Vision-based mobile robot guidance has proved difficult for classical machine vision methods because of the diversity and real-time constraints inherent in the task. This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task. But real world problems like vision-based mobile robot guidance present a different set of challenges for the connectionist paradigm. Among them are: how to develop a general representation from a limited amount of real training data; how to understand the internal representations developed by artificial neural networks; how to estimate the reliability of individual networks; how to combine multiple networks trained for different situations into a single system; how to combine connectionist perception with symbolic reasoning. Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems. Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour. The techniques also are shown to generalize to the task of controlling the precise foot placement of a walking robot.

The present book includes a set of selected papers from the seventh "International Conference on Informatics in Control Automation and Robotics" (ICINCO 2010), held in Madeira, Portugal, from 15 to 18 June 2010. The conference was organized in three simultaneous tracks: "Intelligent Control Systems and Optimization", "Robotics and Automation" and "Signal Processing, Systems Modeling and Control". The book is based on the same structure. ICINCO received 320 paper submissions, not including those of workshops or special sessions, from 57 countries, in all continents. After a double blind paper review performed by the Program Committee only 27 submissions were accepted as full papers and thus selected for oral presentation, leading to a full paper acceptance ratio of 8%. Additional papers were accepted as short papers and posters. A further refinement was made after the conference, based also on the assessment of presentation quality, so that this book includes the extended and revised versions of the very best papers of ICINCO 2010. Commitment to high quality standards is a major concern of ICINCO that will be maintained in the next editions of this conference, including not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, workshops and logistics.

Here is a comprehensive presentation of methodology for the design and synthesis of an intelligent complex robotic system, connecting formal tools from discrete system theory, artificial intelligence, neural network, and fuzzy logic. The necessary methods for solving real time action planning, coordination and control problems are described. A notable chapter presents a new approach to intelligent robotic agent control acting in a realworld environment based on a lifelong learning approach combining cognitive and reactive capabilities. Another key feature is the homogeneous description of all solutions and methods based on system theory formalism.

This volume describes new frontiers in medical and service robotics in the light of recent developments in technology to advance robot design and implementation. In particular, the work looks at advances in design, development and implementation of contemporary surgical, rehabilitation and biorobots. Surgical robots allow surgeons greater access to areas under operation using more precise and less invasive methods. Rehabilitation robots facilitate and support the lives of the infirm, elderly people, or those with dysfunction of body parts affecting movement. These robots are also used for rehabilitation and related procedures, such as training and therapy. Biorobots are designed to imitate the cognition of humans and animals. The need to substitute humans working on delicate, tiresome and monotonous tasks, or working with potentially health-damaging toxic materials, requires intelligent, high-performance service robots with the ability to cooperate, advanced communication and sophisticated perception and cognitive capabilities. Progress in this field is fast and results need to be disseminated to stimulate both practical applications and further research. Thus, these papers are a valuable addition to existing literature.

The human hand and its dexterity in grasping and manipulating objects are some of the hallmarks of the human species. For years, anatomic and biomechanical studies have deepened the understanding of the human hand's functioning and, in parallel, the robotics community has been working on the design of robotic hands capable of manipulating objects with a performance similar to that of the human hand. However, although many researchers have partially studied various aspects, to date there has been no comprehensive characterization of the human hand's function for grasping and manipulation of everyday life objects. This monograph explores the hypothesis that the confluence of both scientific fields, the biomechanical study of the human hand and the analysis of robotic manipulation of objects, would greatly benefit and advance both disciplines through simulation. Therefore, in this book, the current knowledge of robotics and biomechanics guides the design and implementation of a simulation framework focused on manipulation interactions that allows the study of the grasp through simulation. As a result, a valuable framework for the study of the grasp, with relevant applications in several fields such as robotics, biomechanics, ergonomics, rehabilitation and medicine, has been made available to these communities.

