles, and smart factory, will significantly change human living style in digital society. Artificial Intelligence in Wireless Robotics introduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems. The technical contents of this book include fundamental knowledge in robotics, cyber-physical systems, artificial intelligence, statistical decision and Markov decision process, reinforcement learning, state estimation, localization, computer vision and multi-modal data fusion, robot planning, multi-agent systems, networked multi-agent systems, security and robustness of networked robots, and ultra-reliable and low-latency machine-to-machine networking. Examples and exercises are provided for easy and effective comprehension. Engineers wishing to extend knowledge in the robotics, AI, and wireless communications, would be benefited from this book. In the meantime, the book is ready as a textbook for senior undergraduate students or first-year graduate students in electrical engineering, computer engineering, computer science, and general engineering students. The readers of this book shall have basic knowledge in undergraduate probability and linear algebra, and basic programming capability, in order to enjoy deep reading.

This book focuses on fish lateral line inspired sensing, which has attracted increasing attention in recent years. The applications of fish lateral line inspired sensing technology on underwater robots were summarized for the first time in this book.

The study of technology and its implications in the medical field has become an increasingly crucial area of research. By integrating technological innovations into clinical practices, patients can receive improved diagnoses and treatments, as well as faster and safer recoveries. Virtual Reality Enhanced Robotic Systems for Disability Rehabilitation is an authoritative reference source for the latest scholarly research on the use of computer-assisted rehabilitation methods for disabled patients. Highlighting the application of robots, sensors, and virtual environments, this book is ideally designed for graduate students, engineers, technicians, and company administrators interested in the incorporation of auto-training methods in patient recovery.

This book consolidates the current state of knowledge on implementing cooperating robot-based systems to increase the flexibility of manufacturing systems. It is based on the concrete experiences of experts, practitioners, and engineers in implementing cooperating robot systems for more flexible manufacturing systems. Thanks to the great variety of manufacturing systems that we had the opportunity to study, a remarkable collection of methods and tools has emerged. The aim of the book is to share this experience with academia and industry practitioners seeking to improve manufacturing practice. While there are various books on teaching principles for robotics, this book offers a unique opportunity to dive into the practical aspects of implementing complex real-world robotic applications. As it is used in this book, the term "cooperating robots" refers to robots that either cooperate with one another or with people. The book investigates various aspects of cooperation in the context of implementing flexible manufacturing systems. Accordingly, manufacturing systems are the main focus in the discussion on implementing such robotic systems. The book begins with a brief introduction to the concept of manufacturing systems, followed by a discussion of flexibility. Aspects of designing such systems, e.g. material flow, logistics, processing times, shop floor footprint, and design of flexible handling systems, are subsequently covered. In closing, the book addresses key issues in operating such systems, which concern e.g. decision-making, autonomy, cooperation, communication, task scheduling, motion generation, and distribution of control between different devices. Reviewing the state of the art and presenting the latest innovations, the book offers a valuable asset for a broad readership.

This second volume is a continuation of the successful first volume of this Springer book, and as well as addressing broader topics it puts a particular focus on unmanned aerial vehicles (UAVs) with Robot Operating System (ROS). Consisting of three types of chapters: tutorials, cases studies, and research papers, it provides comprehensive additional material on ROS and the aspects of developing robotics systems, algorithms, frameworks, and applications with ROS. ROS is being increasingly integrated in almost all kinds of robots and is becoming the de-facto standard for developing applications and systems for robotics. Although the research community is actively developing applications with ROS and extending its features, amount of literature references is not representative of the huge amount of work being done. The book includes 19 chapters organized into six parts: Part 1 presents the control of UAVs with ROS, while in Part 2, three chapters deal with control of mobile robots. Part 3 provides recent work toward integrating ROS with Internet, cloud and distributed systems. Part 4 offers five case studies of service robots and field experiments. Part 5 presents signal-processing tools for perception and sensing, and lastly, Part 6 introduces advanced simulation frameworks. The diversity of topics in the book makes it a unique and valuable reference resource for ROS users, researchers, learners and developers.

This book is of interest to researchers wanting to know more about the latest topics and methods in the fields of the kinematics, control and design of robotic systems. The papers cover the full range of robotic systems, including serial, parallel and cable-driven manipulators. The systems range from being less than fully mobile, to kinematically redundant, to over-constrained. The book brings together 43 peer-reviewed papers. They report on the latest scientific and applied achievements. The main theme that connects them is the movement of robots in the most diverse areas of application.

This book gathers papers presented at the International Conference "Educational Robotics in the Maker Era - EDUROBOTICS 2018", held in Rome, Italy, on October 11, 2018. The respective chapters explore the connection between the Maker Movement on the one hand, and Educational Robotics, which mainly revolves around the constructivist and constructionist pedagogy, on the other. They cover a broad range of topics relevant for teacher education and for designing activities for children and youth, with an emphasis on using modern low-cost technologies (including block-based programming environments, Do-It-Yourself electronics, 3D printed artifacts, intelligent distributed systems, IoT technology and gamification) in formal and informal education settings. The twenty contributions collected here will introduce researchers and practitioners to the latest advances in educational robotics, with a focus on science, technology, engineering, arts and mathematics (STEAM) education. Teachers and educators at all levels will find valuable insights and inspirations into how educational robotics can promote technological interest and 21st century skills - e.g. creativity, critical thinking, teamwork, and problem-solving - with a special emphasis on new making technologies.

This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.