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A Realistic Remote Gas Sensor Model for Three-Dimensional Olfaction Simulations
Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany. (AASS MRO Lab)
Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany.
Machine Perception and Intelligent Robotics group (MAPIR), Universidad de Malaga, Spain.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
2019 (English)In: ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, article id 8823330Conference paper, Published paper (Refereed)
Abstract [en]

Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring. A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used. Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms. For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing. In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account. Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.

Place, publisher, year, edition, pages
IEEE, 2019. article id 8823330
Keywords [en]
gas detector, remote gas sensor, sensor modelling, TDLAS, gas dispersion simulation
National Category
Remote Sensing Robotics
Identifiers
URN: urn:nbn:se:oru:diva-76220DOI: 10.1109/ISOEN.2019.8823330Scopus ID: 2-s2.0-85072976677ISBN: 978-1-5386-8327-9 (electronic)ISBN: 978-1-5386-8328-6 (print)OAI: oai:DiVA.org:oru-76220DiVA, id: diva2:1350265
Conference
2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka, japan, mMy 26-29, 2019
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2020-02-06Bibliographically approved

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Hüllmann, DinoLilienthal, Achim J.

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