Worst-case optimal average consensus estimators for robot swarms

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

3 Scopus citations

Abstract

Average consensus estimators enable robots in a communication network to calculate the mean of their local inputs in a distributed manner. Many distributed control methods for robot swarms rely on these estimators. The performance of such estimators depends on their design and the network topology. For mobile sensor networks, this topology may be unknown, making it difficult to design average consensus estimators for optimal performance. We introduce a design method for proportional-integral (PI) average consensus estimators that decouples estimator synthesis from network topology. This method also applies to the more general internal model (IM) estimator, yielding extended PI estimators that improve convergence rates without increasing communication costs. In simulations over many geometric random graphs, the extended PI estimator consistently reduces the estimation error settling time by a factor of five.

Original languageEnglish (US)
Title of host publicationIROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3814-3819
Number of pages6
ISBN (Electronic)9781479969340
DOIs
StatePublished - Oct 31 2014
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
CountryUnited States
CityChicago
Period9/14/149/18/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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