An Information-Driven Algorithm in Flocking Systems for an Improved Obstacle Avoidance

Ertug Olcay, Boris Lohmann, Maruthi R. Akella

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The research field of swarm robotics continues to draw inspiration from the behavior of animals in nature. Obstacle avoidance and the navigation in unknown areas are major problems in control of swarming agents. In recent years, a large number of algorithms have been developed for this purpose. These algorithms are mainly based on artificial potential functions and many of the existing strategies do not enable agents to escape from non-convex obstacles. Due to the issue of local minimum, agents with a limited sensor range can often stay stuck behind concave obstacles. In this study, we propose a new algorithm to make many concave obstacles avoidable for flocks in unknown environments through sharing and evaluating the aggregated sensing information among the group.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages298-304
Number of pages7
ISBN (Electronic)9781728148786
DOIs
StatePublished - Oct 2019
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 14 Oct 201917 Oct 2019

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/TerritoryPortugal
CityLisbon
Period14/10/1917/10/19

Keywords

  • cooperative control
  • flocking control
  • multi-agent systems
  • obstacle avoidance

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