Small Unmanned Aircraft Systems Research Articles

Human–Machine Interface Design for Monitoring Safety Risks Associated with Operating Small Unmanned Aircraft Systems in Urban Areas

The envisioned introduction of autonomous Small Unmanned Aircraft Systems (sUAS) into low-altitude urban airspace necessitates high levels of system safety. Despite increased system autonomy, humans will most likely remain an essential component in assuring safety. This paper derives, applies, and evaluates a display design concept that aims to support safety risk monitoring of multiple sUAS by a human operator. The concept comprises of five design principles. The core idea of the concept is to limit display complexity despite increasing the number of sUAS monitored by primarily visualizing highly abstracted information while hiding detailed information of lower abstraction, unless specifically requested by the human operator. States of highly abstracted functions are visualized by function-specific icons that change hue in accordance to specified system states. Simultaneously, the design concept aims to support the human operator in identifying off-nominal situations by implementing design properties that guide visual attention. The display was evaluated in a study with seven subject matter experts. Although preliminary, the results clearly favor the proposed display design concept. The advantages of the proposed design concept are demonstrated, and the next steps for further exploring the proposed display design concept are outlined.

Improving Remotely Sensed River Bathymetry by Image‐Averaging

AbstractBasic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.

Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE)

Abstract. Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning

The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic.

Development of a small unmanned aircraft system to derive CO<sub>2</sub> emissions of anthropogenic point sources

Abstract. A reduction of the anthropogenic emissions of CO2 (carbon dioxide) is necessary to stop or slow down man-made climate change. To verify mitigation strategies, a global monitoring system such as the envisaged European Copernicus anthropogenic CO2 monitoring mission (CO2M) is required. Those satellite data are going to be complemented and validated with airborne measurements. Unmanned aerial vehicle (UAV)-based measurements can provide a cost-effective way to contribute to these activities. Here, we present the development of an sUAS (small unmanned aircraft system) to quantify the CO2 emissions of a nearby point source from its downwind mass flux without the need for any ancillary data. Specifically, CO2 is measured by an NDIR (non-dispersive infrared) detector, and the wind speed and direction are measured with a 2-D ultrasonic acoustic resonance anemometer. By means of laboratory measurements and an in-flight validation at the ICOS (Integrated Carbon Observation System) atmospheric station Steinkimmen (STE) near Bremen, Germany, we estimate that the individual CO2 measurements have a precision of 3 ppm and that CO2 enhancements can be determined with an accuracy of 1.3 % or 0.9 ppm, whichever is larger. We introduce an anemometer calibration method to minimize the effect of rotor downwash on the wind measurements. This method derives the fit parameters of a linear calibration model accounting for scaling, rotation, and a potential constant bias. For this purpose, it analyzes wind measurements taken while following a suitable flight pattern and assuming stationary wind conditions. From the calibration and validation experiments, we estimate the single measurement precision of the horizontal wind speed to be 0.40 m s−1 and the accuracy to be 0.33 m s−1. By means of two flights downwind of the ExxonMobil natural gas processing facility in Großenkneten about 40 km west of Bremen, Germany, we demonstrate how the measurements of elevated CO2 concentrations can be used to infer mass fluxes of atmospheric CO2 related to the emissions of the facility.

Improving Productivity of Low-Altitude Airspace: Speed vs Direction Control

Emerging unmanned aircraft systems (UAS) operations propose to introduce flights orders of magnitude higher than what the airspace handles today. What type of onboard control for enroute separation assurance increases productivity without compromising safety in the low-altitude airspace? This article quantifies the impact of two conflict detection and resolution approaches - pure speed control and pure direction control - on airspace productivity. We focus on small UAS traffic for goods movement in low altitude (under 400 feet) airspace. We compute three types of metrics, namely throughput, travel time extension and energy consumption, from simulations of unmanned traffic over a representative square area. As the inflow rates are increased, the corresponding outflow rates (throughput), travel time extensions and energy consumption for each type of control are measured. A representative power consumption model is developed for aircraft with both kinds of control. Without compromising safety, both direction control and speed control are equally good, unless the UAS are designed to be highly efficient while hovering. If the UAS have a hover power consumption that is equal to or less than one and a half times the minimum power consumption, speed control increases airspace productivity more than direction control.

