Small Unmanned Aircraft Systems Research Articles

Small Unmanned Aircraft System Operator Compliance with Visual Line of Sight Requirements

Small Unmanned Aircraft System Operator Compliance with Visual Line of Sight Requirements

On the Use of Rotary-Wing Aircraft to Sample Near-Surface Thermodynamic Fields: Results from Recent Field Campaigns.

Rotary-wing small unmanned aircraft systems (sUAS) are increasingly being used for sampling thermodynamic and chemical properties of the Earth’s atmospheric boundary layer (ABL) because of their ability to measure at high spatial and temporal resolutions. Therefore, they have the potential to be used for long-term quasi-continuous monitoring of the ABL, which is critical for improving ABL parameterizations and improving numerical weather prediction (NWP) models through data assimilation. Before rotary-wing aircraft can be used for these purposes, however, their performance and the sensors used therein must be adequately characterized. In the present study, we describe recent calibration and validation procedures for thermodynamic sensors used on two rotary-wing aircraft: A DJI S-1000 and MD4-1000. These evaluations indicated a high level of confidence in the on-board measurements. We then used these measurements to characterize the spatiotemporal variability of near-surface (up to 300-m AGL) temperature and moisture fields as a component of two recent field campaigns: The Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast U.S. (VORTEX-SE) in Alabama, and the Land Atmosphere Feedback Experiment (LAFE) in northern Oklahoma.

Preliminary Design of an Unmanned Aircraft System for Aircraft General Visual Inspection

Among non-destructive inspection (NDI) techniques, General Visual Inspection (GVI), global or zonal, is the most widely used, being quick and relatively less expensive. In the aeronautic industry, GVI is a basic procedure for monitoring aircraft performance and ensuring safety and serviceability, and over 80% of the inspections on large transport category aircrafts are based on visual testing, both directly and remotely, either unaided or aided via mirrors, lenses, endoscopes or optic fiber devices coupled to cameras. This paper develops the idea of a global and/or zonal GVI procedure implemented by means of an autonomous unmanned aircraft system (UAS), equipped with a low-cost, high-definition (HD) camera for carrying out damage detection of panels, and a series of distance and trajectory sensors for obstacle avoidance and inspection path planning. An ultrasonic distance keeper system (UDKS), useful to guarantee a fixed distance between the UAS and the aircraft, was developed, and several ultrasonic sensors (HC-SR-04) together with an HD camera and a microcontroller were installed on the selected platform, a small commercial quad-rotor (micro-UAV). The overall system concept design and some laboratory experimental tests are presented to show the effectiveness of entrusting aircraft inspection procedures to a small UAS and a PC-based ground station for data collection and processing.

Detection and mitigation of actuator attacks on small unmanned aircraft systems

Unmanned aircraft systems (UAS) are susceptible to malicious attacks originated by intelligent adversaries, and the actuators constitute one of the critical attack surfaces. In this paper, the problem of detecting and mitigating attacks on the actuators of a small UAS is addressed. Three possible solutions of differing complexity and effectiveness are proposed to address the problem. The first method involves an active detection strategy, whereby carefully designed excitation signals are superimposed on the control commands to increase the detectability of the attack. In the second method, an unknown input observer is designed, which in addition to detecting the attack also estimates the magnitude of the attack. The third method entails designing an actuator system that makes use of variable frequency pulse-width modulated signals to improve the resilience of the actuator against malicious attacks. The effectiveness of the proposed methods is demonstrated using flight experiments and realistic MATLAB simulations that incorporate exogenous disturbances, such as steady winds, atmospheric turbulence, and measurement noise.

Workspace Modeling and Path Planning for Truss Structure Inspection by Unmanned Aircraft

Small unmanned aircraft systems can increase efficiency and reduce risk to humans during the inspection of truss-supported structures such as steel bridges. This paper describes a method to mathematically represent a truss and subsequently to plan an efficient collision-free inspection path. The algorithm checks user-defined perspectives for feasibility and redefines any infeasible perspectives. A deterministic roadmap is then generated as the complete graph over these perspectives, and some additional nodes are generated near the joints of the structure. A traveling salesman problem (TSP) is solved to find an efficient inspection path that tours all perspective points, and a local A* path planner then refines the inspection path to circumvent obstructions. The TSP is re-solved, with an updated cost based on local detours; and the process iterates until a feasible TSP solution is found. The “lazy” approach to global and local planning, for which paths are checked for feasibility after the fact and amended if necessary, ensures quick convergence to an efficient inspection path. Simulation results show the functionality of the algorithm. Comparison with a probabilistic roadmap method indicates the proposed algorithm’s efficiency.

Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning.

Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. Model confidence thresholds are adjusted leading to significant increases in models precision. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan.

Conflict Risk Analysis of Small Unmanned Aircraft Systems

As small unmanned aircraft systems (sUAS) evolve, more sUAS are being used for various applications, such as parcel delivery. Consequently, sUAS traffic is increasing, which is bringing about concern about the risk of conflicts between sUAS. Hence, it is important to assess conflict risk for the safety of sUAS traffic. Although many research efforts have been made on conflicts between aircraft, there is a gap in research assessing the conflict risk of sUAS traffic. Conflict risk is defined as the product of the severity and the probability of conflicts. Previous studies suggest methodologies for the calculation of conflict risk but do not take into account the uncertainty of aircraft position. This study suggests a methodology to assess the conflict risk of sUAS traffic while reflecting position uncertainty. Assessments were performed using Monte-Carlo simulations for two illustrative examples with structured and unstructured traffic. Conflict risk is shown to be affected by the flow rate, sUAS speed, intersection angle, and number of sUAS. The results show that structured traffic is better than unstructured traffic in terms of conflict risk.

On the Use of Unmanned Aircraft for Sampling Mesoscale Phenomena in the Preconvective Boundary Layer

AbstractThe potential value of small unmanned aircraft systems (UAS) for monitoring the preconvective environment and providing useful information in real time to weather forecasters for evaluation at a National Weather Service (NWS) Forecast Office are addressed. The general goal was to demonstrate whether a combination of fixed-wing and rotary-wing UAS can provide detailed, accurate, and useful measurements of the boundary layer important for determining the potential for convection initiation (CI). Two field operations were held: a validation study in which the UAS data were compared with collocated measurements made by mobile rawinsondes and ground-based remote sensing systems and a real-time experiment held to evaluate the potential value of the UAS observations in an operationally relevant environment. Vertical profile measurements were made by the rotary-wing UAS at two mesonet sites every 30 min up to 763 m (2500 ft) AGL in coordination with fixed-wing UAS transects between the sites. The results showed the ability of the fixed-wing UAS to detect significant spatial gradients in temperature, moisture, and winds. Although neither of two different types of rotary-wing UAS measurements were able to strictly meet the requirements for sensor accuracy, one of the systems came very close to doing so. UAS sensor accuracy, methods for retrieving the winds, and challenges in assessing the representativeness of the observations are highlighted. Interesting mesoscale phenomena relevant to CI forecasting needs are revealed by the UAS. Issues needing to be overcome for UAS to ever become a NOAA operational observing system are discussed.

Perspectives on the Application of Unmanned Aircraft for Freshwater Fisheries Census

This work provides an overview of the use of small unmanned aircraft systems for freshwater fisheries survey and fish identification. As an example, a series of river reaches in Mongolia was surveyed for identification and sizing of the endangered Taimen Hucho taimen, the world's largest salmonid. Using polarized video imagery, Taimen were positively identified in depths of over 2 m. River reaches were chosen for survey to include likely Taimen holding grounds as well as areas that were unlikely Taimen habitat. Large areas of river reaches were quickly surveyed with video imagery stored for analysis. Using land‐based targets for sizing and flying autonomous search patterns, we found that Taimen were easily identifiable based on their swimming patterns and could be remotely sized; furthermore, the fish did not appear to be disturbed by the presence of overhead aircraft. Lessons learned from the experiments and recommendations for future advances are provided. The use of small unmanned aircraft systems for fisheries is shown to be a novel and inexpensive alternative to traditional fisheries survey methods.

Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier

Weed detection with aerial images is a great challenge to generate field maps for site-specific plant protection application. The requirements might be met with low altitude flights of unmanned aerial vehicles (UAV), to provide adequate ground resolutions for differentiating even single weeds accurately. The following study proposed and tested an image classifier based on a Bag of Visual Words (BoVW) framework for mapping weed species, using a small unmanned aircraft system (UAS) with a commercial camera on board, at low flying altitudes. The image classifier was trained with support vector machines after building a visual dictionary of local features from many collected UAS images. A window-based processing of the models was used for mapping the weed occurrences in the UAS imagery. The UAS flight campaign was carried out over a weed infested wheat field, and images were acquired between a 1 and 6 m flight altitude. From the UAS images, 25,452 weed plants were annotated on species level, along with wheat and soil as background classes for training and validation of the models. The results showed that the BoVW model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps. Regarding site specific weed control, the classified UAS images would enable the selection of the right herbicide based on the distribution of the predicted weed species.

Online Risk-Based Supervisory Maneuvering Guidance for Small Unmanned Aircraft Systems

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot-in-command of small unmanned aircraft systems should be able to maintain situational awareness and make necessary maneuvers to avoid potential conflicts with nearby air traffic during mid-air encounters. However, due to ever-changing flight environments, the pilot-in-command often mis-estimates collision risks and cannot engage appropriate maneuvers to prevent mid-air traffic from violating the safety boundaries of small unmanned aircraft systems. To fix this problem, an online risk-based guidance method is therefore designed and developed in this paper to quantitatively assess mid-air collision risks and provide online mitigation solutions for detect-and-avoid systems. This will help the pilot-in-command identify hazards and choose appropriate avoidance maneuvers before a safety boundary violation occurs.

Exploring tandem wing UAS designs for operation in turbulent urban environments

The stability of small unmanned air systems can be challenged by turbulence during low-altitude flight in cluttered urban environments. This paper explores the benefits of a tandem wing aircraft configuration with the implementation of a pressure-based phase-advanced turbulence sensory system on a small unmanned air system for gust mitigation. The objective was to utilise passive and active methods to minimise gust-induced perturbations. Experimentation in repeatable turbulence within a wind tunnel’s test section was conducted. The experiments focus on the roll axis, which is isolated through a specially designed roll-axis rig. The results show improvement over conventional aircraft. This work is part of a larger research project aimed at enabling safe, stable and steady small unmanned air systems flight in urban environments.

Pilots’ Estimation of Altitude of a Small Unmanned Aircraft System (sUAS)

Small unmanned aircraft system (sUAS) operations are growing at a rapid rate, with an increasing number of civilian operations. Currently, the Federal Aviation Administration permits both hobbyist and commercial operations. The requirements for the operations differ; for commercial operations, the sUAS must generally be flown under 400 feet. Past data indicate that operators are poor at judging the altitude of sUAS, and there is variability in the altitude information that is presented to the operator. Here, we examined the ability of commercial and hobbyist sUAS pilots to estimate the altitude of their ownship during a realistic flying task. Participants flew a DJI Phantom 4 Pro to three prescribed altitudes: 50 feet, 200 feet, and 350 feet. In each trial, the participant flew the sUAS from its starting point, hovered at what he or she estimated to be the prescribed altitude, and took a photo of a target. Results indicated that participants’ altitude estimates were below the prescribed altitude of 50 feet 52% of the time, and they were below prescribed altitudes of 200 feet and 350 feet 89% of the time. Despite differences in background, performance did not differ between hobbyist and commercial pilots. Variability in absolute and barometric measurements of altitude was also observed. Taken together, the results suggest that sUAS pilots, regardless of their experience, are poor at judging the altitude of their ownship—especially at higher altitudes. The variability in performance and altitude measurements indicates that pilots need a reliable and standard way to measure the altitude of their ownship, especially given the increasingly complex environments in which sUAS intend to fly.

Diversity and Ice Nucleation Activity of Microorganisms Collected With a Small Unmanned Aircraft System (sUAS) in France and the United States.

