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FireNose on Mobile Robot in Harsh Environments
School of Engineering, University of Warwick, Coventry, UK.
School of Engineering, University of Warwick, Coventry, UK.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-4026-7490
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2019 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 19, no 24, p. 12418-12431Article in journal (Refereed) Published
Abstract [en]

In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 19, no 24, p. 12418-12431
Keywords [en]
FireNose, mobile robot, MOX sensor, gas map, harsh environments
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-77784DOI: 10.1109/JSEN.2019.2939039ISI: 000506895500081PubMedID: 2-s2.0-85076340302Scopus ID: 2-s2.0-85076340302OAI: oai:DiVA.org:oru-77784DiVA, id: diva2:1368091
Funder
EU, Horizon 2020Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2020-02-05Bibliographically approved

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Fan, HanSchaffernicht, ErikHernandez Bennetts, VictorLilienthal, Achim J.

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