Unmanned Aircraft Systems Research Articles

Unmanned-Aircraft-System-Assisted Early Wildfire Detection with Air Quality Sensors

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to scientific predictions, as the climate warms and dries, wildfires will become more intense and frequent, as well as more dangerous. These unavoidable catastrophes emphasize how important early wildfire detection and prevention are. The energy management system described in this paper uses an unmanned aircraft system (UAS) with air quality sensors (AQSs) to monitor spot fires before they spread. The goal was to develop an efficient autonomous patrolling system that detects early wildfires while maximizing the battery life of the UAS to cover broad areas. The UAS will send real-time data (sensor readings, thermal imaging, etc.) to a nearby base station (BS) when a wildfire is discovered. An optimization model was developed to minimize the total amount of energy used by the UAS while maintaining the required levels of data quality. Finally, the simulations showed the performance of the proposed solution under different stability conditions and for different minimum data rate types.

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.

Towards the environmental noise assessment of Unmanned Aircraft Systems operations

The rapid expansion of the Unmanned Aircraft Systems (UAS) sector poses a significant challenge for acoustic engineers working on the environmental noise impact assessment of these new aerial technologies. As widely recognised, the noise signatures and operational profiles of UAS differ significantly from conventional aircraft, and therefore, existing measurement procedures and assessment methods might not be able to provide an appropriate assessment of UAS noise. There are specific questions that should be addressed for a wider deployment of UAS operations without compromising communities’ health and well-being due to noise exposure: (i) how will communities respond to the unconventional noise signatures of UAS? and (ii) what measurement procedures and metric can ensure an accurate assessment of UAS noise impact? This publication describes the work carried out for the measurement and development of acoustic hemispheres for UAS operations containing sound level, frequency, and directivity information; and how this acoustic data can be used for noise mapping and auralization. Furthermore, a review of the state-of-the-art of noise metrics for UAS is presented, with a focus on existing gaps and further work required.

A novel distributed architecture for unmanned aircraft systems based on Robot Operating System 2

AbstractA novel distributed control architecture for unmanned aircraft system (UASs) based on the new Robot Operating System (ROS) 2 middleware is proposed, endowed with industrial‐grade tools that establish a novel standard for high‐reliability distributed systems. The architecture has been developed for an autonomous quadcopter to design an inclusive solution ranging from low‐level sensor management and soft real‐time operating system setup and tuning to perception, exploration, and navigation modules orchestrated by a finite‐state machine. The architecture proposed in this study builds on ROS 2 with its scalability and soft real‐time communication functionalities, while including security and safety features, optimised implementations of localisation algorithms, and integrating an innovative and flexible path planner for UASs. Finally, experimental results have been collected during tests carried out both in the laboratory and in a realistic environment, showing the effectiveness of the proposed architecture in terms of reliability, scalability, and flexibility.

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

Comparative Characteristics of Psychophysiological Peculiarities of Operators of Unmanned Aircraft Systems and Air Traffic Control Personnel

Annotation. The article is devoted to the study and comparison of professionally important psychophysiological peculiarities of operators of unmanned aircraft systems (UAS) and air traffic control (ATC) personnel who are serving in the Armed Forces of Ukraine. The purpose of the study is to establish the kinship and differences in the psychophysiological characteristics of operators of UAS and ATC personnel in order to develop recommendations for conducting professional orientation and professional selection of such specialists. To achieve the goal, 142 operators of UAS and 71 specialists who perform duties in the process of ATC under the age of 40 during the period of inpatient flight medical examination on the basis of the Military Medical Clinical Center of the Central Region were selected. The examination was carried out with the help of blank methods and software and hardware complex for psychophysiological research «PFI-2». It is shown that operators of UAS are more prone to risk compared to ATC personnel. The difference in speed characteristics of operators of UAS and ATC personnel indicates higher requirements for decision-making by operators of UAS and greater exhaustion of to the indicators of the critical frequency of light flickers and the number of omission errors in the implementation of the evaluation of a complex visual-motor reaction, which proves that they are busier during the performance of official duties. The number of correct marks when performing the attention task is higher among ATC personnel, and the number of missed rings among operators of UAS is slightly higher compared to the order studied group. Such an effect may be related to the difference in the form of their professional activity during the performance of assigned tasks. The established difference in the level of psychological and psychophysiological parameters of operators OF UAS and ATC personnel can be used to develop criteria for monitoring professionally important qualities and professional selection of specialists in the specified professions.

