Small Unmanned Aircraft Systems Research Articles

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers

Evaluation of the helicoidal surface theory for the noise prediction of small UAS propellers

Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

Experimental Assessment of Chance-Constrained Motion Planning for Small Uncrewed Aircraft

This work extends the experimental evaluation of chance-constrained motion planning algorithms to fielded fixed-wing small uncrewed aircraft systems (sUAS). Despite advances in planning algorithms, certain challenges remain to producing trajectories for nonholonomic mobile robotic systems such as sUAS. These challenges include nonlinear dynamics which create a complex mapping from inputs to outputs, initialization uncertainty in online motion planning due to compute time of planners and latency in data transfer, environmental uncertainty that has non-Gaussian impact on the robot’s trajectory, and system uncertainty that arises from incomplete models of complex systems. Small UAS often have proprietary components such as commercial, off-the-shelf autopilots which prevent motion planning models from accurately capturing system behavior in all regions of the state space. These challenges can be addressed by leveraging probabilistic motion planners that accurately represent and reason over dynamics and uncertainty. Chance-constrained motion planning offers a method of reasoning over uncertainty as feasibility constraints, and Monte Carlo sampling within a motion planning algorithm offers a method of representing complex uncertainty that may not have a closed form representation. This work extends the chance-constrained motion planning problem to reason over constraint on the trajectory and constraints on the state which ensure that the system model remains valid. Dynamical and systems models are formulated to extend to a broad class of fixed-wing UAS through the experimental derivation of input distributions and system parameters. Uncertainty is quantified using a data-driven approach to modeling input distributions, and Monte Carlo uncertainty sampling deployed within a Rapidly-exploring Random Tree motion planning algorithm is used to plan a trajectory containing a representation of uncertainty. The motion planning algorithm’s ability to accurately reason over layered chance constraints is evaluated experimentally in 61 fielded missions. The results show that when the flight conditions fall within the domain of the uncertainty distributions, the motion planning system is able to accurately reason over the chance constraints.

Evaluation of Dicamba Drift Injury and  Yield Loss on Soybean Using Small  Unmanned Aircraft Systems (sUAS)  and Multispectral Imaging Technologies

Highlights The response of dicamba-susceptible soybean to three simulated rates and growth stages has been studied. Soybean physiological data, such as plant height and phototoxicity, was collected at the selected growth stage. Aerial images of the experiment field using UAV were collected on specific days after treatment. Soybean yield, biological response, and the correlation of vegetative indices in predicting soybean yield were achieved. Abstract. Many dicotyledonous weeds can be selectively controlled by dicamba application during the growing season. However, vapor or spray drift from dicamba application could cause significant yield loss on non-target species such as soybean [Glycine max (L.) Merrill] that do not contain the dicamba resistance trait. In this study, the impact of simulated dicamba drift was investigated on dicamba-susceptible soybeans during different growth stages. A field experiment was conducted employing small unmanned aircraft systems (sUAS) and multispectral image sensors to assess the dose-response relationship between dicamba drift and soybean yield. The experiment followed a randomized complete block design, with main plots representing three distinct soybean growth stages (third trifoliate, sixth trifoliate, and early reproductive stage) and sub-plots encompassing various simulated dicamba drift rates. The analysis, involving non-linear regression and normalized difference vegetation index (NDVI) regression, revealed that visible soybean injuries were observed at minimal dicamba rates. However, significant yield loss occurred during the V6 growth stage at a dicamba rate of 98 g.ai ha-1, emphasizing the critical growth stage sensitivity. Moreover, a moderate yet meaningful relationship (R2 = 0.46) was established between sUAS multispectral NDVI data and soybean yield, highlighting the potential for sUAS with multispectral sensors to predict soybean yield losses. These results contribute valuable insights into the impact of dicamba drift on soybeans and the efficacy of remote sensing technology in assessing yield outcomes. Keywords: Dicamba-drift injury, Multispectral image, Precision agriculture, Soybean yield, sUAS, Yield prediction.

