| 期刊全稱(chēng) | Achieving Consensus in Robot Swarms | | 期刊簡(jiǎn)稱(chēng) | Design and Analysis | | 影響因子2023 | Gabriele Valentini | | 視頻video | http://file.papertrans.cn/144/143850/143850.mp4 | | 發(fā)行地址 | Covers collective decision-making strategies for robot swarms.Focuses on the design of self-organized solutions to the best-of-n problem—the problem of deciding which alternative among a finite set of | | 學(xué)科分類(lèi) | Studies in Computational Intelligence | | 圖書(shū)封面 |  | | 影響因子 | .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 identi?es 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 bene?ts and costs. The book leverages the identi?cation 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.. | | Pindex | Book 2017 |
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