SUAS Remote Sensing for Closed-canopy Tree Inventory on Earthen Dams

More than half of the U.S. dams are privately owned and experienced the overgrowth of trees. There is a need to improve dam inspection and maintenance in a timely manner. Small Unmanned Aircraft Systems (sUAS) have been increasingly utilized for near-surface landscape mapping and reconnaissance. This study tests an sUAS protocol of closed-canopy tree survey on earthen dams. A DJI Matrice 100 flight was launched on September 22, 2020. The orthoimage and 3D point cloud are extracted, and the canopy height model is built. Treetops and crowns are delineated using an integrated watershed segmentation and image segmentation procedure. The results include a tree survey inventory that contains the locations, tree heights and crowns of 284 trees growing on the downslope of the dam. Given the flight flexibility and fine 3D details acquired from inexpensive drones, sUAS has a high potential for assessing tree overgrowth toward remediation solutions of earthen dams.

Viability and Application of Mounting Personal PID VOC Sensors to Small Unmanned Aircraft Systems

Using a UAS-mounted sensor to allow for a rapid response to areas in which it may be difficult to reach or may not be safe for humans to encounter can increase the situational awareness of first responders of an aircraft crash site through the remote detection, identification, and quantification of airborne hazardous materials. The primary purpose of this research is to evaluate the innovative remote sensing viability and application of integrating existing commercial-off-the-shelf (COTS) sensors with small unmanned aircraft system (UAS) technology to detect potentially hazardous airborne contaminants at optimum range resolutions using UAS platforms.

Ultra-Wideband Patch Antenna Array With an Inclined Proximity Coupled Feed for Small Unmanned Aircraft RADAR Applications

This paper presents the design of a lightweight and compact patch antenna array with an inclined proximity coupled feed. The antenna array is designed for operation on a small unmanned aircraft system (sUAS) with an ultra-wideband (UWB) radar for remote sensing of soil moisture and snow. To achieve UWB impedance matching, the design utilizes an inclined wide feed and inter-element coupling. A 3-D printed base (polylactic acid) is used to support the feed structure. The antenna array operates over a 2.5 GHz -4.5 GHz frequency (57.2% bandwidth) range, and its weight is 112 grams. A prototype 2 × 4 element array is fabricated and tested. The antenna array has an 88.2% average radiation efficiency and a 13.2 dBi average realized gain over the desired frequency band with broadside and stable radiation pattern.

Small Unmanned Aircraft Systems Acoustic Analysis for Noninvasive Marine Mammal Response: An Exploratory Field Study

As in countless other fields of human endeavor, small unmanned aircraft systems (sUAS) have the potential to benefit pinniped (Pinnipedia; e.g., Phocidae [seals], Otariidae [sea lions], and Odobenidae [walruses]) response efforts. The employment of sUAS could give responders a close-up look at animals in distress in order to determine their condition as well as develop a response strategy. However, unlike other subjects that are regularly inspected by sUAS (e.g., croplands and civil infrastructure) pinnipeds may respond to the distinctive sound generated by small, multirotor sUAS. This reaction may include retreating into the water en masse, which could put the target individual out of reach of the response team. To potentially prevent this outcome, this exploratory field study established sUAS acoustic profiles through quantitative and qualitative measures for multiple aircraft across a range of distance and altitude. These data were collected in both a secluded rural environment and a coastal environment. The results indicate that sUAS sound pressure levels at least 20 dBA (re 20 µPa) below the ambient noise floor are required to completely mask the distinctive sound of the aircraft to human hearing. The results were used to create aircraft operational envelopes to potentially mitigate disturbance while optimizing visual information. To reflect the type of sUAS that would likely be available to small, non-profit marine mammal response groups working in remote locations, the aircraft studied were limited to compact models $3,000 or less.

Small UAS and Delivery Drones: Challenges and Opportunities The 38th Alexander A. Nikolsky Honorary Lecture

During the past two decades, there has been major growth of small-unmanned aerial vehicles (sUAV) for hobbyists and rapidly expanding commercial and military applications. The impetus for this dramatic expansion has been due to the explosion of mobile technology in terms of microelectronics, data processing and transmission capability, high-energy-density batteries, miniaturized integrated programmable chips, and innovations in computer vision and videography/photography. However, there are many challenges to overcome before these small unmanned aerial systems (sUAS) can be used for routine commercial and military applications, which include reliability, sizable payload and range, stringent navigation/guidance requirements, and precision takeoff/landing and robust autonomous flight in constrained and low-altitude gusty environment. The objective of this paper is to cover state-of-the-art of sUAS and delivery drones, identify technology gaps and key scientific barriers, and present future research needs for high payoff applications.

