To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Robot-aided Gas Sensing for Emergency Responses
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-1662-0960
2022 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Emergency response personnel can be exposed to various extreme hazards during the response to natural and human-made disasters. In many of the scenarios, one of the risk factors is the presence of hazardous airborne chemicals. Addressing this risk factor requires typical tiring, taxing and toxic operations that are suitable to be aided by Mobile Robot Olfaction (MRO) techniques. MRO is the research domain combining intelligent mobile robots with an artificial sense of smell. It presents the prospect of practical applications for emergency response as it allows to convey useful information on-site and online without risking the safety of human responders. However, standard gas sampling procedures for laboratory use are not directly applicable to MRO due to the complexity of uncontrolled environments and the need for fast deployment and analysis. Besides, state-of-the-art gas sensing approaches have difficulties handling A Priori Unknown Gases (APUG). In APUG situations, the number or/and identities of the present chemicals are unknown, posing challenges in recognizing the underlying risks with conventional solutions such as supervised learning-based electronic noses or dedicated gas sensors targeting known analytes.

This dissertation focuses on contributions toward real-world applications of robot-aided gas sensing with an APUG problem setup. The dissertation starts with a requirement analysis of Gas Sensing for Emergency Response (GSER) to identify the key tasks in ad hoc applications. Considering that not all analytes of interest in a field application may be known in advance, a pipeline incorporating non-supervised detection and discrimination of multiple chemicals and consequent distribution modelling is found to be important for GSER. The remainder of the thesis fills this pipeline with three steps: 1) An ensemble learning-based gas detection approach is proposed to recognize significant changes from sensor signals as well as model the baseline response pattern. 2) A clustering analysis-based gas discrimination approach is developed to perform online analysis that automatically learns the number of different chemical compounds from the acquired measurements and provides a probabilistic representation of their class labels. 3) The integration of the proposed non-supervised gas detection and gas discrimination approaches with gas distribution modelling allows prototyping of a GSER system, which can enhance emergency responders’ situational awareness in the target environment. This GSER system demonstrates the concept of discriminating and mapping multiple unknown chemical compounds in uncontrolled environments with validation and evaluation using real-world data sets.

During the research on the GSER system, gas dispersal simulation is also investigated to facilitate MRO algorithm development and validation in general. In-field experiments of MRO algorithms are often time-consuming, expensive, cumber some, and lack repeatability, while most of the available simulation tools are limited to insitu gas sensors and simple environments. These issues were addressed by improving a simulation framework to replicate geometrical representations of actual real-world environments and support a variety of gas sensor models. The potential applicability of the resulting work is demonstrated by simulating a gas emission monitoring task and facilitating the development process of a state-of-the-art time-dependent gas distribution modelling algorithm.

Place, publisher, year, edition, pages
Örebro: Örebro University , 2022. , p. 176
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 95
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-101509ISBN: 9789175294643 (print)OAI: oai:DiVA.org:oru-101509DiVA, id: diva2:1699641
Public defence
2022-11-18, Örebro universitet, Långhuset, Hörsal L2, Fakultetsgatan 1, Örebro, 13:00 (English)
Opponent
Supervisors
Available from: 2022-09-28 Created: 2022-09-28 Last updated: 2024-01-03Bibliographically approved

Open Access in DiVA

Cover(171 kB)37 downloads
File information
File name COVER01.pdfFile size 171 kBChecksum SHA-512
47d3cb411aa24b13eb1dadcf201536a9dbc26a21ed1083c7e958d3651366bd97f30a046537d93f191b0a84d1105454922660f8e9b29488bd5ec945250b3eff11
Type coverMimetype application/pdf
Spikblad(75 kB)35 downloads
File information
File name SPIKBLAD01.pdfFile size 75 kBChecksum SHA-512
c6e7bfa3543fa0bb10b506d3f81d05babd445744642223ff7bc223f6a5bd849ebc473fbd6e27f825c86c9031c1bdc26487fcc9b4a680a0d99e33972d2e52a56a
Type spikbladMimetype application/pdf

Authority records

Fan, Han

Search in DiVA

By author/editor
Fan, Han
By organisation
School of Science and Technology
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 207 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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