An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot
The source localization of gas leaks is important to avoid any potential danger to the surroundings or the probable waste of resources. Currently there are several localization methods using robotic systems that try to find the origin of a gas plume. Many of these methods require wind velocity infor...
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| Format: | Article |
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MDPI AG
2018-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4375 |
| _version_ | 1818035246583513088 |
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| author | Jorge Edwin Sánchez-Sosa Juan Castillo-Mixcóatl Georgina Beltrán-Pérez Severino Muñoz-Aguirre |
| author_facet | Jorge Edwin Sánchez-Sosa Juan Castillo-Mixcóatl Georgina Beltrán-Pérez Severino Muñoz-Aguirre |
| author_sort | Jorge Edwin Sánchez-Sosa |
| collection | DOAJ |
| description | The source localization of gas leaks is important to avoid any potential danger to the surroundings or the probable waste of resources. Currently there are several localization methods using robotic systems that try to find the origin of a gas plume. Many of these methods require wind velocity information involving the use of commercial anemometric systems which are extremely expensive compared to metal oxide gas sensors. This article proposes the validation of the Gaussian plume model inside an empty room and its application to localize the source of a gas plume without employing anemometric sensors, exclusively using concentration data. The model was selected due to its simplicity and since it easily admits variants closer to reality, explaining the behavior of pollutants transported by the wind. An artificial gas source was generated by a conventional fan and liquid ethanol as contaminant. We found that the physical fan, far from making the model impossible to implement, enriched the information and added realism. The use of a robotic system capable of autonomously mapping the room concentration distribution is described. The results showed that the Gaussian plume model is applicable to localize our experimental gas source. An estimated position of the source with a deviation of 14 cm (6.1%) was obtained. |
| first_indexed | 2024-12-10T06:52:00Z |
| format | Article |
| id | doaj.art-c5db70c3f2644cb0809a3472b8b29fff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T06:52:00Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c5db70c3f2644cb0809a3472b8b29fff2022-12-22T01:58:32ZengMDPI AGSensors1424-82202018-12-011812437510.3390/s18124375s18124375An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile RobotJorge Edwin Sánchez-Sosa0Juan Castillo-Mixcóatl1Georgina Beltrán-Pérez2Severino Muñoz-Aguirre3Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, C.U., C.P. 72570 Puebla, MéxicoFacultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, C.U., C.P. 72570 Puebla, MéxicoFacultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, C.U., C.P. 72570 Puebla, MéxicoFacultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, C.U., C.P. 72570 Puebla, MéxicoThe source localization of gas leaks is important to avoid any potential danger to the surroundings or the probable waste of resources. Currently there are several localization methods using robotic systems that try to find the origin of a gas plume. Many of these methods require wind velocity information involving the use of commercial anemometric systems which are extremely expensive compared to metal oxide gas sensors. This article proposes the validation of the Gaussian plume model inside an empty room and its application to localize the source of a gas plume without employing anemometric sensors, exclusively using concentration data. The model was selected due to its simplicity and since it easily admits variants closer to reality, explaining the behavior of pollutants transported by the wind. An artificial gas source was generated by a conventional fan and liquid ethanol as contaminant. We found that the physical fan, far from making the model impossible to implement, enriched the information and added realism. The use of a robotic system capable of autonomously mapping the room concentration distribution is described. The results showed that the Gaussian plume model is applicable to localize our experimental gas source. An estimated position of the source with a deviation of 14 cm (6.1%) was obtained.https://www.mdpi.com/1424-8220/18/12/4375Gaussian plume modelgas source localizationconcentration distributionwind velocity distributionmetal-oxide semiconductor sensorrobotic system |
| spellingShingle | Jorge Edwin Sánchez-Sosa Juan Castillo-Mixcóatl Georgina Beltrán-Pérez Severino Muñoz-Aguirre An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot Sensors Gaussian plume model gas source localization concentration distribution wind velocity distribution metal-oxide semiconductor sensor robotic system |
| title | An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot |
| title_full | An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot |
| title_fullStr | An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot |
| title_full_unstemmed | An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot |
| title_short | An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot |
| title_sort | application of the gaussian plume model to localization of an indoor gas source with a mobile robot |
| topic | Gaussian plume model gas source localization concentration distribution wind velocity distribution metal-oxide semiconductor sensor robotic system |
| url | https://www.mdpi.com/1424-8220/18/12/4375 |
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