Status of Law Enforcement Drone Education at Two-Year Community College

Small unmanned aircraft systems (drones) have only been widely available for civilian application for less than 10 years. Despite the newness, drones are being used to support a broad range of public safety applications. The purpose of this study is to evaluate if law enforcement programs at two-year community colleges are providing adequate drone education. The course catalogs and websites of 255 community colleges in the Southeast United States were searched for drone-related keywords. Only 16% of the schools had at least one drone-related course available. A follow-up survey was sent to these schools’ law enforcement program coordinators. The survey revealed that the program coordinators know that law enforcement is using drones, felt drones reduced injuries, and desired to expand their UAS curriculum. However, the survey also found that current faculty are unfamiliar with UAS regulations and have a general lack of professional development in this area.

Handling Qualities Assessment and Performance Evaluation for Unmanned Aerial Systems and Pilots

When fly-by-wire controls first appeared, much research focused on how to evaluate control laws and bare airframes for airworthiness given the reliance on electronic augmentation. Similarly, handling qualities and methods of evaluation for novel flying vehicles have been discussed whenever novel designs are developed. In the last 20 years, with the proliferation of unmanned aircraft systems (UAS), much research has focused on transferring existing methods of performance evaluation and airworthiness assessment to accommodate the unique feature of many UAS. However, the changes in sensory information and pilot experience levels require examining the aircraft and pilots together in a holistic manner. In this work, the authors present the results of a broad study focused on evaluation of both pilot skill and aircraft performance in a holistic way. In this study, the skill of the remote pilot is considered as well as the effectiveness of the electronic flight control system because the evaluation is conducted on the system at-large as opposed to considering each piece individually. An evaluation approach using mission-task-elements (MTEs), like that used within Aeronautical Design Standard-33 Handling Qualities Requirements for Military Rotorcraft (ADS-33), is developed and presented for UAS. In the study, new UAS pilots were evaluated before, during, and after an intensive training program using qualitative measures (like Cooper–Harper rating) and quantitative measurements (like elapsed time) of their performance while flying the prescribed MTEs. The techniques described can provide insight into pilot performance, airframe airworthiness, and effectiveness of stability systems in an efficient test program and the results are inherently easy to understand in the context of MTEs.

Ground Test and Numerical Simulation of Aerodynamic Interference of the Marsupial UAS

The marsupial unmanned aircraft system (UAS) consists of a large parent unmanned aerial vehicle (UAV) and multiple small children UAVs that can be launched and recovered in the air. The employment of marsupial UAS can expand the mission range of small UAVs and enhance the collaborative capabilities of small UAVs. However, the serious aerodynamic interference between the parent UAV and the child UAV will affect the flight safety during the launch and recovery process. In this paper, the interference characteristics of marsupial UAS is investigated through ground tests and CFD simulation. Ground tests compared the lift and power of the child UAV with and without parent UAV interference in different areas, and the simulation extended the experimental scope. Three specific interference regions above the parent UAV are defined, including the area above the rotors, the area above body and the transition area. In the first two aeras, the variation of the disturbed lift is more than 30% of the child UAV weight. In the transition aera, the child UAV will be subjected to significant lift variations and asymmetric moments. According to the interference characteristics of different regions, the safe flight boundaries and the appropriate paths of children UAVs are proposed.