Many microbes relevant to crops, domestic animals, and humans are transported over long distances through the atmosphere. Some of these atmospheric microbes catalyze the freezing of water at higher temperatures and facilitate the onset of precipitation. We collected microbes from the lower atmosphere in France and the United States with a small unmanned aircraft system (sUAS). 55 sampling missions were conducted at two locations in France in 2014 (an airfield in Pujaut, and the top of Puy de Dôme), and three locations in the U.S. in 2015 (a farm in Blacksburg, Virginia, and a farm and a lake in Baton Rouge, Louisiana). The sUAS was a fixed-wing electric drone equipped with a remote-operated sampling device that was opened once the aircraft reached the desired sampling altitude (40–50 meters above ground level). Samples were collected on agar media (TSA, R4A, R2A, and CA) with and without the fungicide cycloheximide. Over 4,000 bacterial-like colonies were recovered across the 55 sUAS sampling missions. A positive relationship between sampling time and temperature and concentrations of culturable bacteria was observed for sUAS flights conducted in France, but not for sUAS flights conducted in Louisiana. A droplet freezing assay was used to screen nearly 2,000 colonies for ice nucleation activity, and 15 colonies were ice nucleation active at temperatures warmer than −8°C. Sequences from portions of 16S rDNA were used to identify 503 colonies from 54 flights to the level of genus. Assemblages of bacteria from sUAS flights in France (TSA) and sUAS flights in Louisiana (R4A) showed more similarity within locations than between locations. Bacteria collected with sUAS on TSA in France and Virginia were significantly different across all levels of classification tested (P < 0.001 for class, order, family, and genus). Principal Coordinates Analysis showed a strong association between the genera Curtobacterium, Pantoea, and Pseudomonas from sUAS flights in Virginia, and Agrococcus, Lysinibacillus, and Paenibacillus from sUAS flights in France. Future work aims to understand the potential origin of the atmospheric microbial assemblages collected with sUAS, and their association with mesoscale atmospheric processes.

Coordinated Sampling of Microorganisms Over Freshwater and Saltwater Environments Using an Unmanned Surface Vehicle (USV) and a Small Unmanned Aircraft System (sUAS).

Biological aerosols (bioaerosols) are ubiquitous in terrestrial and aquatic environments and may influence cloud formation and precipitation processes. Little is known about the aerosolization and transport of bioaerosols from aquatic environments. We designed and deployed a bioaerosol-sampling system onboard an unmanned surface vehicle (USV; a remotely operated boat) to collect microbes and monitor particle sizes in the atmosphere above a salt pond in Falmouth, MA, United States and a freshwater lake in Dublin, VA, United States. The bioaerosol-sampling system included a series of 3D-printed impingers, two different optical particle counters, and a weather station. A small unmanned aircraft system (sUAS; a remotely operated airplane) was used in a coordinated effort with the USV to collect microorganisms on agar media 50 m above the surface of the water. Samples from the USV and sUAS were cultured on selective media to estimate concentrations of culturable microorganisms (bacteria and fungi). Concentrations of microbes from the sUAS ranged from 6 to 9 CFU/m3 over saltwater, and 12 to 16 CFU/m3 over freshwater (over 10-min sampling intervals) at 50 m above ground level (AGL). Concentrations from the USV ranged from 0 (LOD) to 42,411 CFU/m3 over saltwater, and 0 (LOD) to 56,809 CFU/m3 over freshwater (over 30-min sampling intervals) in air near the water surface. Particle concentrations recorded onboard the USV ranged from 0 (LOD) to 288 μg/m3 for PM1, 1 to 290 μg/m3 for PM2.5, and 1 to 290 μg/m3 for PM10. A general trend of increasing concentration with an increase in particle size was recorded by each sensor. Through laboratory testing, the collection efficiency of the 3D-printed impingers was determined to be 75% for 1 μm beads and 99% for 3 μm beads. Additional laboratory tests were conducted to determine the accuracy of the miniaturized optical particle counters used onboard the USV. Future work aims to understand the distribution of bioaerosols above aquatic environments and their potential association with cloud formation and precipitation processes.

