De-RANSAC: Robust consensus for robot formations

Eduardo Montijano, Sonia Martínez, and Carlos Sagüés
Network Science and Systems Issues in Multi-Robot Autonomy, Workshop at the IEEE International Conference on Robotics and Automation 2010, Anchorage, AK, USA, May 2010

Abstract:

This paper studies the problem of distributed consensus in the presence of spurious sensor information. We propose a new method, De-RANSAC, which allows a multi-agent system to detect outliers---erroneous measurements or incorrect hypotheses---when the sensed information is gathered in a distributed way. The method is an extension of the RANSAC (RANdom SAmple Consensus) algorithm, which leads to a consensus result on the goodness of a set of measurements with certain probability. In order to execute the full process in a decentralized way, we propose a distributed voting policy valid for fixed and switching topologies. Simulations of real applications are provided showing the reliability of the proposed method.

File: main.pdf

Bib-tex entry:

@InProceedings{EM-SM-CS:10-icra-netss,
author = {E. Montijano and S. Mart{\'\i}nez and C. Sag\"{u}\'{e}s},
booktitle = {Proceeding of the Network Science and Systems Issues in Multi-Robot Autonomy Workshop, ICRA-NETSS},
title = {De-{RANSAC}: {D}ecentralized {RANSAC} for {R}obot {F}ormations},
year = {2010},
month = {May},
address = {Anchorage, AK, USA}
}