This invaluable book comprehensively describes evolutionary robotics and computational intelligence, and how different computational intelligence techniques are applied to robotic system design. It embraces the most widely used evolutionary approaches with their merits and drawbacks, presents some related experiments for robotic behavior evolution and the results achieved, and shows promising future research directions. Clarity of explanation is emphasized such that a modest knowledge of basic evolutionary computation, digital circuits and engineering design will suffice for a thorough understanding of the material. The book is ideally suited to computer scientists, practitioners and researchers keen on computational intelligence techniques, especially the evolutionary algorithms in autonomous robotics at both the hardware and software levels. Sample Chapter(s). Chapter 1: Artificial Evolution Based Autonomous Robot Navigation (184 KB). Contents: Artificial Evolution Based Autonomous Robot Navigation; Evolvable Hardware in Evolutionary Robotics; FPGA-Based Autonomous Robot Navigation via Intrinsic Evolution; Intelligent Sensor Fusion and Learning for Autonomous Robot Navigation; Task-Oriented Developmental Learning for Humanoid Robots; Bipedal Walking Through Reinforcement Learning; Swing Time Generation for Bipedal Walking Control Using GA Tuned Fuzzy Logic Controller; Bipedal Walking: Stance Ankle Behavior Optimization Using Genetic Algorithm. Readership: Researchers in evolutionary robotics, and graduate and advanced undergraduate students in computational intelligence.

An overview of the basic concepts and methodologies of evolutionary robotics, which views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention.

Evolutionary Algorithms (EAs) now provide mature optimization tools that have successfully been applied to many problems, from designing antennas to complete robots, and provided many human-competitive results. In robotics, the integration of EAs within the engineer’s toolbox made tremendous progress in the last 20 years and proposes new methods to address challenging problems in various setups: modular robotics, swarm robotics, robotics with non-conventional mechanics (e.g. high redundancy, dynamic motion, multi-modality), etc. This book takes its roots in the workshop on "New Horizons in Evolutionary Design of Robots" that brought together researchers from Computer Science and Robotics during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009) in Saint Louis (USA). This book features extended contributions from the workshop, thus providing various examples of current problems and applications, with a special emphasis on the link between Computer Science and Robotics. It also provides a comprehensive and up-to-date introduction to Evolutionary Robotics after 20 years of maturation as well as thoughts and considerations from several major actors in the field. This book offers a comprehensive introduction to the current trends and challenges in Evolutionary Robotics for the next decade.

Today, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments.

This is an unusual book. It launches a new style of research into the nature of the mind, a style that pro?ciently uncovers, explores and exploits the synergies between complex systems thinking, sophisticated theoretical critique, synthetic modeling technologies and experimental work. Rather than adopting a grandiose programmatic approach, Marieke Rohde presents us with a pragmatic conjunction of elements, each of them strongly feeding off the others and making it impossible to shelf her work strictly under any one rubric such as psychology, robotics, arti?cial intelligence or philosophy of mind. Perhaps the least unjust choice is to call this a work of new cognitive science. It is yesterday’s news to remark on how our conceptual framework for understanding c- plex systems is changing. There is a recognized need to supplement the scienti?c categories of mechanistic, XIX century thought for new ways of thinking about non-linear forms of interaction and inter-relation between events and processes at multiple scales. Since the times of cybernetics and in parallel to the development of the computer as a scienti?c tool, we have witnessed several proposals for “revolutionary” ways of dealing with complexity: catastrophe theory, general systems theory, chaos, self-organized criticality, complex n- works, etc. Despite not always ful?lling their stated potential, these ideas have helped us increase our capability to understand complex systems and have in general left us with new concepts, new tools and new ways of formulating questions. This conceptual change, however, has not been homogeneous.

An authoritative overview of current research in this exciting interdisciplinary field.

What Is Evolutionary Robotics An embodied approach to artificial intelligence (AI), evolutionary robotics involves the creation of robots by the use of Darwinian concepts of natural selection during the design process. A behavioral goal can be used to guide the design of a robot or a subsystem of a robot, such as a neural controller. This design is then optimized. In most cases, designs are reviewed using computer simulations because it would be extremely expensive to physically fabricate thousands or millions of designs and test them in the actual world due to the amount of time, money, and risk involved. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Evolutionary Robotics Chapter 2: Evolutionary Algorithm Chapter 3: Evolutionary Computation Chapter 4: Neuroevolution Chapter 5: Neuroevolution of Augmenting Topologies Chapter 6: Evolvable Hardware Chapter 7: Dario Floreano Chapter 8: Stefano Nolfi Chapter 9: HyperNEAT Chapter 10: Evolutionary Developmental Robotics (II) Answering the public top questions about evolutionary robotics. (III) Real world examples for the usage of evolutionary robotics in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of evolutionary robotics' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of evolutionary robotics.

In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. The book tries to mediate between two apparently opposed perspectives: engineering and cognitive science. The experiments presented in the book and the results obtained contribute to the assessment of ER not only as a design tool, but also as a methodology for modelling and understanding intelligent adaptive behaviours.