Robust distributed decision-making in robot swarms: Exploiting a third truth state

Michael Crosscombe; Jonathan Lawry; Sabine Hauert; Martin Homer

IROS 2017poster30 citations

Robust distributed decision-making in robot swarms: Exploiting a third truth state

Michael Crosscombe, Jonathan Lawry, Sabine Hauert, Martin Homer

Abstract

In this paper, we investigate the best-of-n distributed decision problem in robot swarms. In this context, we compare the weighted voter model [25] with a three-valued model that incorporates an intermediate belief state meaning either `uncertain' or `indifferent'. We focus particularly on the trade-off between speed of convergence to a shared belief, and robustness to the presence of unreliable individuals in the population. By means of both simulation and embodied experiments in real robot swarms of 400 Kilobots, we show that the three-valued model is much more robust than the weighted voter model, but with decreased speed of convergence.

BibTeX

@inproceedings{iros2017_robustdistribute,
  title = {Robust distributed decision-making in robot swarms: Exploiting a third truth state},
  author = {Michael Crosscombe and Jonathan Lawry and Sabine Hauert and Martin Homer},
  booktitle = {IROS 2017},
  year = {2017}
}