@inproceedings{3db346fea5f04fc194dfeca22505976d,
title = "Worst-case optimal average consensus estimators for robot swarms",
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.",
author = "Matthew Elwin and Freeman, {Randy A} and Lynch, {Kevin M}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 ; Conference date: 14-09-2014 Through 18-09-2014",
year = "2014",
month = oct,
day = "31",
doi = "10.1109/IROS.2014.6943098",
language = "English (US)",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3814--3819",
booktitle = "IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems",
address = "United States",
}