Nowadays, multiple attention have been paid on a robot working in the human living environment, such as in the field of medical, welfare, entertainment and so on. Various types of researches are being conducted actively in a variety of fields such as artificial intelligence, cognitive engineering, sensor- technology, interfaces and motion control. In the future, it is expected to realize super high functional human-like robot by integrating technologies in various fields including these types of researches. The book represents new developments and advances in the field of bio-inspired robotics research introducing the state of the art, the idea of multi-locomotion robotic system to implement the diversity of animal motion. It covers theoretical and computational aspects of Passive Dynamic Autonomous Control (PDAC), robot motion control, multi legged walking and climbing as well as brachiation focusing concrete robot systems, components and applications. In addition, gorilla type robot systems are described as hardware of Multi-Locomotion Robotic system. It is useful for students and researchers in the field of robotics in general, bio-inspired robots, multi-modal locomotion, legged walking, motion control, and humanoid robots. Furthermore, it is also of interest for lecturers and engineers in practice building systems cooperating with humans.

This book presents the concept of cognition in a clear, lucid and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms, and demonstrates their application with real life experiments.

Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes.

This book presents the most recent advances in the research and applications of reconfigurable mechanisms and robots. It collects 93 independently reviewed papers presented at the Third ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots (ReMAR 2015) held in Beijing, China, 20-22 July 2015. The conference papers are organized into seven parts to cover the reconfiguration theory, topology, kinematics and design of reconfigurable mechanisms including reconfigurable parallel mechanisms. The most recent results on reconfigurable robots are presented including their analysis, design, simulation and control. Bio-inspired mechanisms are also explored in the challenging fields of rehabilitation and minimally invasive surgery. This book further addresses deployable mechanisms and origami-inspired mechanisms and showcases a wide range of successful applications of reconfigurable mechanisms and robots. Advances in Reconfigurable Mechanisms and Robots II should be of interest for researchers, engineers and postgraduate students in mechanical engineering, electrical engineering, computer science and mathematics.

This volume surveys three decades of modern robot control theory and describes how the work of Suguru Arimoto shaped its development. Twelve survey articles written by experts associated with Suguru Arimoto at various stages in his career treat the subject comprehensively. This book provides an important reference for graduate students and researchers, as well as for mathematicians, engineers and scientists whose work involves robot control theory.

In 1960, R. E. Kalman published his celebrated paper on recursive min imum variance estimation in dynamical systems [14]. This paper, which introduced an algorithm that has since been known as the discrete Kalman filter, produced a virtual revolution in the field of systems engineering. Today, Kalman filters are used in such diverse areas as navigation, guid ance, oil drilling, water and air quality, and geodetic surveys. In addition, Kalman's work led to a multitude of books and papers on minimum vari ance estimation in dynamical systems, including one by Kalman and Bucy on continuous time systems [15]. Most of this work was done outside of the mathematics and statistics communities and, in the spirit of true academic parochialism, was, with a few notable exceptions, ignored by them. This text is my effort toward closing that chasm. For mathematics students, the Kalman filtering theorem is a beautiful illustration of functional analysis in action; Hilbert spaces being used to solve an extremely important problem in applied mathematics. For statistics students, the Kalman filter is a vivid example of Bayesian statistics in action. The present text grew out of a series of graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst.

This book reviews the fundamentals of screw theory concerned with velocity analysis of rigid-bodies, confirmed with detailed and explicit proofs. The author additionally investigates acceleration, jerk, and hyper-jerk analyses of rigid-bodies following the trend of the velocity analysis. With the material provided in this book, readers can extend the theory of screws into the kinematics of optional order of rigid-bodies. Illustrative examples and exercises to reinforce learning are provided. Of particular note, the kinematics of emblematic parallel manipulators, such as the Delta robot as well as the original Gough and Stewart platforms are revisited applying, in addition to the theory of screws, new methods devoted to simplify the corresponding forward-displacement analysis, a challenging task for most parallel manipulators.

