English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. View item / 
  2. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

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

MPS-Authors
/persons/resource/persons83915

Franchi,  A
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717905
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Franchi, A., Oriolo, G., & Stegagno, P. (2010). Probabilistic mutual localization in multi-agent systems from anonymous position measures. In 49th IEEE Conference on Decision and Control (CDC 2010) (pp. 6534-6540). Piscataway, NJ, USA: IEEE.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BD3A-B
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, manyto-
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.