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Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subseq...
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/PMC10305319/ https://www.ncbi.nlm.nih.gov/pubmed/37420554 http://dx.doi.org/10.3390/s23125387 |
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author | Gongora, Andres Monroy, Javier Rahbar, Faezeh Ercolani, Chiara Gonzalez-Jimenez, Javier Martinoli, Alcherio |
author_facet | Gongora, Andres Monroy, Javier Rahbar, Faezeh Ercolani, Chiara Gonzalez-Jimenez, Javier Martinoli, Alcherio |
author_sort | Gongora, Andres |
collection | PubMed |
description | The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel. |
format | Online Article Text |
id | pubmed-10305319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103053192023-06-29 Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots Gongora, Andres Monroy, Javier Rahbar, Faezeh Ercolani, Chiara Gonzalez-Jimenez, Javier Martinoli, Alcherio Sensors (Basel) Article The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel. MDPI 2023-06-07 /pmc/articles/PMC10305319/ /pubmed/37420554 http://dx.doi.org/10.3390/s23125387 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 Gongora, Andres Monroy, Javier Rahbar, Faezeh Ercolani, Chiara Gonzalez-Jimenez, Javier Martinoli, Alcherio Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title | Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title_full | Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title_fullStr | Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title_full_unstemmed | Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title_short | Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots |
title_sort | information-driven gas distribution mapping for autonomous mobile robots |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305319/ https://www.ncbi.nlm.nih.gov/pubmed/37420554 http://dx.doi.org/10.3390/s23125387 |
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