Biologically Inspired Robot Behavior Engineering

Author:   Richard J. Duro ,  Jose Santos ,  Manuel Grana
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   Softcover reprint of the original 1st ed. 2003
Volume:   109
ISBN:  

9783790825176


Pages:   439
Publication Date:   21 October 2010
Format:   Paperback
Availability:   In Print   Availability explained
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Biologically Inspired Robot Behavior Engineering


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Author:   Richard J. Duro ,  Jose Santos ,  Manuel Grana
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   Softcover reprint of the original 1st ed. 2003
Volume:   109
Dimensions:   Width: 15.50cm , Height: 2.30cm , Length: 23.50cm
Weight:   0.700kg
ISBN:  

9783790825176


ISBN 10:   3790825174
Pages:   439
Publication Date:   21 October 2010
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1. Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots.- 2. Behavior coordination and its modification on monkey-type mobile robot.- 3. Visuomotor control in flies and behavior-based agents.- 4. Using evolutionary methods to parameterize neural models: a study of the lamprey central pattern generator.- 5. Biologically inspired neural network approaches to real-time collision-free robot motion planning.- 6. Self-adapting neural networks for mobile robots.- 7. Evolving robots able to integrate sensory-motor information over time.- 8. A non-computationally-intensive neurocontroller for autonomous mobile robot navigation.- 9. Some approaches for reusing behaviour based robot cognitive architectures obtained through evolution.- 10. Modular neural architectures for robotics.- 11. Designing neural control architectures for an autonomous robot using vision to solve complex learning tasks.- 12. Robust estimation of the optical flow based on VQ-BF.- 13. Steps towards one-shot vision-based self-localization.- 14. Computing the optimal trajectory of arm movement: the TOPS (Task Optimization in the Presence of Signal-dependent noise) model.- 15. A general learning approach to visually guided 3D-positioning and pose control of robot arms.

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