Search

This paper proposes a digital twin (DT) framework for point source applications in environmental sensing (ES). The DT concept has become quite popular among process and manufacturing industries for improving performance and estimating remaining useful life (RUL). However, environmental behavior, such as in gas dispersion, is ever changing and hard to model in real-time. The DT framework is applied to the point source environmental monitoring problem, through the use of hybrid modeling and optimization techniques. A controlled release case study is overviewed to illustrate our proposed DT framework and several spatial interpolation techniques are explored for source estimation. Future research efforts and directions are discussed. [ABSTRACT FROM AUTHOR]

Even as human–robot interactions become increasingly common, conventional small Unmanned Aircraft Systems (sUAS), typically multicopters, can still be unsafe for deployment in an indoor environment in close proximity to humans without significant safety precautions. This is due to their fast-spinning propellers, and lack of a fail-safe mechanism in the event of a loss of power. A blimp, a non-rigid airship filled with lighter-than-air gases is inherently safer as it ’floats’ in the air and is generally incapable of high-speed motion. The Spherical Indoor Coandă Effect Drone (SpICED), is a novel, safe spherical blimp design propelled by closed impellers utilizing the Coandă effect. Unlike a multicopter or conventional propeller blimp, the closed impellers reduce safety risks to the surrounding people and objects, allowing for SpICED to be operated in close proximity with humans and opening up the possibility of novel human–drone interactions. The design implements multiple closed-impeller rotors as propulsion units to accelerate airflow along the the surface of the spherical blimp and produce thrust by utilising the Coandă effect. A cube configuration with eight uni-directional propulsion units is presented, together with the closed-loop Proportional–Integral–Derivative (PID) controllers, and custom control mixing algorithm for position and attitude control in all three axes. A physical prototype of the propulsion unit and blimp sUAS was constructed to experimentally validate the dynamic behavior and controls in a motion-captured environment, with the experimental results compared to the side-tetra configuration with four bi-directional propulsion units as presented in our previously published conference paper. An up to 40% reduction in trajectory control error was observed in the new cube configuration, which is also capable of motion control in all six Degrees of Freedom (DoF) with additional pitch and roll control when compared to the side-tetra configuration.

A recent rash of near mid-air collisions coupled with the widespread proliferation of small unmanned aircraft systems (sUAS) raise concerns that integration is posing additional risk to the National Airspace System. In 2016, sUAS sighting reports by manned aircraft pilots averaged 147 per month. In the first three quarters of 2017, sUAS sightings jumped to 188 per month. The purpose of this study was to evaluate sUAS operator behavior to determine potential interference with aviation operations. While previous research has indeed yielded findings about operator behavior, such studies were generally based on data derived from Aviation Safety Reporting System filings or the UAS sighting report database maintained by the Federal Aviation Administration. In this study, the authors partnered with a UAS technology company to deploy an AeroScope, a passive radiofrequency detection device, to detect UAS flight activity in an urban area. While the device was limited to collecting flight information from only DJImanufactured platforms, it is estimated that the company holds a market share in excess of 70% providing a reasonable barometer for sUAS activity in the sample area. Over the 19-day sample period, the AeroScope device recorded 258 detections of 77 unique sUAS platforms. The authors assessed sUAS operator behavioral characteristics, including: UAS models, operating altitudes, preferred flying days and times, flight durations, and operating locations. The authors assessed 93 potential violations of 14 CFR 107 regulations, including controlled airspace breaches, exceeding maximum flight altitudes, and flight outside of daylight or civil twilight hours. The authors concluded that UAS activity in the sample area posed potential conflicts with a runway visual approach, created a collision hazard with three heliports, and heightened risk for visual flight rules operations underneath a controlled airspace shelf. The authors determined existing sUAS geofencing systems were ineffective at deterring sUAS activity unless they imposed flight restrictions in addition to hazard notification. [ABSTRACT FROM AUTHOR]

