Achieving Consensus in Robot Swarms: Design and Analysis of by Gabriele Valentini

By Gabriele Valentini

This ebook makes a speciality of the layout and research of collective decision-making thoughts for the best-of-n challenge. After supplying a formalization of the constitution of the best-of-n challenge supported by way of a accomplished survey of the swarm robotics literature, it introduces the functioning of a collective decision-making method and identifies a suite of mechanisms which are crucial for a method 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 settle on one choice from of a finite set whilst optimizing benefits and prices. The booklet leverages the identification of those mechanisms to enhance a modular and model-driven method to layout collective decision-making techniques and to research their functionality at various point of abstractions. finally, the writer offers a sequence of case reviews within which the proposed technique is used to layout diversified recommendations, utilizing robotic experiments to teach how the designed concepts may be ported to varied program scenarios.

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Wörn, K. Crailsheim, Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robot. Auton. Syst. 57(9), 913–921 (2009a) T. Schmickl, R. Thenius, C. Moeslinger, G. Radspieler, S. Kernbach, M. Szymanski, K. Crailsheim, Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agents Multi-Agent Syst. D. K. Visscher, T. M. R. R. Marshall, Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335(6064), 108–111 (2012) O.

2009) and developed a control strategy taking inspiration from theoretical studies of the aggregation behaviors of cockroaches (Amé et al. 2006). In their strategy the robot controller is composed of 3 phases: exploration, stay under a shelter, and move back to the shelter. Initially, the robots explore the environment by performing a random walk. Once a robot discovers a shelter, it moves randomly within the shelter’s area and estimates the density of other robots therein. If during this phase, a robot accidentally exits the shelter, it performs a U-turn aimed at reentering the original shelter.

Evans, A. Martinoli, Aggregation-mediated collective perception and action in a group of miniature robots, in Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2, AAMAS ’10 (IFAAMAS, 2010), pp. 599–606 G. Mermoud, U. C. Evans, A. Martinoli, Top-down vs. bottom-up model-based methodologies for distributed control: a comparative experimental study, ed. By O. Khatib, V. Kumar, G. Experimental Robotics, vol. 79. STAR (Springer, 2014), pp.

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