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Sensors 2014, 14(9), 17331-17352; doi:10.3390/s140917331

Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds

1
Applied Autonomous Sensor Systems, Örebro University, Fakultetsgatan 1, 70182 Örebro, Sweden
2
Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Baldiri Reixac 4-8, 08028-Barcelona, Spain
3
Departament d'Electrònica, Universitat de Barcelona, Martí i Franqués 1, 08028-Barcelona, Spain
*
Author to whom correspondence should be addressed.
Received: 18 August 2014 / Revised: 10 September 2014 / Accepted: 11 September 2014 / Published: 17 September 2014
(This article belongs to the Section Chemical Sensors)
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Abstract

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources. View Full-Text
Keywords: environmental monitoring; gas discrimination; gas distribution mapping; service robots; open sampling systems; PID, metal oxide sensors environmental monitoring; gas discrimination; gas distribution mapping; service robots; open sampling systems; PID, metal oxide sensors
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hernandez Bennetts, V.; Schaffernicht, E.; Pomareda, V.; Lilienthal, A.J.; Marco, S.; Trincavelli, M. Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds. Sensors 2014, 14, 17331-17352.

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