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E-raamat: Evolutionary Robotics: From Algorithms To Implementations

(Nus, S'pore), (Nus, S'pore), (Texas A&m Univ, Usa)
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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.

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"This book is a refreshing and much needed addition to the field of Robotics, and concentrates on the application of soft computing for intelligent sensing, learning, and robot navigation ... the book is particularly valuable for researchers and practitioners of intelligent robot navigation, as a convenient and advanced reference. The authors have been able to expand the horizons of the field and to introduce novel ways of looking at the traditional problem of robot navigation. Some research topics are indicated as well, making the book suitable for researchers in the use of soft computing in robotic navigation. The book is quite readable, and it challenges the readers to explore further ideas and issues in the field."IEEE Computational Intelligence Magazine

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.
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