Overview
- State-of-the-art research inspired by biological principles for self-organizing robotic systems
- Bridges multi-disciplinary research areas such as robotics, artificial life, systems biology, and evolutionary computation
- Written by experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 355)
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Table of contents (11 chapters)
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Front Matter
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Self-Organizing Swarm Robotic Systems
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Front Matter
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Autonomous Mental Development in Robotic Systems
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Front Matter
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Special Applications
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Front Matter
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Back Matter
Keywords
About this book
Self-organizing approaches inspired from biological systems, such as social insects, genetic, molecular and cellular systems under morphogenesis, and human mental development, has enjoyed great success in advanced robotic systems that need to work in dynamic and changing environments. Compared with classical control methods for robotic systems, the major advantages of bio-inspired self-organizing robotic systems include robustness, self-repair and self-healing in the presence of system failures and/or malfunctions, high adaptability to environmental changes, and autonomous self-organization and self-reconfiguration without a centralized control. “Bio-inspired Self-organizing Robotic Systems” provides a valuable reference for scientists, practitioners and research students working on developing control algorithms for self-organizing engineered collective systems, such as swarm robotic systems, self-reconfigurable modular robots, smart material based robotic devices, unmanned aerial vehicles, and satellite constellations.
Editors and Affiliations
Bibliographic Information
Book Title: Bio-Inspired Self-Organizing Robotic Systems
Editors: Yan Meng, Yaochu Jin
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-20760-0
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-20759-4Published: 08 May 2011
Softcover ISBN: 978-3-662-50664-6Published: 23 August 2016
eBook ISBN: 978-3-642-20760-0Published: 11 May 2011
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 275
Topics: Computational Intelligence, Robotics and Automation, Artificial Intelligence