A Flying Odor Compass to Autonomously Locate the Gas Source

Luo, Bing; Meng, Qing-Hao; Wang, Jiaying; Zeng, Ming

doi:10.1109/tim.2017.2759378

Cited by 16 publications

(21 citation statements)

References 37 publications

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“…The ability to sense a gas from a UAV has numerous proposed applications, including detecting gas leaks (Luo, Meng, Wang, & Zeng, ; Smith, John, Christensen, & Chen, ; Smith, John, Stark, Christensen, & Chen, ), monitoring various sources of air pollution (Alvarado, Gonzalez, Erskine, Cliff, & Heuff, ; Aurell et al, ; Chang, Wang, Chang, Liang, & Lin, ; Villa, Salimi, Morton, Morawska, & Gonzalez, ), measuring important variables in greenhouses (Roldán, Joossen, Sanz, del Cerro, & Barrientos, ) and exploring sources of methane emissions (Greatwood et al, ; Neumann, Kohlhoff, Hüllmann, Lilienthal, & Kluge, ). The UAVs used in aerial gas sensing research include both fixed wing and rotary wing platforms (Alvarado, Gonzalez, Fletcher, & Doshi, ).…”

Section: Related Workmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

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Section: Related Workmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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“…This sensor arrangement is labeled as “proposal”: in this study. The other is to arrange the alcohol sensors upward at the center of the quadcopter for comparison with the reference [ 22 ]. When the sensors were placed at the center of the quadcopter, odor information was acquired from a wide omnidirectional range [ 22 ].…”

Section: Directivity Experiments Of Odor Detectionmentioning

confidence: 99%

“…However, the three-dimensional diffusion of chemicals causes CPT robots to lose information related to the chemicals, unless they can follow it in a three-dimensional manner. Thus, some researchers attempted to solve the CPT problem using three-dimensional movable unmanned aerial vehicles (UAVs) [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Our approach to fulfill this aim is to use the aero-olfactory effect of the quadcopter [ 26 ] in order to sense chemicals. This aero-olfactory effect is called a “wake” in the literature [ 22 ], so we use the word “wake” in this paper. The “wake” is a phenomenon in which a strong odor distribution occurs around the quadcopter due to the influence of the rotation of the propellers [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…This aero-olfactory effect is called a “wake” in the literature [ 22 ], so we use the word “wake” in this paper. The “wake” is a phenomenon in which a strong odor distribution occurs around the quadcopter due to the influence of the rotation of the propellers [ 22 ]. Also, we conduct two types of experiments to evaluate the basic performance of the sensing mechanism using wake.…”

Section: Introductionmentioning

confidence: 99%

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Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

Shigaki

Fikri

Kurabayashi

2018

Sensors

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In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem. Solving CPT represents an important technique in the field of engineering because it can be used to perform rescue operations at the time of a disaster and to identify sources of harmful substances. An appropriate intake of air when sensing odors plays an important role in performing CPT. Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors. By experimental evaluation, we verified that the quadcopter wake intake method has good directivity and can be used to realize CPT. Concretely, even at various odor source heights, the quadcopter had a three-dimensional CPT success rate of at least 70%. These results imply that, although a further development of three-dimensional CPT is necessary in order to conduct it in unknown and cluttered environments, the intake method proposed in this paper enables a pocket-sized quadcopter to perform three-dimensional CPT.

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Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A Flying Odor Compass to Autonomously Locate the Gas Source

Cited by 16 publications

References 37 publications

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

Recent Progress and Trend of Robot Odor Source Localization

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