oru.sePublikationer
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
Time-dependent gas distribution modelling
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0003-1662-0960
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (Center for Autonomous Applied Sensor Systems (AASS))ORCID iD: 0000-0003-0217-9326
2017 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 96, 157-170 p.Article in journal (Refereed) Published
Abstract [en]

Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 96, 157-170 p.
Keyword [en]
Mobile robot olfaction, Statistical gas distribution modelling, Temporal sub-sampling, Time-dependent gas distribution modelling
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-62783DOI: 10.1016/j.robot.2017.05.012ISI: 000413881900014OAI: oai:DiVA.org:oru-62783DiVA: diva2:1159790
Note

Funding Agency:

EC  FP7-224318-DIADEM

Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Asadi, SaharFan, HanHernandez Bennetts, VictorLilienthal, Achim

Search in DiVA

By author/editor
Asadi, SaharFan, HanHernandez Bennetts, VictorLilienthal, Achim
By organisation
School of Science and Technology
In the same journal
Robotics and Autonomous Systems
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1046 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