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.

With the increasing applications of intelligent robotic systems in various ?elds, the - sign and control of these systems have increasingly attracted interest from researchers. This edited book entitled “Design and Control of Intelligent Robotic Systems” in the book series of “Studies in Computational Intelligence” is a collection of some advanced research on design and control of intelligent robots. The works presented range in scope from design methodologies to robot development. Various design approaches and al- rithms, such as evolutionary computation, neural networks, fuzzy logic, learning, etc. are included. We also would like to mention that most studies reported in this book have been implemented in physical systems. An overview on the applications of computational intelligence in bio-inspired robotics is given in Chapter 1 by M. Begum and F. Karray, with highlights of the recent progress in bio-inspired robotics research and a focus on the usage of computational intelligence tools to design human-like cognitive abilities in the robotic systems. In Chapter 2, Lisa L. Grant and Ganesh K. Venayagamoorthy present greedy search, particle swarm optimization and fuzzy logic based strategies for navigating a swarm of robots for target search in a hazardous environment, with potential applications in high-risk tasks such as disaster recovery and hazardous material detection.

Since the late 1960s, there has been a revolution in robots and industrial automation, from the design of robots with no computing or sensorycapabilities (first-generation), to the design of robots with limited computational power and feedback capabilities (second-generation), and the design of intelligent robots (third-generation), which possess diverse sensing and decision making capabilities. The development of the theory of intelligent machines has been developed in parallel to the advances in robot design. This theory is the natural outcome of research and development in classical control (1950s), adaptive and learning control (1960s), self-organizing control (1970s) and intelligent control systems (1980s). The theory of intelligent machines involves utilization and integration of concepts and ideas from the diverse disciplines of science, engineering and mathematics, and fields like artificial intelligence, system theory and operations research. The main focus and motivation is to bridge the gap between diverse disciplines involved and bring under a common cover several generic methodologies pertaining to what has been defined as machine intelligence. Intelligent robotic systems are a specific application of intelligent machines. They are complex computer controlled robotic systems equipped with a diverse set of visual and non visual sensors and possess decision making and problem solving capabilities within their domain of operation. Their modeling and control is accomplished via analytical and heuristic methodologies and techniques pertaining to generalized system theory and artificial intelligence. Intelligent Robotic Systems: Theory, Design and Applications, presents and justifies the fundamental concepts and ideas associated with the modeling and analysis of intelligent robotic systems. Appropriate for researchers and engineers in the general area of robotics and automation, Intelligent Robotic Systems is both a solid reference as well as a text for a graduate level course in intelligent robotics/machines.

As robotic systems make their way into standard practice, they have opened the door to a wide spectrum of complex applications. Such applications usually demand that the robots be highly intelligent. Future robots are likely to have greater sensory capabilities, more intelligence, higher levels of manual dexter ity, and adequate mobility, compared to humans. In order to ensure high-quality control and performance in robotics, new intelligent control techniques must be developed, which are capable of coping with task complexity, multi-objective decision making, large volumes of perception data and substantial amounts of heuristic information. Hence, the pursuit of intelligent autonomous robotic systems has been a topic of much fascinating research in recent years. On the other hand, as emerging technologies, Soft Computing paradigms consisting of complementary elements of Fuzzy Logic, Neural Computing and Evolutionary Computation are viewed as the most promising methods towards intelligent robotic systems. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, Soft Computing techniques have found wide application in the area of intelligent control of robotic systems.

Incorporating intelligence in industrial systems can help to increase productivity, cut-off production costs, and to improve working conditions and safety in industrial environments. This need has resulted in the rapid development of modeling and control methods for industrial systems and robots, of fault detection and isolation methods for the prevention of critical situations in industrial work-cells and production plants, of optimization methods aiming at a more profitable functioning of industrial installations and robotic devices and of machine intelligence methods aiming at reducing human intervention in industrial systems operation. To this end, the book analyzes and extends some main directions of research in modeling and control for industrial systems. These are: (i) industrial robots, (ii) mobile robots and autonomous vehicles, (iii) adaptive and robust control of electromechanical systems, (iv) filtering and stochastic estimation for multisensor fusion and sensorless control of industrial systems (iv) fault detection and isolation in robotic and industrial systems, (v) optimization in industrial automation and robotic systems design, and (vi) machine intelligence for robots autonomy. The book will be a useful companion to engineers and researchers since it covers a wide spectrum of problems in the area of industrial systems. Moreover, the book is addressed to undergraduate and post-graduate students, as an upper-level course supplement of automatic control and robotics courses.

This book focusses on robotic skill learning and intelligent control for robotic manipulators including enabling of robots to efficiently learn motor and stiffness/force regulation policies from humans. It explains transfer of human limb impedance control strategies to the robots so that the adaptive impedance control for the robot can be realized.

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them. The contributions of the different authors have been grouped into four main sections: RobotsControl and IntelligenceSensingCollaborative automation The chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area.