Peer-to-Peer Localization via On-board Sensing for Aerial Flocking

Fat Hy Omar Rajab, Samet Guler, Jeff S. Shamma

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

Abstract

The performance of mobile multi-robot systems dramatically depends on the mutual awareness of individual robots, particularly the positions of other robots. GPS and motion capture cameras are commonly used to acquire and ultimately communicate positions of robots. Such sensing schemes depend on infrastructure and restrict the capabilities of a multi-robot system, e.g., the robots cannot operate in both indoor and outdoor environments. Conversely, peer-to-peer localization algorithms can be used to free the robots from such infrastructures. In such systems, robots use on-board sensing to infer the positions of nearby robots. In this approach, it is essential to have a model of the motion of other robots. We introduce a flocking localization scheme that takes into account motion behavior exhibited by the other robots. The proposed scheme depends only on the robots' on-board sensors and computational capabilities and yields a more accurate localization solution than the peer-to-peer localization algorithms that do not take into account the flocking behavior. We verify the performance of our scheme in simulations and demonstrate experiments on two unmanned aerial vehicles.

Original languageEnglish (US)
Title of host publication2020 17th International Conference on Ubiquitous Robots, UR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages464-471
Number of pages8
ISBN (Electronic)9781728157153
DOIs
StatePublished - Jun 2020
Externally publishedYes
Event17th International Conference on Ubiquitous Robots, UR 2020 - Kyoto, Japan
Duration: Jun 22 2020Jun 26 2020

Publication series

Name2020 17th International Conference on Ubiquitous Robots, UR 2020

Conference

Conference17th International Conference on Ubiquitous Robots, UR 2020
CountryJapan
CityKyoto
Period6/22/206/26/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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