Small Unmanned Aircraft Systems Research Articles

HF/VHF Radar Sounding of Ice from Manned and Unmanned Airborne Platforms

Ice thickness and bed topography of fast-flowing outlet glaciers are large sources of uncertainty for the current ice sheet models used to predict future contributions to sea-level rise. Due to a lack of coverage and difficulty in sounding and imaging with ice-penetrating radars, these regions remain poorly constrained in models. Increases in off-nadir scattering due to the highly crevassed surfaces, volumetric scattering (due to debris and/or pockets of liquid water), and signal attenuation (due to warmer ice near the bottom) are all impediments in detecting bed-echoes. A set of high-frequency (HF)/very high-frequency (VHF) radars operating at 14 MHz and 30–35 MHz were developed at the University of Kansas to sound temperate ice and outlet glaciers. We have deployed these radars on a small unmanned aircraft system (UAS) and a DHC-6 Twin Otter. For both installations, the system utilized a dipole antenna oriented in the cross-track direction, providing some performance advantages over other temperate ice sounders operating at lower frequencies. In this paper, we describe the platform-sensor systems, field operations, data-processing techniques, and preliminary results. We also compare our results with data from other ice-sounding radars that operate at frequencies both above (Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Depth Sounder (MCoRDS)) and below (Jet Propulsion Laboratory (JPL) Warm Ice Sounding Explorer (WISE)) our HF/VHF system. During field campaigns, both unmanned and manned platforms flew closely spaced parallel and repeat flight lines. We examine these data sets to determine image coherency between flight lines and discuss the feasibility of forming 2D synthetic apertures by using such a mission approach.

Development and Flight Test Results of a Small UAS Distributed Flush Airdata System

AbstractSmall unmanned aircraft systems (sUAS) have proven their effectiveness for measuring both the inertial and aircraft-relative wind. One of the more common aircraft-relative wind instruments is the multihole probe (MHP). While the MHP is accurate and simple to use, two main drawbacks exist: 1) the MHP airdata system can cost several times that of the sUAS and 2) the probe itself is often exposed to damage during routine operations. Flush airdata systems (FADS) are an alternative approach and utilize pressure ports flush with the aircraft surface. This removes any external components, thereby mitigating the risk of damage to the airdata system. The work presented details the implementation of a FADS for sUAS. Computational fluid dynamics simulations were used to determine the port locations of the FADS. Airframe locations were sorted based on the total sensitivity over a range of angles of attack and sideslip. The FADS was calibrated in flight using an onboard MHP. Multilayer feedforward neural networks were employed to produce estimates of the angle of attack and sideslip, while static and stagnation ports on the fuselage measured airspeed. Results from flight testing show errors in angle of attack and sideslip were unbiased. Additionally, 97.9% of the errors in airspeed were within 1 m s−1 of the MHP, while 93.8% and 87.3% of the angle of attack and sideslip errors, respectively, were within 1°. Flight tests show that a FADS can be calibrated in flight and that it is an effective method for measuring the aircraft-relative wind from small UAS.

Ground-Based Sense-and-Avoid System for Small Unmanned Aircraft

In this Paper, we present a complete, proof-of-concept sense-and-avoid solution for small unmanned aircraft systems, including a small low-cost ground-based radar system, multitarget tracking and estimation, collision detection, and an avoidance planner. We describe the development of a small frequency-modulated continuous-wave phased-array radar system that provides a three-dimensional surveillance volume. The radar measurements are processed using the recursive random sample consensus algorithm, producing tracks for the intruders and the ownship. We propose a collision-detection algorithm based on the geometric relationship between encountering aircraft. If a collision threat is detected, a collision-free new path is generated for the ownship using a two-step path-planning algorithm. In the first step, an initial suboptimal path is generated using an search. In the second step, the path is refined using a variant of the potential fields technique, adapted to the sense-and-avoid scenario. The performance of the complete system is demonstrated with flight-test experiments.