Verification presents the most time-consuming task in the integrated circuit design process. The increasing similarity between implementation verification and the ever-needed task of providing vectors for manufacturing fault testing is tempting many professionals to combine verification and testing efforts.
This book presents the basis for reusing the test vector generation and simulation for the purpose of implementation verification, to result in a significant timesaving. The book brings the results in the direction of merging manufacturing test vector generation and verification. It first discusses error fault models suitable for approaching the verification by testing methods. Then, it elaborates a proposal for an implicit fault model that uses the Arithmetic Transform representation of a circuit and the faults. Presented is the fundamental link between the error size and the test vector size, which allows parametrizable verification by test vectors. Furthermore, the test vector set is sufficient not only for detecting, but also for diagnosing and correcting the design errors.
The practical use of any such simulation-based verification scheme can be seriously impaired by redundant faults, that otherwise require exhaustive simulations. The redundant fault identification methods are presented that are well suited for the type of faults considered. Finally, the same representation can be used to augment and expand the formal verification schemes that are to be used in conjunction with the simulation-based verification.
The primary audience for Verification by Error Modeling includes researchers in verification and testing, managers in charge of verification of test andpracticing engineers. Due to its comprehensive coverage of background topics, the book can also be used for teaching courses on verification and test topics.

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.

] Starting with the research of G. Bogelsack in the 1970s, the analysis of biological locomotion andmanipulation systemsandtheirtechnical realizationhas beenan- portant research eld within the Faculty of Mechanical Engineering at the Ilmenau University of Technology. In 1996, the German Research Foundation (DFG) funded the Innovation College "Motion Systems" at the University of Jena in a coope- tion with engineers at the Ilmenau University of Technology. Thus, research was able to be intensi ed and extended. Of course, the whole spectrum of biologically inspired systems is much too wide, so the analysis was still focused on locomotion and manipulation systems. At this stage J. Steigenberger from the Faculty of Mathematics and Natural S- ences at the Ilmenau University of Technology contributed important studies of worm-like locomotion systems with much dedication and technical competence. Moreover, he conceived and carried out a lecture series entitled "Mathematical Basics for Locomotion Systems," which was based on his evaluation of national and international research developments in this eld. I. Zeidis and K. Zimmermann contributed many publications on the mechanics of worm-like locomotion systems based on continuum and rigid-body models as well as asymptotic methods. Since 2004 the German Research Foundation has supported a series of projects led by K. Zimmermann dedicated to biologically inspired robotics. In addition to these activities, the Department of Technical Mechanics and the Department of Computer Application in Mechanical Engineering (M. Weiss) together with masters and doctoral students started the development of mobile robots for the RoboCup Small-Size League in 1998."

This authoritative reference work will provide readers with a complete overview of artificial intelligence (AI), including its historic development and current status, existing and projected AI applications, and present and potential future impact on the United States and the world. Some people believe that artificial intelligence (AI) will revolutionize modern life in ways that improve human existence. Others say that the promise of AI is overblown. Still others contend that AI applications could pose a grave threat to the economic security of millions of people by taking their jobs and otherwise rendering them "obsolete"-or, even worse, that AI could actually spell the end of the human race. This volume will help users understand the reasons AI development has both spirited defenders and alarmed critics; explain theories and innovations like Moore's Law, mindcloning, and Technological Singularity that drive AI research and debate; and give readers the information they need to make their own informed judgment about the promise and peril of this technology. All of this coverage is presented using language and terminology accessible to a lay audience. Introduction explaining the historical evolution of AI Chronology of important AI-related events Authoritative entries on leading pioneers, entrepreneurs, and thinkers; AI concepts and theories; AI's potential impact on different facets of society; and major movies and other cultural touchstones exploring AI technology