A Method for Evaluating Global Navigation Satellite System Position Accuracy in Small Unmanned Aircraft Systems

Highlights A novel UAS-based GNSS testing system was developed to assess both horizontal and vertical accuracy components. Recommendations are provided for further standard development related to UAS GNSS testing protocols. Future research can utilize this framework to enhance understanding of UAS GNSS accuracy. Abstract. Current standards for Global Navigation Satellite Systems (GNSS) testing are primarily designed for assessing horizontal positioning accuracy in ground-based applications. However, there is a notable absence of guidelines specifically addressing vertical accuracy assessment and applications that extend beyond ground-based scenarios, including those involving small unmanned aircraft systems (UAS). This effort aimed to develop a UAS-based GNSS testing system, tailored for the evaluation of GNSS receiver performance in dynamic low-altitude scenarios. The main objectives included (1) designing a GNSS testing system for UAS applications and (2) validating the system through field testing. The system development consisted of GNSS receiver configuration, data acquisition system design, processing procedures, and UAS payload and base station component design. The UAS payload featured multiple GNSS receivers operating in different modes, and data were collected using a custom Python script on a Raspberry Pi computer. For in-field validation, dynamic flight testing with vertical profiles was conducted. A robotic tracking total station (TTS) was used as the reference instrument for measuring the location of the UAS payload. The findings underscore the system's capabilities, primarily emphasizing its functionality rather than conducting an extensive assessment of the individual GNSS receiver performance. The UAS-based testing system that was developed was capable of satisfying most of the criteria for existing GNSS test standards. The insights derived from this process offer recommendations for standardizing UAS-based GNSS testing methods, with a particular focus on mission design, battery management, data processing, and environmental adaptability. Keywords: Dynamic Error, Global Navigation Satellite Systems (GNSS), Positioning Accuracy, Tracking Total Station (TTS), Unmanned Aircraft Systems (UAS).

The conservation impact of botanical drones: Documenting and collecting rare plants from vertical cliffs and other hard‐to‐reach areas

Abstract A high percentage of island floras are at risk of extinction and have been reduced to relic populations, often in remote hard‐to‐reach areas. Uncrewed aircraft systems (UAS aka drones) are now being utilized to assist in the survey and collection of rare plants in inaccessible areas or vertical cliff habitats. Here, we test the application of this technology for conservation of 23 plant taxa in three oceanic island hotspots: Hawaiʻi, Madeira and the Republic of Palau. We collect high‐resolution imagery using a small UAS to document the distribution and abundance of vascular flowering plants. Location information is then used to map and assess plant populations. Depending on the terrain, collections are completed using either traditional rope techniques or newly developed remote drone‐based collection methods. Over the course of 6 years, we have greatly expanded our knowledge of rare and endangered species, while increasing survey efficiency and staff safety. Most importantly, this work has had a large impact on the conservation of critically endangered plants. Although using drones for botanical conservation comes with limits and challenges, we see great potential in the continued employment of these techniques wherever plants are growing on cliffs or in other hard‐to‐reach areas.

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating from Small UAS Platforms

SDR-Based Dual Polarized L-Band Microwave Radiometer Operating from Small UAS Platforms

Construction of interpolated source from dynamic flights (UAV/AAM Special Issue)

A previous comparison of far-field predictions from equivalent acoustic sources generated from dynamic flight measurements of small unmanned aircraft systems demonstrated that a single pass over an acoustic array was insufficient to accurately predict far-field acoustic levels. In effort to reduce the uncertainty in the current study, measurements of multiple passes of dynamic flight within finite engine speed/airspeed bins were combined. From these measurements, equivalent acoustic sources were decomposed using spherical harmonics fitted to levels depropagated on a spherical surface. Two source descriptions, one at a low speed and one at high speed, represent end points on the noise-power-distance curve, which was similar to a technique demonstrated in a previous study of static measurements. Interpolation of spherical harmonic coefficients between the two sources permitted the determination of intermediate speed conditions. The resulting equivalent acoustic sources removed more than 10 dB of uncertainty from the dynamic flight definitions.

Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading

Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.

Leveraging machine learning and similarity judgements to identify perceptually relevant acoustic features of small, unmanned aircraft system signal similarity

Auditory signals can be described quantitatively by a set of measurable acoustic features (i.e., zero crossing rate, attack slope, etc.) or qualitatively with adjectives, such as whooping, thunderous, melodic, or in comparative terms such as different, louder, etc. Listeners can rate the similarity of signals and assign qualitative descriptions relatively easily; however, most lack the ability to articulate the quantitative basis of these judgments. Because the qualitative differences in signals typically correlate to a measurable difference in the acoustic features, signal similarity ratings can be used to recover the acoustic features that define signal similarity. In the present study, subjects were given combinations of small, unmanned aircraft systems (SUAS) signals consisting of either two different SUASs or SUAS and non-SUAS and asked to rate similarity on a scale from non-similar to highly similar. Using these similarity ratings along with acoustic difference features, machine learning algorithms were trained to predict human responses. These algorithms predict the position of a withheld UAS signal within the similarity feature-space. Crucial prediction acoustic difference features are extracted from the algorithms via feature importance and sensitivity analysis techniques. The extracted acoustic difference features may be inferred as prominent information impacting the human perception of signal similarity.

