A Fast, Accurate, and Scalable Probabilistic Sample-Based Approach for Counting Swarm Size

Hanlin Wang; Michael Rubenstein

ICRA 2020poster5 citations

A Fast, Accurate, and Scalable Probabilistic Sample-Based Approach for Counting Swarm Size

Abstract

This paper describes a distributed algorithm for computing the number of robots in a swarm, only requiring communication with neighboring robots. The algorithm can adjust the estimated count when the number of robots in the swarm changes, such as the addition or removal of robots. Probabilistic guarantees are given, which show the accuracy of this method, and the trade-off between accuracy, speed, and adaptability to changing numbers. The proposed approach is demonstrated in simulation as well as a real swarm of robots.

BibTeX

@inproceedings{icra2020_afastaccurateand,
  title = {A Fast, Accurate, and Scalable Probabilistic Sample-Based Approach for Counting Swarm Size},
  author = {Hanlin Wang and Michael Rubenstein},
  booktitle = {ICRA 2020},
  year = {2020}
}