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

Hanlin Wang, Michael Rubenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7180-7185
Number of pages6
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
CountryFrance
CityParis
Period5/31/208/31/20

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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