Convergence of ant colony multi-agent swarms

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

Ant Colony algorithms are a set of biologically inspired algorithms used commonly to solve distributed optimization problems. Convergence has been proven in the context of optimization processes, but these proofs are not applicable in the framework of robotic control. In order to use Ant Colony algorithms to control robotic swarms, we present in this work more general results that prove asymptotic convergence of a multi-agent Ant Colony swarm moving in a weighted graph.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Hybrid Systems (HSCC 2020)
Subtitle of host publicationComputation and Control, part of CPS-IoT Week
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)978-1-4503-7018-9
DOIs
Publication statusPublished - 2020
EventHSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control - Sydney, Australia
Duration: 21 Apr 202024 Apr 2020

Conference

ConferenceHSCC '20: 23rd ACM International Conference on Hybrid Systems: Computation and Control
Country/TerritoryAustralia
CitySydney
Period21/04/2024/04/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • ant colony
  • convergence
  • random walk
  • swarm robotics

Fingerprint

Dive into the research topics of 'Convergence of ant colony multi-agent swarms'. Together they form a unique fingerprint.

Cite this