Bio-Inspired Self-Organizing Robotic Systems: Studies in Computational Intelligence, cartea 355
Editat de Yan Meng, Yaochu Jinen Limba Engleză Hardback – 8 mai 2011
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Specificații
ISBN-13: 9783642207594
ISBN-10: 3642207596
Pagini: 288
Ilustrații: X, 275 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.54 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642207596
Pagini: 288
Ilustrații: X, 275 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.54 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Part I: Self-Organizing Swarm Robotic Systems
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Part II: Self-Reconfigurable Modular Robots
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Part III: Autonomous Mental Development in Robotic Systems
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Part IV: Special Applications
Part III: Autonomous Mental Development in Robotic Systems
.-
Part IV: Special Applications.
.-
Part II: Self-Reconfigurable Modular Robots
.-
Part III: Autonomous Mental Development in Robotic Systems
.-
Part IV: Special Applications
Part III: Autonomous Mental Development in Robotic Systems
.-
Part IV: Special Applications.
Textul de pe ultima copertă
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.
Caracteristici
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