Development of Small UAS Beyond-Visual-Line-of-Sight (BVLOS) Flight Operations: System Requirements and Procedures

Due to safety concerns of integrating small unmanned aircraft systems (UAS) into non-segregated airspace, aviation authorities have required a set of detect and avoid (DAA) systems to be equipped on small UAS for beyond-visual-line-of-sight (BVLOS) flight operations in civil airspace. However, the development of small UAS DAA systems also requires BVLOS flights for testing and validation. To mitigate operational risks for small UAS BVLOS flight operations, this paper proposes to initially test small UAS DAA systems in BVLOS flights in a restricted airspace with additional safety features. Later, this paper further discusses the operating procedures and emergency action plans for small UAS BVLOS flight operations. The testing results show that these safety systems developed can help improve operational safety for small UAS BVLOS flight operations.

Establishing Training and Certification Criteria for Visual Observers of Unmanned Aircraft Systems

Safe integration of Unmanned Aircraft Systems (UAS) into airspace generally occupied by manned aircraft and other aviation stakeholders is a pressing global challenge. In the United States, efforts are being made to integrate small and large UAS into the National Airspace System (NAS). Whereas regulations for the civil operation of small UAS (25 kg and lighter) have already been adopted, those for larger unmanned systems are still being crafted. Thus, a two-part mixed methods study was conducted to examine three pivotal issues in the safe operation of large UAS: (1) What kind of visual observer skills are needed to execute safe UAS operations; (2) Should visual observers involved in UAS operations receive formal training; and (3) Should visual observers be required to pass a certification exam? In the first phase, subject matter experts identified various vigilance, trajectory estimation and communication skills that were vital to performing visual observer duties successfully and elaborated on their training regimens. In the second phase, survey participants were approximately evenly split on the need for formal classroom/online and hands-on visual observer training. Furthermore, participants generally favored visual observers having to pass a classroom/online certification exam, whereas they were against a practical (hands-on) exam.

Measuring wind with Small Unmanned Aircraft Systems

Small Unmanned Aircraft Systems (SUAS) are an emerging technology that is suitable for multitude of applications requiring small sensor payloads including airborne wind measurement. This work discusses the considerations and limitations of different SUAS configurations, in particular Multi-rotor UAS (MUAS), and their capabilities when operating within the Atmospheric Boundary Layer (ABL). Several methods for measuring fluctuating flows from SUAS are discussed, and preliminary results from a quadrotor-mounted Multi-Hole Pressure Probe (MHPP) “flying anemometer” platform are presented. Flights at a range of altitudes demonstrated that in-situ measurements of both mean wind velocity and turbulence intensity from hovering airborne platform are feasible. This indicates that MUAS can be used as flexible wind sensing platforms with good high spatial resolution. Suggestions for how future SUAS technological and operation developments may further improve wind engineering applications are also discussed.

A Comparison of Traffic Organization Methods for Small Unmanned Aircraft Systems

Small unmanned aircraft systems (sUAS), better known as “drones,” have been drastically increasing in ownership and popularity during recent times. While this technology was once restricted to military and hobbyist use, numerous public and private-sector groups have proposed new uses, such as package delivery and firefighting. As the number of sUAS in operation increases, the methods currently utilized to manage sUAS traffic flow are expected to become inadequate; thus, it is necessary to develop an alternate system of sUAS traffic management. This paper proposes a computerized simulation methodology to quantitatively evaluate air traffic management methods based on safety, efficiency, and computational intensity. This methodology is then used to test the current organization method against free competition for available airspace and a freeway-style method of traffic organization known as an “air highway.” It was ultimately found that these alternate methods of organization are less foolproof than the current method with regards to safety, but are significantly more practical to implement due to reduced computational intensity; additionally, these methods are sometimes capable of reducing travel time.

