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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, Germany.
BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
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(English)In: Sensor Letters, ISSN 1546-198XArticle in journal (Refereed) Accepted
Abstract [en]

In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental 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 the suitability of this algorithm for micro UAV platforms.

Place, publisher, year, edition, pages
American Scientific Publishers.
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-32670OAI: oai:DiVA.org:oru-32670DiVA: diva2:676770
Available from: 2013-12-06 Created: 2013-12-06 Last updated: 2017-10-17Bibliographically approved

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Hernandez Bennetts, VictorLilienthal, Achim J.
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