oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-4026-7490
Örebro University, School of Science and Technology. (Mobile Robotics & Olfaction Lab, AASS Research Center)ORCID iD: 0000-0003-0217-9326
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 19, no 3, article id E685
Keywords [en]
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
National Category
Robotics
Identifiers
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
Note

Funding Agency:

European Commission  645101

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-06-19Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim

Search in DiVA

By author/editor
Fan, HanHernandez Bennetts, VictorSchaffernicht, ErikLilienthal, Achim
By organisation
School of Science and Technology
In the same journal
Sensors
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 185 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf