Neurotechnology For Biomimetic Robots

An overview of neurotechnology, the engineering of robots based on animals and animal behavior. The goal of neurotechnology is to confer the performance advantages of animal systems on robotic machines. Biomimetic robots differ from traditional robots in that they are agile, relatively cheap, and able to deal with real-world environments. The engineering of these robots requires a thorough understanding of the biological systems on which they are based, at both the biomechanical and physiological levels.This book provides an in-depth overview of the field. The areas covered include myomorphic actuators, which mimic muscle action; neuromorphic sensors, which, like animal sensors, represent sensory modalities such as light, pressure, and motion in a labeled-line code; biomimetic controllers, based on the relatively simple control systems of invertebrate animals; and the autonomous behaviors that are based on an animal's selection of behaviors from a species-specific behavioral "library." The ultimate goal is to develop a truly autonomous robot, one able to navigate and interact with its environment solely on the basis of sensory feedback without prompting from a human operator.

This state-of-the-art survey contains selected papers contributed by researchers in intelligent systems, cognitive robotics, and neuroscience including contributions from the MirrorBot project and from the NeuroBotics Workshop 2004. The research work presented demonstrates significant novel developments in biologically inspired neural models for use in intelligent robot environments and biomimetic cognitive behavior.

An introduction to how neuroethology can inform the development of robots controlled by synaptic networks instead of algorithms, from a pioneer in biorobotics. The trait most fundamental to the evolution of animals is the capability to adapt to novel circumstances in unpredictable environments. Recent advances in biomimetics have made it feasible to construct robots modeled on such unsupervised autonomous behavior, and animal models provide a library of existence proofs. Filling an important gap in the field, this introductory textbook illuminates how neurobiological principles can inform the development of robots that are controlled by synaptic networks, as opposed to algorithms. Joseph Ayers provides a comprehensive overview of the sensory and motor systems of a variety of model biological systems and shows how their behaviors may be implemented in artificial systems, such as biomimetic robots. Introduces the concept of biological intelligence as applied to robots, building a strategy for autonomy based on the neuroethology of simple animal models Provides a mechanistic physiological framework for the control of innate behavior Illustrates how biomimetic vehicles can be operated in the field persistently and adaptively Developed by a pioneer in biorobotics with decades of teaching experience Proven in the classroom Suitable for professionals and researchers as well as undergraduate and graduate students in cognitive science and computer science

An introduction to how neuroethology can inform the development of robots controlled by synaptic networks instead of algorithms, from a pioneer in biorobotics. The trait most fundamental to the evolution of animals is the capability to adapt to novel circumstances in unpredictable environments. Recent advances in biomimetics have made it feasible to construct robots modeled on such unsupervised autonomous behavior, and animal models provide a library of existence proofs. Filling an important gap in the field, this introductory textbook illuminates how neurobiological principles can inform the development of robots that are controlled by synaptic networks, as opposed to algorithms. Joseph Ayers provides a comprehensive overview of the sensory and motor systems of a variety of model biological systems and shows how their behaviors may be implemented in artificial systems, such as biomimetic robots. Introduces the concept of biological intelligence as applied to robots, building a strategy for autonomy based on the neuroethology of simple animal models Provides a mechanistic physiological framework for the control of innate behavior Illustrates how biomimetic vehicles can be operated in the field persistently and adaptively Developed by a pioneer in biorobotics with decades of teaching experience Proven in the classroom Suitable for professionals and researchers as well as undergraduate and graduate students in cognitive science and computer science

This book is for a first course in robotics, especially in unmanned aerial or underwater vehicles.

A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille

This book reports on the latest advances in the study of motion control in biomimetic swimming robots with high speed and high manoeuvrability. It presents state-of-the-art studies on various swimming robots including robotic fish, dolphins and jellyfish in a unified framework, and discusses the potential benefits of applying biomimetic underwater propulsion to autonomous underwater vehicle design, such as: speed, energy economy, enhanced manoeuvrability, and reduced detectability. Given its scope, the book will be of interest to researchers, engineers and graduate students in robotics and ocean engineering who wish to learn about the core principles, methods, algorithms, and applications of biomimetic underwater robots.