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A Comparison of Multiple Odor Source Localization Algorithms
There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a gi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223208/ https://www.ncbi.nlm.nih.gov/pubmed/37430713 http://dx.doi.org/10.3390/s23104799 |
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author | Staples, Marshall Hugenholtz, Chris Serrano-Ramirez, Alex Barchyn, Thomas E. Gao, Mozhou |
author_facet | Staples, Marshall Hugenholtz, Chris Serrano-Ramirez, Alex Barchyn, Thomas E. Gao, Mozhou |
author_sort | Staples, Marshall |
collection | PubMed |
description | There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow. |
format | Online Article Text |
id | pubmed-10223208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102232082023-05-28 A Comparison of Multiple Odor Source Localization Algorithms Staples, Marshall Hugenholtz, Chris Serrano-Ramirez, Alex Barchyn, Thomas E. Gao, Mozhou Sensors (Basel) Article There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow. MDPI 2023-05-16 /pmc/articles/PMC10223208/ /pubmed/37430713 http://dx.doi.org/10.3390/s23104799 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Staples, Marshall Hugenholtz, Chris Serrano-Ramirez, Alex Barchyn, Thomas E. Gao, Mozhou A Comparison of Multiple Odor Source Localization Algorithms |
title | A Comparison of Multiple Odor Source Localization Algorithms |
title_full | A Comparison of Multiple Odor Source Localization Algorithms |
title_fullStr | A Comparison of Multiple Odor Source Localization Algorithms |
title_full_unstemmed | A Comparison of Multiple Odor Source Localization Algorithms |
title_short | A Comparison of Multiple Odor Source Localization Algorithms |
title_sort | comparison of multiple odor source localization algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223208/ https://www.ncbi.nlm.nih.gov/pubmed/37430713 http://dx.doi.org/10.3390/s23104799 |
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