Probabilistic mutual localization in multi-agent systems from anonymous position measures

Antonio Franchi*, Giuseppe Oriolo, Paolo Stegagno

*Corresponding author for this work

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

16 Citations (Scopus)

Abstract

Recent research on multi-agent systems has produced a plethora of decentralized controllers that implicitly assume various degrees of agent localization. However, many practical arrangements commonly taken to allow and achieve localization imply some form of centralization, from the use of physical tagging to allow the identification of the single agent to the adoption of global positioning systems based on cameras or GPS. These devices clearly decrease the system autonomy and range of applicability, and should be avoided if possible. Following this guideline, this work addresses the mutual localization problem with anonymous relative position measures, presenting a robust solution based on a probabilistic framework. The proposed localization system exhibits higher accuracy and lower complexity (O(n2)) than our previous method [1]. Moreover, with respect to more conventional solutions that could be conceived on the basis of the current literature, our method is theoretically suitable for tasks requiring frequent, many-to-many encounters among agents (e.g., formation control, cooperative exploration, multiple-view environment sensing). The proposed localization system has been validated by means of an extensive experimental study.

Original languageEnglish
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages6534-6540
Number of pages7
ISBN (Electronic)978-1-4244-7746-3, 978-1-4244-7744-9 (CD)
ISBN (Print)978-1-4244-7745-6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: 15 Dec 201017 Dec 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
PublisherIEEE
Volume2010
ISSN (Print)0191-2216
ISSN (Electronic)0743-1546

Conference

Conference2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA
Period15/12/1017/12/10

Fingerprint Dive into the research topics of 'Probabilistic mutual localization in multi-agent systems from anonymous position measures'. Together they form a unique fingerprint.

Cite this