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Gas Distribution Mapping and Source Localization Using a 3D Grid of Metal Oxide Semiconductor Sensors
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain.
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-0217-9326
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain.
2020 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 304, article id 127309Article in journal (Refereed) Published
Abstract [en]

The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 304, article id 127309
Keywords [en]
Mobile robotic olfaction, Metal oxide gas sensors, Signal processing, Sensor networks, Gas source localization, Gas distribution mapping
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-78709DOI: 10.1016/j.snb.2019.127309ISI: 000500702500075Scopus ID: 2-s2.0-85075330402OAI: oai:DiVA.org:oru-78709DiVA, id: diva2:1380687
Note

Funding Agencies:

Spanish MINECO program  BES-2015-071698 TEC2014-59229-R

H2020-ICT by the European Commission  645101

Available from: 2019-12-19 Created: 2019-12-19 Last updated: 2020-02-05Bibliographically approved

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Hernandez Bennetts, VictorLilienthal, Achim J.

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