Fast Collision Detection for Small Unmanned Aircraft Systems in Urban Airspace

With the dramatic development of small unmanned aircraft systems (sUAS), how to ensure sUAS safety operation has been a growing concern. This article proposes a fast probabilistic collision detection method for sUAS based on probability density function approximations. Firstly, cylindrical collision zones for sUAS and obstacles are established by geometrical methods for simplifying collision modeling, and instantaneous collision probability for sUAS is expressed by a triple integral. Secondly, a rapid estimation algorithm is derived for instantaneous collision probability, and then the predicted collision probability in probabilistic collision detection can be obtained by the maximum of instantaneous collision probabilities during the encounter. Randomized tests indicate that the average computation time of the proposed algorithm is less than 0.001s, and the Mean Absolute Error (MAE) is less than 0.01 and the Root Mean Squared Error (RMSE) is less than 0.02. Finally, numerical simulations are carried out to analyze the influence of parameters, including crossing angle, predicted separation at the closest point of approach (CPA), and predicted time to CPA, on collision probabilities. The optimal detection time for collision detection is also discussed in the different types of encounters. The collision detection method proposed in this article can provide support for real-time collision avoidance and the definition of dynamic safety bounds for sUAS.

Detectability of Clothing Color by Small Unmanned Aircraft Systems for Search and Rescue Operations

Detectability of Clothing Color by Small Unmanned Aircraft Systems for Search and Rescue Operations

Pilot Visual Detection of Small Unmanned Aircraft on Final Approach during Nighttime Conditions

In December 2020, the Federal Aviation Administration (FAA) announced the release of a new final rule, permitting operators of small unmanned aircraft systems (sUAS) to perform routine night operations. Public comments to the Notice of Proposed Rulemaking indicated potential safety concerns regarding a pilot’s ability to spot a low-altitude sUAS during nighttime conditions. Leveraging data from the FAA’s UAS Sighting Report Database, the research team evaluated the significance of aircraft encounters with UAS at night. Researchers conducted an inflight experiment in which 10 pilots performed an instrument approach to airport during nighttime conditions in which a multi-rotor sUAS presented a potential collision hazard. The sUAS was equipped with lighting visible for 3 miles with a sufficient flash rate to avoid a collision, as specified by the new regulation. Participants performed five approaches, with the sUAS flying different scripted encounter profiles. Participants were asked to indicate when they visually spotted the sUAS, with sighting data recorded via an onboard observer. Geolocation information from both the aircraft and sUAS were compared at the time of each reported sighting to assess visibility distance and orientation. The sUAS was successfully spotted during 30 percent (n = 12) of the testing passes. Hovering sUAS were spotted at the same rate as moving sUAS, however, sUAS in motion were spotted at a much greater range. Researchers noted disproportionately higher spotting rates occurred when the sUAS was oriented on the starboard side of the aircraft vs. the port side. It is believed that airport lighting systems may have obscured or otherwise camouflaged portside sUAS encounters. When asked to estimate distance to an encountered sUAS, most participants underestimated, perceiving the sUAS to be much closer than reality. Additionally, the researchers assessed the potential for the participants to initiate evasive maneuvers, based on the distance and closure rate of the aircraft and sUAS at the time of sighting. Based on the FAA’s Aircraft Identification and Reaction Time Chart, collision avoidance would only have been successful during 15 percent of encounters (n = 6). The research team recommends Remote Pilots employ vigilant traffic awareness during nighttime operations, and leverage use of ADS-B (In) technology and monitor Common Traffic Advisory Frequencies to maintain situational awareness—particularly when operating in proximity to airports.