A recent rash of near mid-air collisions coupled with the widespread proliferation of small unmanned aircraft systems (sUAS) raise concerns that integration is posing additional risk to the National Airspace System. In 2016, sUAS sighting reports by manned aircraft pilots averaged 147 per month. In the first three quarters of 2017, sUAS sightings jumped to 188 per month. The purpose of this study was to evaluate sUAS operator behavior to determine potential interference with aviation operations. While previous research has indeed yielded findings about operator behavior, such studies were generally based on data derived from Aviation Safety Reporting System filings or the UAS sighting report database maintained by the Federal Aviation Administration. In this study, the authors partnered with a UAS technology company to deploy an AeroScope, a passive radiofrequency detection device, to detect UAS flight activity in an urban area. While the device was limited to collecting flight information from only DJImanufactured platforms, it is estimated that the company holds a market share in excess of 70% providing a reasonable barometer for sUAS activity in the sample area. Over the 19-day sample period, the AeroScope device recorded 258 detections of 77 unique sUAS platforms. The authors assessed sUAS operator behavioral characteristics, including: UAS models, operating altitudes, preferred flying days and times, flight durations, and operating locations. The authors assessed 93 potential violations of 14 CFR 107 regulations, including controlled airspace breaches, exceeding maximum flight altitudes, and flight outside of daylight or civil twilight hours. The authors concluded that UAS activity in the sample area posed potential conflicts with a runway visual approach, created a collision hazard with three heliports, and heightened risk for visual flight rules operations underneath a controlled airspace shelf. The authors determined existing sUAS geofencing systems were ineffective at deterring sUAS activity unless they imposed flight restrictions in addition to hazard notification. [ABSTRACT FROM AUTHOR]

Floods affected approximately two billion people around the world from 1998–2017, causing over 142,000 fatalities and over 656 billion U.S. dollars in economic losses. Flood data, such as the extent of inundation and peak flood stage, are needed to define the environmental, economic, and social impacts of significant flood events. Ground-based global positioning system (GPS) surveys of post-flood high-water marks (HWMs) and topography are commonly used to define flood inundation and stage, but can be time-consuming, difficult, and expensive to conduct. Here, we demonstrate and test the use of small unmanned aircraft systems (sUAS) and close-range remote sensing techniques to collect high-accuracy flood data to define peak flood stage elevations and river cross-sections. We evaluate the elevation accuracy of the HWMs from sUAS surveys by comparison with traditional GPS surveys, which have acceptable accuracy for many post-flood assessments, at two flood sites on two small streams in the U.S. Mean elevation errors for the sUAS surveys were 0.07 m and 0.14 m for the semiarid and temperate sites, respectively; those values are similar to typical errors when measuring HWM elevations with GPS surveys. Results demonstrate that sUAS surveys of HWMs and cross-sections can be an accurate and efficient alternative to GPS surveys; we provide insights that can be used to decide whether sUAS or GPS techniques will be most efficient for post-flood surveying.

Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3−8 m in width and 50−300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain.

One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly.

The use of small unmanned aircraft systems (sUAS) to acquire very high-resolution multispectral imagery has attracted growing attention recently; however, no systematic, feasible, and convenient radiometric calibration method has been specifically developed for sUAS remote sensing. In this research, we used a modified color infrared (CIR) digital single-lens reflex (DSLR) camera as the sensor and the DJI S800 hexacopter sUAS as the platform to collect imagery. Results show that the relationship between the natural logarithm of measured surface reflectance and image raw, unprocessed digital numbers (DNs) is linear and the \bmy-intercept of the linear equation can be theoretically interpreted as the minimal possible surface reflectance that can be detected by each sensor waveband. The empirical line calibration equation for every single band image can be built using the \bmy-intercept as one data point, and the natural log-transformed measured reflectance and image DNs of a gray calibration target as another point in the coordinate system. Image raw DNs are therefore converted to reflectance using the calibration equation. The Mann–Whitney \bmU test results suggest that the difference between the measured and the predicted reflectance values of 13 tallgrass sampling quadrats is not statistically significant. The method theory developed in this study can be employed for other sUAS-based remote sensing applications. [ABSTRACT FROM PUBLISHER]

Floods affected approximately two billion people around the world from 1998–2017, causing over 142,000 fatalities and over 656 billion U.S. dollars in economic losses. Flood data, such as the extent of inundation and peak flood stage, are needed to define the environmental, economic, and social impacts of significant flood events. Ground-based global positioning system (GPS) surveys of post-flood high-water marks (HWMs) and topography are commonly used to define flood inundation and stage, but can be time-consuming, difficult, and expensive to conduct. Here, we demonstrate and test the use of small unmanned aircraft systems (sUAS) and close-range remote sensing techniques to collect high-accuracy flood data to define peak flood stage elevations and river cross-sections. We evaluate the elevation accuracy of the HWMs from sUAS surveys by comparison with traditional GPS surveys, which have acceptable accuracy for many post-flood assessments, at two flood sites on two small streams in the U.S. Mean elevation errors for the sUAS surveys were 0.07 m and 0.14 m for the semiarid and temperate sites, respectively; those values are similar to typical errors when measuring HWM elevations with GPS surveys. Results demonstrate that sUAS surveys of HWMs and cross-sections can be an accurate and efficient alternative to GPS surveys; we provide insights that can be used to decide whether sUAS or GPS techniques will be most efficient for post-flood surveying. [ABSTRACT FROM AUTHOR]