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Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-1662-0960
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-4026-7490
Örebro universitet, Institutionen för naturvetenskap och teknik. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID-id: 0000-0003-0217-9326
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 3, artikkel-id E685Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

sted, utgiver, år, opplag, sider
MDPI, 2019. Vol. 19, nr 3, artikkel-id E685
Emneord [en]
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:oru:diva-72366DOI: 10.3390/s19030685ISI: 000459941200248PubMedID: 30736489Scopus ID: 2-s2.0-85061226919OAI: oai:DiVA.org:oru-72366DiVA, id: diva2:1287969
Merknad

Funding Agency:

European Commission  645101

Tilgjengelig fra: 2019-02-12 Laget: 2019-02-12 Sist oppdatert: 2020-02-06bibliografisk kontrollert

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