COCAP: a carbon dioxide analyser for small unmanned aircraft systems

Abstract. Unmanned aircraft systems (UASs) could provide a cost-effective way to close gaps in the observation of the carbon cycle, provided that small yet accurate analysers are available. We have developed a COmpact Carbon dioxide analyser for Airborne Platforms (COCAP). The accuracy of COCAP's carbon dioxide (CO2) measurements is ensured by calibration in an environmental chamber, regular calibration in the field and by chemical drying of sampled air. In addition, the package contains a lightweight thermal stabilisation system that reduces the influence of ambient temperature changes on the CO2 sensor by 2 orders of magnitude. During validation of COCAP's CO2 measurements in simulated and real flights we found a measurement error of 1.2 µmol mol−1 or better with no indication of bias. COCAP is a self-contained package that has proven well suited for the operation on board small UASs. Besides carbon dioxide dry air mole fraction it also measures air temperature, humidity and pressure. We describe the measurement system and our calibration strategy in detail to support others in tapping the potential of UASs for atmospheric trace gas measurements.

Quantifying situation awareness for small unmanned aircraft

ABSTRACTA novel statistical model is presented to quantify situation awareness in the operation of small civilian Unmanned Aircraft Systems (UAS). Today, the vast majority of small Unmanned Aircraft Systems (UAS) operation takes place under Visual Line of Sight (VLOS) of a human operator, who is wholly responsible for the safety of the flight. As operation begins to move to Beyond Visual Line of Sight (BVLOS), it is likely that this responsibility will become shared between operator and the increasingly autonomous UAS itself. Before we seek to quantify the safety of such a system, it is beneficial to analyse the safety of existing Visual Line of Sight (VLOS) operations to provide a target level of safety. Prior to considering any on-board decision making, it is essential to ensure that the artificial situation awareness system of a UAS in Beyond Visual Line of Sight (BVLOS) is at least as good as awareness of a human operator. The paper provides a probabilistic theory and model for the high-level abstractions of situation awareness to guide future assessment of BVLOS operations.

Dynamics of a Small Unmanned Aircraft Parachute System

Parachute Systems (PS) can be readily used by small Unmanned Aircraft Systems (UAS) for risk mitigation and aircraft recovery. To date there has been limited research into the fundamental dynamics of parachutes at low Reynolds numbers,with existing studies focusing on larger parachutes. An understanding of the dynamics is needed to establish sound guidelines for parachute design and for their use during UAS operations. Existing design guidelines are reviewed and the key parachute design parameters are identifi ed. The validity of the existing guidelines applied to lower Reynolds number parachutes is explored through a series of wind-tunnel tests. It was found that existing design guidelines underpredict the key parameters of inflation time and peak forces for parachute deployments at typical UAS operating speeds. The ramifi cations on the design and operation of small UAS are discussed.

Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

Deep learning networks have shown great success in several computer vision applications, but its implementation in natural land cover mapping in the context of object-based image analysis (OBIA) is rarely explored area especially in terms of the impact of training sample size on the performance comparison. In this study, two representatives of deep learning networks including fully convolutional networks (FCN) and patch-based deep convolutional neural networks (DCNN), and two conventional classifiers including random forest and support vector machine were implemented within the framework of OBIA to classify seven natural land cover types. We assessed the deep learning classifiers using different training sample sizes and compared their performance with traditional classifiers. FCN was implemented using two types of training samples to investigate its ability to utilize object surrounding information.Our results indicate that DCNN may produce inferior performance compared to conventional classifiers when the training sample size is small, but it tends to show substantially higher accuracy than the conventional classifiers when the training sample size becomes large. The results also imply that FCN is more efficient in utilizing the information in the training sample than DCNN and conventional classifiers, with higher if not similar achieved accuracy regardless of sample size. DCNN and FCN tend to show similar performance for the large sample size when the training samples used for training the FCN do not contain object surrounding label information. However, with the ability of utilizing surrounding label information, FCN always achieved much higher accuracy than all the other classification methods regardless of the number of training samples.

