Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 202)
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About this book
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.
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.
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Keywords
Table of contents (30 chapters)
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Front Matter
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Trajectory Generation
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Front Matter
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Recurrent Networks
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Front Matter
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Reviews
` Given the potential of neural networks to reduce the design complexity of control systems through learning, this book is essential for those involved in the research and development of robotic control systems. '
University Computing, Vol.15/4, 1993
University Computing, Vol.15/4, 1993
Editors and Affiliations
Bibliographic Information
Book Title: Neural Networks in Robotics
Editors: George A. Bekey, Kenneth Y. Goldberg
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-3180-7
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1993
Hardcover ISBN: 978-0-7923-9268-2Published: 30 November 1992
Softcover ISBN: 978-1-4613-6394-1Published: 12 October 2012
eBook ISBN: 978-1-4615-3180-7Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XII, 563
Topics: Robotics and Automation, Control, Robotics, Mechatronics, Complex Systems, Artificial Intelligence, Statistical Physics and Dynamical Systems