Achieving Consensus in Robot Swarms

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128,39 

Design and Analysis of Strategies for the best-of-n Problem, Studies in Computational Intelligence 706

ISBN: 3319536087
ISBN 13: 9783319536088
Autor: Valentini, Gabriele
Verlag: Springer Verlag GmbH
Umfang: xiv, 146 S., 9 s/w Illustr., 37 farbige Illustr., 146 p. 46 illus., 37 illus. in color.
Erscheinungsdatum: 22.02.2017
Auflage: 1/2017
Produktform: Gebunden/Hardback
Einband: Gebunden

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.

Artikelnummer: 1021971 Kategorie:

Beschreibung

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a nite set when optimizing benets and costs. The book leverages the identication of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, usingrobot experiments to show how the designed strategies can be ported to different application scenarios.

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E-Mail: juergen.hartmann@springer.com

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