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A Multiple UAV System for Vision-Based Search and Localization
University of California, Berkeley, CA, USA.
University of California, Berkeley, CA, USA.
University of California, Berkeley, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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2008 (English)Report (Other academic)
Abstract [en]

The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2865
Keyword [en]
Bayes methods, Aerospace control, Aircraft, Cameras, Particle filtering
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-56188ISRN: LiTH-ISY-R-2865OAI: diva2:316982
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-01Bibliographically approved

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Törnqvist, David
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Automatic ControlThe Institute of Technology
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