The best-of-n problem in robot swarms: Formalization, state of the art, and novel perspectives

Gabriele Valentini*, Eliseo Ferrante, Marco Dorigo

*Corresponding author for this work

Research output: Contribution to JournalReview articleAcademicpeer-review

Abstract

The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem, while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.

Original languageEnglish
Article number9
JournalFrontiers Robotics AI
Volume4
Issue numberMAR
DOIs
Publication statusPublished - 1 Mar 2017
Externally publishedYes

Keywords

  • Best-of-n problem
  • Collective decision-making
  • Consensus achievement
  • Self-organization
  • Swarm robotics

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