Wind Measurement and Estimation with Small Unmanned Aerial Systems (sUAS) Using On-Board Mini Ultrasonic Anemometers

Hollenbeck, Derek; Nunez, Gregorio; Christensen, L. E.; Chen, YangQuan

doi:10.1109/icuas.2018.8453418

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…For sUAS, lightweight and accurate wind sensors are needed to provide in situ measurements that can be used in the quantification methodology. Some examples of lightweight sensors can range from five hole probes (or multi-hole probes) to ultrasonic anemometers that utilize time of flight (e.g., Anemoment (Longmont, CO, USA) Trisonica used in [107] or the Gill (Lymington, Hampshire, UK) WindMaster used on the OP-TOKopter [108]) and resonance based (FT Technologies FT742 and FT205 used in [109]) to more custom micro electrical mechanical systems (MEMS)-based solutions (such as in [110]). These wind sensors can also be applied to general wind profiling and mapping applications.…”

Section: Sensors and Equipmentmentioning

confidence: 99%

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: Sensors and Equipmentmentioning

confidence: 99%

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Hollenbeck

Zulevic

Chen

2021

Drones

Self Cite

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“…Aerodynamic disturbances: Accurate wind or airflow sensing is at the heart of the techniques employed for aerodynamic disturbance estimation. A common strategy is based on directly measuring the airflow surrounding the robot via sensors, such as pressure sensors [18], ultrasonic sensors [19], or whisker-like sensors [20]. Other strategies estimate the airflow via its inertial effects on the robot, for example using model-based approaches [21], [22], learningbased approaches [23], [24], or hybrid (model-based and learning-based) solutions [25].…”

Section: Related Workmentioning

confidence: 99%

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Tagliabue

Paris

Kim

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman filter (UKF) to simultaneously estimate the threedimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The modelbased approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learningbased strategy uses a Long Short-Term Memory (LSTM) to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.

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“…Lately, several small-sized sonic anemometer have become commercially available (e.g. Decagon Devices DS-2, Anemoment TriSonica, FT Technologies FT205), which have subsequently been successfully applied to UAVs (Palomaki et al, 2017;Donnell et al, 2018;Nolan et al, 2018;Hollenbeck et al, 2018;Natalie and Jacob, 2019;Adkins et al, 2020). Palomaki et al (2017) used a small 2D sensor (Decagon Devices DS-2) on a DJI Flame Wheel F550 hexrotor.…”

Section: Uav-based Wind Speed Measurementsmentioning

confidence: 99%

Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer

Thielicke¹,

Hübert²,

Müller³

2020

Preprint

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Abstract. Wind data collection in the atmospheric boundary layer benefits from short term wind speed measurements using unmanned aerial vehicles. Fixed and rotary wing devices with diverse anemometer technology have been used in the past to provide such data, but the accuracy still has the potential to be increased. We developed a light weight drone (weight including sensor 45 min) for carrying an industry standard precision sonic anemometer. Accuracy tests have been performed with the isolated anemometer at high tilt angles in a calibration wind tunnel, with the drone flying in a large wind tunnel, and with the full system flying at different heights next to a bistatic lidar reference. The propeller-induced flow deflects the air to some extent, but this effect is compensated effectively. Our data fusion shows no signs of crosstalk between ground speed and wind speed. When compared with the bistatic lidar in very turbulent conditions, with 10 seconds averaging interval and with the UAV constantly circling around the measurement volume of the lidar reference, wind speed measurements have an average absolute bias of 1.9 % (0.073 m s−1), wind elevation average absolute bias is 0.5°, and wind azimuth average absolute bias is 1.5°, indicating excellent accuracy under challenging and dynamic conditions. The system was finally flown in the wake of a wind turbine, successfully measuring the spatial velocity deficit distribution during forward flight, yielding results that are in very close agreement to lidar measurements and the theoretical distribution. We believe that the results presented in this paper can provide important information for designing flying systems for precise air speed measurements either for short duration at multiple locations (battery powered) or for long duration at a single location (power supplied via cable). UAVs that are able to accurately measure three-dimensional wind might be used as cost effective and flexible addition to measurement masts and lidar scans.

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Wind Measurement and Estimation with Small Unmanned Aerial Systems (sUAS) Using On-Board Mini Ultrasonic Anemometers

Cited by 18 publications

References 12 publications

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer

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