Perspective: Agricultural Aerial Application with Unmanned Aircraft Systems: Current Regulatory Framework and Analysis of Operators in the United States

HighlightsThe FAA has used two exemptions (17261 and 18009) as precedents for approval of numerous agricultural operations for unmanned aircraft systems (UAS).While many operators have received exemptions, a significant portion have not received an agricultural aircraft operator certificate (AAOC), despite the need for both to operate UAS in agricultural operations.Operators who have both an exemption and an AAOC tend to be clustered in geographic areas, with many states without a single such operator.Abstract. Unmanned aircraft systems (UAS) have seen rapid growth in many industries in the U.S. since the introduction of small UAS regulations (14 CFR § 107). However, adoption of UAS for agricultural aerial application has been limited. Two landmark regulatory exemptions by the Federal Aviation Administration (FAA) have laid the foundation for commercial agricultural aerial application with UAS. Since the initial introduction of these exemptions, the pace of new exemptions for agricultural aerial application with UAS has remained steady. By the end of 2019, 64 operators had received exemptions in which the FAA cited one of the two landmark exemptions as a precedent. This study analyzed these exemptions to determine geographic distribution, aircraft manufacturer, number of employees, and time to operator certification. Results indicate that less than half of operators who received an exemption from the FAA became certified for aerial application. Additionally, certified operators were not evenly distributed throughout the U.S. despite the broader distribution of exemption holders. Two UAS manufacturers dominated the market, with over 80% of exemptions requesting UAS from one or both manufacturers. While regulatory hurdles for agricultural aerial application with UAS have been substantially reduced through the introduction of standardized exemptions, this has not resulted in the anticipated influx of certified operators. There are additional impediments preventing operator certification, including technological limitations of currently available UAS, which need to be addressed to improve the rate of UAS integration into agricultural aerial application. Keywords: Chemical applications, Drone, Precision agriculture, UAS, UAV, Unmanned aerial vehicle, Unmanned aircraft systems.

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Semi-parametric Regression based on Machine Learning Methods for UAS Stall Identification

Abstract A semi-parametric regression methodology is formulated to identify the unsteady lift characteristics of a small UAS undergoing dynamic stall. Based on the trailing edge separation model of Leishmann and Beddoes, the nonlinear evolution of the separation point is formulated so that it can be estimated by non-parametric Machine Learning methods. Validation of the methodology is presented with the identification of the lift coefficient based on quasi-steady wind tunnel tests.

Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery

Wildfires burn 4–10 million acres annually across the United States and wildland fire related damages and suppression costs have exceeded $13 billion for a single year. High-intensity wildfires contribute to post-fire erosion, degraded wildlife habitat, and loss of timber resources. Accurate and temporally adequate assessment of the effects of wildland fire on the environment is critical to improving the of wildland fire as a tool for restoring ecosystem resilience. Sensor miniaturization and small unmanned aircraft systems (sUAS) provide affordable, on-demand monitoring of wildland fire effects at a much finer spatial resolution than is possible with satellite imagery. The use of sUAS would allow researchers to obtain data with more detail at a much lower initial cost. Unfortunately, current regulatory and technical constraints prohibit the acquisition of imagery using sUAS for the entire extent of large fires. This research examined the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images. Despite the lower resolution of the satellite image, the research utilized the advantages of satellite imagery such as global coverage, low cost, temporal stability, and spectral extent while leveraging the higher resolution of hyperspatial sUAS imagery for training and validating the mapping analytics.

Integration of Smartphones Into Small Unmanned Aircraft Systems to Sense Water in Soil by Using Reflected GPS Signals

We investigate the feasibility of using built-in GNSS sensors within ubiquitous smartphone devices from a small UAS for the purpose of land remote sensing. We summarize the experimental findings and challenges that need to be resolved in order to perform the GNSS reflectometry (GNSS-R) technique via smartphones. In late 2018, a series of experiments were conducted and designed by integrating two smartphones into a multicopter UAS by attaching them to ground plates to isolate and record both direct and reflected GNSS carrier-to-noise density ratio ( $C/N_0$ ) separately. It was demonstrated that, first, fluctuations of moving GNSS specular reflections are correlated with spatial ground features with appreciable dynamic range and second, radiation pattern of the smartphone's inbuilt antenna has a significant effect on the received signal strength. In 2020, more experiments were conducted to examine the quality of in-built chip and antenna of a smartphone with regard to the GNSS-R approach as well as the consistency of measurements. These follow-up experiments involved, first, placement of the smartphone on a pan-tilt mechanism on a tripod, second, formation flights with smartphone on a gimbal and a high-quality custom-built dual-channel GNSS-R receiver, and, third, flying the UAS at different times of the day on two consecutive days. It was demonstrated that, first, the radiation pattern of the smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and, second, internal GNSS chip produces observables of sufficient quality, and, third, the fluctuations of the reflected signal are repeatable under the same configuration at different times.