Joint estimation of gas and wind maps for fast-response applications

Gongora, Andres; Monroy, Javier; González-Jiménez, Javier

doi:10.1016/j.apm.2020.06.026

Cited by 26 publications

(27 citation statements)

References 22 publications

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“…Leveraging gas dispersion modeling in path planning with source estimation approaches. A recent paper in [193] showed a joint estimation method (wind and gas) that performed fairly well compared to existing methods at reconstructing plumes within enclosed structures.…”

Section: Future Directionsmentioning

confidence: 97%

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Hollenbeck

Zulevic

Chen

2021

Drones

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Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

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Section: Future Directionsmentioning

confidence: 97%

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Hollenbeck

Zulevic

Chen

2021

Drones

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“…The number of parameters in these models is not fixed and therefore less assumptions are made about the gas distributions. To account for the transportation of particles, GMRF and Kernel DM have further additions to account for wind direction in GW-GMRF (Gongora et al, 2020) and Kernel DM+V/W (Asadi et al, 2017). This leads to the ability to account for multi modal distributions and the inclusion of obstacles in the environment.…”

Section: Estimationmentioning

confidence: 99%

Autonomous search of an airborne release in urban environments using informed tree planning

et al. 2022

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The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

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“…Mobile robot olfaction (MRO) is the field concerned with the integration and application of the sense of smell into mobile robots. It is a widely multidisciplinary research area, involving problems such as chemical sensing and classification [4,15], dispersion modeling [5,7], optimal sampling [8] or gas source localization [10,17], among others.…”

Section: Introductionmentioning

confidence: 99%

GadenTools: A Toolkit for Testing and Simulating Robotic Olfaction Tasks with Jupyter Notebook Support

Ojeda

Ruiz-Sarmiento

Monroy

et al. 2022

ROBOT2022: Fifth Iberian Robotics Conference

Self Cite

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This work presents GadenTools, a toolkit designed to ease the development and integration of mobile robotic olfaction applications by enabling a convenient and user-friendly access to Gaden's realistic gas dispersion simulations. It is based on an easy-to-use Python API, and includes an extensive tutorial developed with Jupyter Notebook and Google Colab technologies. A detailed set of examples illustrates aspects ranging from basic access to sensory data or the generation of groundtruth images, to the more advanced implementation of plume tracking algorithms, all in an online web-editor with no installation requirements. All the resources, including the source code, are made available in an online open repository.

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Joint estimation of gas and wind maps for fast-response applications

Cited by 26 publications

References 22 publications

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Autonomous search of an airborne release in urban environments using informed tree planning

GadenTools: A Toolkit for Testing and Simulating Robotic Olfaction Tasks with Jupyter Notebook Support

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