Cancer Care During the COVID-19 Pandemic: The African Narrative and Prospects.

The COVID-19 pandemic has had a significant impact on healthcare services globally. Whilst it has been particularly disruptive for cancer care in low-resource settings, a few African countries have been able to adapt strategies to enable continued delivery of medical care to persons with cancer. This study seeks to highlight how much effect the coronavirus pandemic has had on oncological care in Africa and to indicate the way forward. For this narrative review, PubMed and Google Scholar were used to search for literature addressing the effect of the coronavirus pandemic on the care of patients with cancer in Africa with ensuing coping strategies. Selection criteria were manuscripts published since the onset of the pandemic in 2019 and written in the English language with Africa being the focus. In total, 52 research papers involving up to 21 African nations were found and reviewed. Across the board, the COVID-19 pandemic resulted in the deferral of oncological screening programs and a halt in immunization activities routinely scheduled for preventable cancers. It caused a colossal shortage in the availability of appropriately trained medical personnel, reduced frequency and duration of outpatient consultations, and a delay in cancer investigations and diagnosis. It also stirred up the substandard modification of chemotherapy regimens and radiotherapy due to the scarcity of anticancer medications and radioisotopes and engendered the cancellation of cancer surgical procedures. Palliative care for patients with locally advanced and metastatic disease was in many cases interrupted and cancer research activities were abruptly deferred. Ultimately, these led to poor patient outcomes and increased cancer-related fatalities. However, a few African countries - Rwanda, Ghana, and Tunisia - have continued to adapt telemedicine, small unmanned aircraft systems (sUAS), and home therapy to facilitate cancer care. To date, there is a paucity of data concerning the successesand cost-effectiveness of these relatively new methods recently adapted to cater to the medical needs of cancer patients in Africa. The pandemic has presented the African communityan opportunity to advance her healthcare systems, especially as it pertains to the delivery of medical care to persons with cancer. The need of the hour is to study further the alternative cancer care delivery systems initiated during the pandemic in order to determine their sustainability in Africa at large.

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft Systems (UAS) Industry

Industry groups engage in venue shifting when they seek to overturn or alter restrictive regulations imposed by one political venue through another. A critical step in this process is resolving uncertainties surrounding the preference of the targeted venue and the nature of the relevant policy proposal. While existing studies emphasize a long-term trial-and-error process of policy learning, we focus on nascent industries and argue that ventures seek other information sources to resolve these uncertainties quickly. In particular, nascent industry groups are likely to perceive that the targeted venue will support their policy proposal if the targeted venue is ideologically distant from the venue that has enacted the restrictive regulations, if the targeted venue has recently supported other nascent industry groups’ similar policy proposals, or if the industry groups themselves are more exposed to industry peers’ success in promoting the same policy proposal in other jurisdictions. Under these conditions, the industry groups invest more to influence the targeted venue in response to restrictive regulations enacted by other venues. We find support for our theory by examining how from 2013 to 2019 the small unmanned aircraft systems industry trade associations in the United States lobbied state governments to nullify local regulations.

Safety Assessment of an Unmanned Air Vehicle Flight Control System in accordance with SAE ARP 4761

Certification Technologies of Small Unmanned Aircraft System (CTsUAS) is an Unmanned Air Vehicle, which is on development based on KC-100. KC-100 is a four-seater civil aircraft developed by the Korea Aerospace Industries (KAI) with the type certification of the US Federal Aviation Administration (FAA). For the type certification of CTsUAS, system safety assessment and analysis have been conducted in accordance with SAE ARP 4761. This paper presents the partial results of safety assessment of the flight control system of CTsUAS, focusing on the fault detection and recovery functions. Through the lessons from the results, additional considerations needed to support safety assessment and safety compliance determination of an unmanned aircraft system have been derived.   

