TY - GEN
T1 - Probabilistic mutual localization in multi-agent systems from anonymous position measures
AU - Franchi, Antonio
AU - Oriolo, Giuseppe
AU - Stegagno, Paolo
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79953149077&partnerID=8YFLogxK
U2 - 10.1109/CDC.2010.5717905
DO - 10.1109/CDC.2010.5717905
M3 - Conference contribution
AN - SCOPUS:79953149077
SN - 978-1-4244-7745-6
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 6534
EP - 6540
BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010
PB - IEEE
CY - Piscataway, NJ
T2 - 2010 49th IEEE Conference on Decision and Control, CDC 2010
Y2 - 15 December 2010 through 17 December 2010
ER -