The Impact of Sensor Response and Airspeed on the Representation of the Convective Boundary Layer and Airmass Boundaries by Small Unmanned Aircraft Systems

AbstractThe objective of the research presented is to assess the impact of sensor response and aircraft airspeed on the accuracy of in situ observations collected by small unmanned aircraft systems profiling the convective boundary layer or transecting airmass boundaries. Estimates are made using simulated aircraft flown within large-eddy simulations. Both instantaneous errors (differences between observed temperature, which include the effects of sensor response and airspeed, and actual temperature) and errors in representation (differences between serial observations and representative snapshots of the atmospheric state) are considered. Synthetic data are retrieved assuming a well-aspirated first-order sensor mounted on rotary-wing aircraft operated as profilers in a simulated CBL and fixed-wing aircraft operated through transects across a simulated airmass boundary. Instantaneous errors are found to scale directly with sensor response time and airspeed for both CBL and airmass boundary experiments. Maximum errors tend to be larger for airmass boundary transects compared to the CBL profiles. Instantaneous errors for rotary-wing aircraft profiles in the CBL simulated for this work are attributable to the background lapse rate and not to turbulent temperature perturbations. For airmass boundary flights, representation accuracy is found to degrade with decreasing airspeed. This signal is most pronounced for flights that encounter the density current wake. When representation errors also include instantaneous errors resulting from sensor response, instantaneous errors are found to be dominant for flights that remain below the turbulent wake. However, for flights that encounter the wake, sensor response times generally need to exceed ~5 s before instantaneous errors become larger than errors in representation.

Use of a Value Model to Ethically Govern Various Applications of Small UAS

Widespread use of small unmanned aircraft systems is becoming prominent in the US. From structural health monitoring to journalism, small unmanned aerial systems (sUAS) are allowing people to gain a view of their surroundings and conduct their jobs in ways like never before. With this come many ethical concerns that must be addressed before the sight of a sUAS flying overhead is a widely acceptable occurrence to a majority of the population. Currently, UAS operations used in civil airspace are governed by the Federal Aviation Administration (FAA) Part 107 rules, but these regulations do not address certain ethical considerations. This paper will use the concept of a value model to quantify these ethical concerns so that they may be encoded into the design of a UAS and evaluation of missions before the missions are conducted. This could prove valuable in addressing the ethical challenges that are faced when implementing unmanned aerial systems (UAS) operations into the airspace, especially when UASs are in airspace in densely populated areas.

Estimation of Minimum Alerting Boundaries in Winds for Small Unmanned Aircraft Systems

To achieve a level of safety equivalent to manned aircraft in the national airspace system, the pilot in command of small unmanned aircraft systems should be able to maintain situational awareness and make necessary maneuvers to avoid potential conflicts with nearby air traffic during midair encounters. However, due to ever-changing flight environments, the pilot in command often misestimates conflict risks and cannot engage the appropriate maneuvers to prevent midair traffic from violating the safety boundaries of small unmanned aircraft systems. To fix this problem, a Monte-Carlo-simulation-based method is therefore introduced and designed to quantitatively determine the alerting threshold boundaries in various wind conditions for detect-and-avoid systems to help the pilot in command identify hazards and trigger avoidance maneuvers before a safety boundary violation occurs.

Considerations for Atmospheric Measurements with Small Unmanned Aircraft Systems

This paper discusses results of the CLOUD-MAP (Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics) project dedicated to developing, fielding, and evaluating integrated small unmanned aircraft systems (sUAS) for enhanced atmospheric physics measurements. The project team includes atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use sUAS in atmospheric physics. Annual integrated evaluation of the systems in coordinated field tests are being used to validate sensor performance while integrated into various sUAS platforms. This paper focuses on aspects related to atmospheric sampling of thermodynamic parameters with sUAS, specifically sensor integration and calibration/validation, particularly as it relates to boundary layer profiling. Validation of sensor output is performed by comparing measurements with known values, including instrumented towers, radiosondes, and other validated sUAS platforms. Experiments to determine the impact of sensor location and vehicle operation have been performed, with sensor aspiration a major factor. Measurements are robust provided that instrument packages are properly mounted in locations that provide adequate air flow and proper solar shielding.

Well-Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk

Unmanned aircraft systems must demonstrate a capability to sense and avoid air traffic as part of a layered conflict management system to enable safe operations in the National Airspace System. During operations, an unmanned aircraft system should attempt to remain “well clear” to minimize the need for a collision avoidance action. Previously, a well-clear definition was adopted for large unmanned aircraft systems; however, this definition is not appropriate for small unmanned aircraft system weighing less than 55 lb operating at low altitudes. In response, this paper outlines research toward a definition of well clear for small unmanned aircraft systems, based on airborne collision risk, for midterm concepts of operations at low altitudes in nonterminal airspace.