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, combining a multidisciplinary approach in the computational analysis of additive manufactured structures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency, without sacrificing performance, while demonstrating versatility to fulfill different mission profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy aerodynamic data, which is particularly important for complex aircraft concepts such as blended wing bodies. To address challenges relating to anisotropic materials, the Tsai–Wu failure criterion is applied to the structural analysis, using CFD solutions as load inputs. Aerodynamic performance results show the low-speed variant attains an endurance of 1 h, 48 min, whereas its high-speed counterpart is 29 min at a 66.7% higher cruise speed. Each variant serves different aspects of small UAS deployment, with low speed envisioned for high-endurance surveying, and high speed for long-range or time-critical missions such as delivery. The experimental and simulation results suggest room for design iteration, in wing area and geometry adjustments. Structural simulations demonstrated the need for airframe improvements to the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to be useful across multiple industries, advocating for further research and design improvements.

A Layered Structure Approach to Assure Urban Air Mobility Safety and Efficiency

The demand for air mobility services will depend on the safety of these operations but also on the transportation time savings in congested urban areas. An adequate air space structure is therefore essential to achieve both objectives. Corridors, the most extended solution proposed nowadays, can meet the safety requirements necessary for air taxi operations, but they are rigid (point-to-point solutions) and would increase delays. As an alternative, this paper presents the airspace structure proposed in the SESAR AMU-LED Project, based on layers to assure both safety and efficiency of air taxi operations. In this proposal, small UAS will fly in the bottom part, called the Very Low Level, whereas air taxis will fly in the upper part. The paper applies a collision risk model to determine the minimum required safety buffer between both layers to assure the necessary safety levels. The results obtained show that a buffer of 10 m between them would meet the required safety levels for air taxi operations.

Analysis of Flight Plans for Visual Flight Rules Toward Preflight Information Sharing

With the increased use of small unmanned aircraft systems, the collision risk between manned aircraft and small unmanned aircraft systems is increasing. One promising solution to avoid such risks is preflight information sharing and conflict resolution between manned aircraft and small unmanned aircraft systems. However, detailed flight plan information is often not available for aircraft operating under visual flight rules. The authors developed flight estimation methods for a visual flight rules helicopter flight by using the information extracted from the visual flight rules flight plan, such as the origin/destination points, the estimated flight time, and the names of the points on the route. Furthermore, one method estimates the positional coordinate of points on the route by using the track data of the previous flights. The proposed methods were evaluated by comparing the results with the actual track, and they are discussed from the viewpoints of information quality and implementation effort. Potential implementation for future operational environment in a low-altitude airspace was investigated based on the evaluation results. Finally, recommendations are provided for preflight information sharing and conflict resolution between visual flight rules aircraft and small unmanned aircraft systems.

Optimization and Decision-Making Framework for Small Unmanned Aircraft Systems Fleet Design

Many unmanned aircraft applications require or benefit from deploying a fleet to perform a mission. Technology innovations such as rapid prototyping and microelectronics enable the use of unmanned aircraft systems (UAS) at lower costs, so the ability to design a bespoke UAS for a specific mission is an additional advantage. The problem of designing the new UAS and allocating the fleet of these aircraft is a challenging optimization problem. Using constrained multi-objective design optimization with an interactive multicriteria decision-making method, which involves the mission customer, the approach in this paper generates UAS designs and fleet allocations and determines preferential solutions for the customer. The paper presents a case study involving a UAS weather profiling mission for the aircraft sizing and fleet allocation and uses inputs from meteorological subject-matter experts acting as customers to demonstrate the functionality of the approach.

Ground Crash Area Estimation of Quadrotor Aircraft Under Propulsion Failure

Small unmanned aircraft systems or drones are expected to be used for different applications, such as parcel delivery, inspection, and aerial photography, in urban areas. However, drones usually use an electric system to power up the propulsion, communications, navigation, and flight control system, which means that it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no extensive publication about the high-fidelity modeling used by drones to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multirotor aircraft are limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different multirotor drones’ failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB cosimulation methods. Conclusions from the study show the crash trajectory, flight distance, and impact speed of the drones under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multirotor drones for the urban environment operation.

A Data-Driven Flight Proficiency Benchmark for Small Unmanned Aircraft Systems Curriculum at Two-Year Institutions of Higher Education

A Data-Driven Flight Proficiency Benchmark for Small Unmanned Aircraft Systems Curriculum at Two-Year Institutions of Higher Education