Preliminary Evaluation of Behavioral Response of Nesting Waterbirds to Small Unmanned Aircraft Flight

Small unmanned aircraft systems present an emerging technology with the potential to survey colonial waterbird populations while reducing disturbance in comparison to traditional ground counts. Recent research with these systems has been performed on some colonially nesting avian species; however, none have focused on wading bird species. During 2015–2016, this study tested the behavioral response of a mixed-species rookery (Cattle Egret (Bubulcus ibis), Snowy Egret (Egretta thula), Glossy Ibis (Plegadis falcinellus) and a groundnesting colony of Common Terns (Sterna hirundo)) in shrub habitat to small unmanned aircraft system flights at 12 m, 15 m, 30 m, and 50 m. Even at the lowest altitudes, the birds either showed no reaction or acclimated within 60 sec of the fly-over. Conversely, physically entering the colony to conduct ground surveys resulted in all Common Terns flushing from their nests beginning when the observer was 50 m away and required significantly more time in the colony overall: ~30–60 min vs. ~3–7 min with the small unmanned aircraft system. While this study focuses only on the behavioral response of nesting birds and not comparison of count estimates, these results provide preliminary evidence that small unmanned aircraft systems provide the potential to monitor colonial nesting bird populations while minimizing disturbance to the colony.

UAS Maintenance: A Critical Component in Maintaining Airworthiness

Over the last several decades, the notion of traditional aircraft design has significantly changed. While there are many modern aircraft that resemble earlier models, their components, systems, and overall architecture have evolved, including the introduction of small unmanned aircraft systems (sUAS). Despite the growing consumer attraction to own and operate these aircraft, the subsequent impact on safety is an area of intense concern. The requirement of an established maintenance program for manned aircraft is rooted in ensuring that all aircraft are deemed safe for flight. Preventive maintenance schedules are based on component reliability data, which minimizes unnecessary maintenance while preventing maintenance-related aircraft accidents. This same requirement has not been extended to small unmanned aircraft systems. Current regulations and requirements for sUAS maintenance refer only to maintaining airworthiness, with no specific scheduled or unscheduled maintenance practices required. As the demand for small unmanned aircraft systems continues to grow, the safety threat will rise. The requirement of formal maintenance practices, which are based on component reliability data, would fulfill this need. To further explore the possibility of an established maintenance program for sUAS operators, this paper reviews current maintenance requirements and proposes new accident data collection measures which contribute to the creation of a scheduled maintenance program. A formal certification process which follows a modified MSG-3 process is also proposed.

Assessing the Geospatial Accuracy of Aerial Imagery Collected with Various UAS Platforms

Abstract. Recent development of small unmanned aircraft systems (UAS) provides a relatively low-cost solution to collect aerial imagery with very high spatial and temporal resolutions. The geospatial accuracy of collected data can range from a few centimeters to several meters, and the use of ground control points (GCPs) is recommended to correct for large geospatial errors. However, whether or not GCPs are used, the true geospatial accuracy of collected UAS data remains unknown. The objective of this study was to measure and compare the geospatial accuracy of images obtained with various UAS platforms at two flight altitudes. Aerial imagery was collected using four platforms equipped with different RGB cameras: Phantom 4, eBee Ag, eBee Plus, and Trimble UX5. All platforms were equipped with manufacturer GPS receivers, and RTK was activated on the eBee Plus. Each platform was flown at 75 and 120 m altitudes, and the experiment was replicated three times. Results demonstrated that using GCPs during data processing improved the horizontal and vertical accuracies of the Phantom 4, eBee Ag, and Trimble UX, decreased the between-flight variability, and accounted for the negative effect of flight altitude. On the other hand, the RTK technology used with the eBee Plus resulted in images with very high geospatial accuracy with or without GCPs. Using GCPs during data processing or RTK technology at the time of flight provided aerial imagery with horizontal accuracies of 1.5 to 10 cm and vertical accuracies of 0.0 to 0.4 m. These results are within an acceptable range for data utilization, unlike the horizontal and vertical accuracies obtained without GCPs or RTK, which ranged from 32 to 441 cm and from 1 to 126 m, respectively. Results from this study quantify the geospatial accuracy of UAS imagery and provide a better understanding of the relationships between the accuracy of the GPS receivers in UAS, flight altitude, and horizontal and vertical accuracies of collected images. Keywords: Accuracy, Drone, Ground control points, Precision agriculture, UAV.