While sailing has a long tradition, both as a means of transportation and as a sport, robotic sailing is a fairly new area of research. One of its unique characteristics is the use of wind for propulsion. On the one hand, this allows for long range and long term autonomy. On the other hand, the dependency on changing winds presents a serious challenge for short and long term planning, collision avoidance, and boat control. Moreover, building a robust and seaworthy sailing robot is no simple task, leading to a truly interdisciplinary engineering problem. These proceedings summarize the state of the art as presented at the International Robotic Sailing Conference 2011. Following an overview of the history of autonomous sailing a number of recent boat designs is presented, ranging from small one-design boats to vessels built to cross the Atlantic Ocean. Subsequently, various aspects of system design and validation are discussed, further highlighting the interdisciplinary nature of the field. Finally, methods for collision avoidance, localization and route planning are covered.

Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain provides imperative research on the development of data fusion and analytics for healthcare and their implementation into current issues in a real-time environment. While highlighting IoT, bio-inspired computing, big data, and evolutionary programming, the book explores various concepts and theories of data fusion, IoT, and Big Data Analytics. It also investigates the challenges and methodologies required to integrate data from multiple heterogeneous sources, analytical platforms in healthcare sectors. This book is unique in the way that it provides useful insights into the implementation of a smart and intelligent healthcare system in a post-Covid-19 world using enabling technologies like Artificial Intelligence, Internet of Things, and blockchain in providing transparent, faster, secure and privacy preserved healthcare ecosystem for the masses.

Surveillance systems have become increasingly popular. Full involvement of human operators has led to shortcomings, e.g. high labor cost, limited capability for multiple screens, inconsistency in long-duration, etc. Intelligent surveillance systems (ISSs) can supplement or even replace traditional ones. In ISSs, computer vision, pattern recognition, and artificial intelligence technologies are used to identify abnormal behaviours in videos. They present the development of real-time behaviour-based intelligent surveillance systems. The book focuses on the detection of individual abnormal behaviour based on learning and the analysis of dangerous crowd behaviour based on texture and optical flow. Practical systems include a real-time face classification and counting system, a surveillance robot system that utilizes video and audio information for intelligent interaction, and a robust person counting system for crowded environments.

Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

This book aims at reporting some of the most challenging open problems of control theoretic nature raised by robotics applications. Topics covered in the book represent many of the most innovative areas in contemporary robotics research, with special emphasis on vision, sensory-feedback control, human-centered robotics, manipulation, planning, flexible and cooperative robots, or assembly systems. The basic idea behind the book is to present the variety of innovative applications and related technology demands that arise from robotics and automation to a larger community, including in particular, researchers in automatic control, applied mathematics, mechanical engineering, or computer science. The book is intended for an audience of researchers and graduate students in those disciplines and in robotics. It is the outcome of a workshop held in Las Vegas, Nevada on December 14, 2002 jointly sponsored by the IEEE Control Systems Society and the IEEE Robotics and Automation Society.

In the last decade, we have seen an extraordinary progress in the the ory and applications of robot kinematics. This has been motivated espe cially by the development of complex parallel and humanoid robots. The present book reports the most recent research advances in the theory, design, control and application of robotic systems, which are intended for a variety of purposes such as manipulation, manufacturing, automa tion, surgery, locomotion and biomechanics. The issues addressed are fundamentally kinematic in nature, including synthesis, calibration, re dundancy, force control, dexterity, inverse and forward kinematics, kine matic singularities, as well as over-constrained systems. Methods used include line geometry, quaternion algebra, screw algebra, and linear alge bra. These methods are applied to both parallel and serial multi-degree of-freedom systems. The results should interest researchers, teachers and students, in fields of engineering and mathematics related to robot theory, design, control and application. This is the sixth book of the series Advances in Robot Kinematics published by Kluwer. The contributions in this book had been rigorously reviewed by in dependent reviewers and fifty one articles had been recommended for publication. They were introduced in seven chapters. These articles were also reported and discussed at the ninth international symposium on Advances in Robot Kinematics which was held in June 2004 in Sestri Levante in Italy. Indexed in Conference Proceedings Citation Index- Science (CPCI-S)