Contaminant Cloud Boundary Monitoring Using UAV Sensor Swarms

White, Brian; Tsourdos, Antonios; Ashokaraj, I.; Subchan, S.; Żbikowski, Rafał

doi:10.2514/6.2007-6761

Cited by 16 publications

(15 citation statements)

References 6 publications

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“…Such tasks are of interest in many missions, including detection and monitoring of harmful algae blooms [1], tracking forest fires [2,3] or contaminant clouds [4], oil spills [5], automated exploration of sea temperature and salinity, hazardous weather conditions like hurricanes and tropical storms, tracking propagation of poisonous gases, etc.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Ovchinnikov

Semakova

Matveev

2015

Robotics and Autonomous Systems

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Section: Introductionmentioning

confidence: 99%

“…Since integral curves are disjoint, robot j will not cross the arc A i from 1). Hence r j (t) ∈ P + i (t) 4), (B.10)…”

mentioning

confidence: 98%

Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Ovchinnikov

Semakova

Matveev

2015

Robotics and Autonomous Systems

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“…As a result, researchers have attempted to use aircra to obtain high-quality air concentration measurements of contaminants. [6][7][8][9][10][11][12][13][14] For example, an aircra was used for monitoring nitrogen emissions from point sources. 9 A previous study also demonstrated a method of source estimation using remote aircra.…”

Section: Introductionmentioning

confidence: 99%

Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

et al. 2017

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“…Yang and Huang [13] utilized a sensory system based on an unmanned helicopter to monitor the SO 2 , NO, and CO in a chemical industry park. The UAV has also been used in the source term estimation and boundary tracking of atmospheric dispersion [14,15].…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation in Air Contaminant Dispersion Using a Particle Filter and Expectation-Maximization Algorithm

et al. 2017

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Abstract:The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm) are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV) observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.

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Contaminant Cloud Boundary Monitoring Using UAV Sensor Swarms

Cited by 16 publications

References 6 publications

Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Estimating contaminant source in chemical industry park using UAV-based monitoring platform, artificial neural network and atmospheric dispersion simulation

Data Assimilation in Air Contaminant Dispersion Using a Particle Filter and Expectation-Maximization Algorithm

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