DEVELOPMENT OF THE NEW EDUCATIONAL CONTENT “SMALL UAS IN CIVIL ENGINEERING APPLICATION SCENARIOS”

Abstract. The key point of this paper is presentation of the main idea and some results of the project “Small UAS in civil engineering application scenarios” (SUAS-CAS). This project was proposed by newly established in 2016 ISPRS WG V/7: “Innovative Technologies in Training Civil Engineers and Architects”. Here we are presenting our experience in using low-cost UAS in training architects at Kyiv National University of Construction and Architecture, which was chosen as basic for this project. In the first part of paper, the project outline is presented. Then the first and possible follow project outcomes were described. In some details is described the training module “Small UAS in architecture” which was developed and included as a part of the subject “Architectural photogrammetry”.

Potential for Small Unmanned Aircraft Systems Applications for Identifying Groundwater‐Surface Water Exchange in a Meandering River Reach

AbstractThe exchange of groundwater and surface water (GW‐SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here we describe the use of a suite of high spatial resolution remote sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW‐SW exchange. sUAS provided centimeter‐scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS‐based vegetation imagery, vegetation‐top elevation, and normalized difference vegetation index mapping indicated GW‐SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite‐based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.

ARTICLE: Droning On: The State and Federal Legal Response to the Deregulation of U.S. Airspace for Small Unmanned Aircraft Systems

With the 2012 congressional mandate that the Federal Aviation Administration promulgate rules allowing the use of small unmanned aircraft systems (commonly called drones) in U.S. airspace, many have expressed concerns about potential invasions of privacy by both private citizens and law enforcement agencies. This article provides a survey of the state and federal case law and legislative responses to such concerns through August of 2016, with a detailed focus on legislative enactments creating crimes related to civilian drone use and regulating the law enforcement use of drones to collect evidence in criminal investigations. The article also analyzes and attempts to make sense of the seemingly “hit-or-miss” state legislative responses to the advent of widespread drone use in the United States.

The rapid development of bespoke small unmanned aircraft

ABSTRACTThe ability to quickly fabricate small unmanned aircraft system (sUAS) through Additive Manufacturing (AM) methods opens a range of new possibilities for the design and optimisation of these vehicles. In this paper, we propose a design loop that makes use of surrogate modelling and AM to reduce the design and optimisation time of scientific sUAS. AM reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimiser is used to search this model to find an optimal design for a bespoke aircraft. This paper presents the design loop and a case study which demonstrates its application.

VISIBLE, VERY NEAR IR AND SHORT WAVE IR HYPERSPECTRAL DRONE IMAGING SYSTEM FOR AGRICULTURE AND NATURAL WATER APPLICATIONS

Abstract. The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (~ 50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded DroneKnowledge – Towards knowledge based export of small UAS remote sensing technology project.

The potential of small-Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index

The loss of unimproved grassland has led to species decline in a wide range of taxonomic groups. Agricultural intensification has resulted in fragmented patches of remnant grassland habitat both across Europe and internationally. The monitoring of remnant patches of this habitat is critically important, however, traditional surveying of large, remote landscapes is a notoriously costly and difficult task. The emergence of small-Unmanned Aircraft Systems (sUAS) equipped with low-cost multi-spectral cameras offer an alternative to traditional grassland survey methods, and have the potential to progress and innovate the monitoring and future conservation of this habitat globally. The aim of this article is to investigate the potential of sUAS for rapid detection of threatened unimproved grassland and to test the use of an Enhanced Normalized Difference Vegetation Index (ENDVI). A sUAS aerial survey is undertaken at a site nationally recognised as an important location for fragmented unimproved mesotrophic grassland, within the south east of England, UK. A multispectral camera is used to capture imagery in the visible and near-infrared spectrums, and the ENDVI calculated and its discrimination performance compared to a range of more traditional vegetation indices. In order to validate the results of analysis, ground quadrat surveys were carried out to determine the grassland communities present. Quadrat surveys identified three community types within the site; unimproved grassland, improved grassland and rush pasture. All six vegetation indices tested were able to distinguish between the broad habitat types of grassland and rush pasture; whilst only three could differentiate vegetation at a community level. The Enhanced Normalized Difference Vegetation Index (ENDVI) was the most effective index when differentiating grasslands at the community level. The mechanisms behind the improved performance of the ENDVI are discussed and recommendations are made for areas of future research and study.