ArticleSensors (Basel, Switzerland)2018
An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Morphology-Guided Conditional Generative Adversarial Network for Rapid Prediction of Hazard Gas Dispersion Field in Complex Urban Environments.Sensors (Basel, Switzerland) · 2026Article
- Sensor-Driven Localization of Airborne Contaminant Sources via the Sandpile-Advection Model and (1 + 1)-Evolution Strategy.Sensors (Basel, Switzerland) · 2025Article
- Leak Detection and Localization in Multi-Grid Space Using Improved Gaussian Plume Model.Sensors (Basel, Switzerland) · 2023Article
- Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-COSensors (Basel, Switzerland) · 2020Article
- Discrimination Improvement of a Gas Sensors' Array Using High-Frequency Quartz Crystal Microbalance Coated with Polymeric Films.Sensors (Basel, Switzerland) · 2020Article
- Effective Exploration Behavior for Chemical-Sensing Robots.Biomimetics (Basel, Switzerland) · 2019Article
- Application of an Array of Metal-Oxide Semiconductor Gas Sensors in an Assistant Personal Robot for Early Gas Leak Detection.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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