By Gabriele Valentini
This booklet makes a speciality of the layout and research of collective decision-making thoughts for the best-of-n challenge. After offering a formalization of the constitution of the best-of-n challenge supported via a complete survey of the swarm robotics literature, it introduces the functioning of a collective decision-making approach and identiﬁes a collection of mechanisms which are crucial for a technique to unravel the best-of-n challenge. The best-of-n challenge is an abstraction that captures the common requirement of a robotic swarm to decide on one choice from of a ﬁnite set whilst optimizing beneﬁts and prices. The e-book leverages the identiﬁcation of those mechanisms to advance a modular and model-driven method to layout collective decision-making concepts and to investigate their functionality at varied point of abstractions. finally, the writer presents a sequence of case stories during which the proposed method is used to layout assorted recommendations, utilizing robotic experiments to teach how the designed ideas will be ported to diverse program scenarios.
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Extra resources for Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem
As the robot moves in the environment, it spreads and receives virtual nectar to and from other neighboring robots. This behavior allows robots to create a virtual gradient of nectar that can be used by robots to navigate back and forth a pair of locations following the shortest of two paths or to orient towards the largest of two aggregation areas. , the length of a path and the size of the aggregation area) which influences the rate of diffusion of virtual nectar. This trophallaxis-inspired strategy has been studied later using models of Brownian motion (Hamann 2010; Hamann and Wörn 2008).
The authors find that the weighted mean underlying social odometry favors the selection by the swarm of the closest foraging patch due to the fact that robots traveling to that patch have higher confidence in their location estimates. In this strategy, the option quality is determined by a combination of an internal preference factor with an environmental bias factor. The internal preference is represented by the level of confidence because it is derived by each robot from its own measured distance.
Labella, E. Sahin, ¸ M. Dorigo, Evolving aggregation behaviors in a swarm of robots, ed. By W. Banzhaf, J. Ziegler, T. Christaller, P. T. Kim. Advances in Artificial Life, vol. 2801. LNCS (Springer, 2003), pp. 865–874 V. Trianni, C. L. Christensen, E. Tuci, M. Dorigo, S. Nolfi, From solitary to collective behaviours: decision making and cooperation, ed. By F. M. Rocha, E. Costa, I. Harvey, A. Coutinho. Advances in Artificial Life, vol. 4648. LNCS (Springer, 2007), pp. E. Turgut, H. Çelikkanat, F.
Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem by Gabriele Valentini