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A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields
BAM Federal Institute for Materials Research and Testing, Berlin.
Institute of Computer Science, FU University, Berlin, Germany.
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
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2014 (English)In: Sensor Letters, ISSN 1546-198X, Vol. 12, no 6-7, 1113-1118 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can addressenvironmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions,we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate thesuitability of this algorithm for micro UAV platforms.

Place, publisher, year, edition, pages
Valencia, USA: American Scientific Publishers, 2014. Vol. 12, no 6-7, 1113-1118 p.
Keyword [en]
autonomous micro UAV, chemical and wind sensing, gas source localization, particle filter
National Category
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-41249DOI: 10.1166/sl.2014.3168Scopus ID: 2-s2.0-84911400337OAI: oai:DiVA.org:oru-41249DiVA: diva2:780080
Available from: 2015-01-13 Created: 2015-01-13 Last updated: 2016-12-13Bibliographically approved

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Hernandez Bennetts, VictorLilienthal, Achim J.
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