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
Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge
German Aerospace Center, Oberpfaffenhofen, Germany. (AASS MRO Lab)
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
German Aerospace Center, Oberpfaffenhofen, Germany.ORCID iD: 0000-0002-6065-6453
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 520Article in journal (Refereed) Published
Abstract [en]

In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019. Vol. 19, no 3, article id 520
Keywords [en]
Robotic exploration, gas source localization, mobile robot olfaction, sparse Bayesian learning, multi-agent system, advection-diffusion model
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-71964DOI: 10.3390/s19030520ISI: 000459941200083PubMedID: 30691174Scopus ID: 2-s2.0-85060572534OAI: oai:DiVA.org:oru-71964DiVA, id: diva2:1284133
Projects
SmokeBot (EC H2020, 645101)
Note

Funding Agencies:

European Commission  645101 

Valles Marineris Explorer initiative of DLR (German Aerospace Center) Space Administration 

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-03-19Bibliographically approved

Open Access in DiVA

Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge(1497 kB)13 downloads
File information
File name FULLTEXT01.pdfFile size 1497 kBChecksum SHA-512
613bbf630336b45bf84479043e953b7d0fbdbb1094b8157024bf1426ba2ee43b579d1614ab396c52da2eeec730329a6932cf7b4c2a50ed9f34df5cc964576ae0
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records BETA

Wiedemann, ThomasLilienthal, Achim

Search in DiVA

By author/editor
Wiedemann, ThomasLilienthal, AchimShutin, Dmitriy
By organisation
School of Science and Technology
In the same journal
Sensors
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 13 downloads
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

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 63 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