Strategies for Passing Regulatory Procedures during the Operational Phase of Unmanned Aircraft Systems

The problem of integration of unmanned aerial systems (UAS) into the airspace of the Russian Federation is currently receiving increased attention. The conceptual proposals under development define the main directions of regulatory and technological policy for the integration of UAS into the airspace of the Russian Federation, as well as the effective airspace use (AU) by all users in the interests of national security and economic development of the Russian Federation. The issues of regulation in the field of UAS application in the Russian Federation, in this regard, are of particular relevance. This article analyzes the main possible strategies for passing regulatory procedures at the stage of UAS operation. On the basis of this analysis, the strategies for the commercial application of UAS are formulated.

UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms

In recent years, deep learning-based approaches have proliferated across a variety of ecological studies. Inspired by deep learning’s emerging prominence as the preferred tool for analyzing wildlife image datasets, this study employed You Only Look Once (YOLO), a single-shot, real-time object detection algorithm, to effectively detect cavity trees of Red-cockaded Woodpeckers or RCW (Dryobates borealis). In spring 2022, using an unmanned aircraft system (UAS), we conducted presence surveys for RCW cavity trees within a 1264-hectare area in the Sam Houston National Forest (SHNF). Additionally, known occurrences of RCW cavity trees outside the surveyed area were aerially photographed, manually annotated, and used as a training dataset. Both YOLOv4-tiny and YOLOv5n architectures were selected as target models for training and later used for inferencing separate aerial photos from the study area. A traditional survey using the pedestrian methods was also conducted concurrently and used as a baseline survey to compare our new methods. Our best-performing model generated an mAP (mean Average Precision) of 95% and an F1 score of 85% while maintaining an inference speed of 2.5 frames per second (fps). Additionally, five unique cavity trees were detected using our model and UAS approach, compared with one unique detection using traditional survey methods. Model development techniques, such as preprocessing images with tiling and Sliced Aided Hyper Inferencing (SAHI), proved to be critical components of improved detection performance. Our results demonstrated the two YOLO architectures with tiling and SAHI strategies were able to successfully detect RCW cavities in heavily forested, heterogenous environments using semi-automated review. Furthermore, this case study represents progress towards eventual real-time detection where wildlife managers are targeting small objects. These results have implications for more achievable conservation goals, less costly operations, a safer work environment for personnel, and potentially more accurate survey results in environments that are difficult using traditional methods.

BIM and BEM Interoperability–Evaluation of a Case Study in Modular Wooden Housing

Building information modelling (BIM) is the first step towards implementing Building 4.0, where virtual reality and digital twins are key elements. The use of unmanned aircraft systems (UAS/drones) to capture data from buildings is nowadays a very popular method, so a methodology was developed to digitally integrate the photogrammetric surveys of a building into BIM, exclusively with the use of drones. Currently, buildings are responsible for 40% of energy consumption in Europe; therefore, the interconnection between BIM and building energy modelling (BEM) is essential to digitalize the construction sector, increasing competitiveness through cost reduction. In this context, the BlueWoodenHouse Project aims, among other activities, to characterize the solutions/systems of building materials and monitor the temperature, relative humidity and CO2, as well as energy consumption, of a single-family modular wooden house located in the north of Portugal, with 190 m2 and three users. Thus, the experimental monitoring results, of this case study, were used to validate the numerical model developed in the DesignBuilder simulator, which includes the building envelope’s 3D geometrical data obtained by one of those aircraft, in order to demonstrate the usefulness of drones for the optimization of solutions, from the energy point of view.

Unmanned Aircraft Systems: A Latin American Review and Analysis from the Colombian Context

The usage of unmanned aircraft systems to complete routine, commercial, and industrial tasks has increased throughout the world, evidencing better profitability and reducing risks for operators. However, in some countries, there is a low implementation of unmanned aircraft systems, particularly in the electrical sector, due to a lack of appropriation or adaptation of technology to the local environment. Therefore, this paper presents an analysis of the uses of unmanned aircraft systems in the electrical industry worldwide and its possible application to a local context to identify how the expansion of unmanned aerial vehicles is helping various industries. The contribution of this paper is to show how the employment of unmanned aerial vehicles can help in any particular task in the electrical sector and the appropriation of these technologies in a country, showing a possible categorization of unmanned aerial vehicles based on future applications and current regulations. The analysis was carried out in the Colombian context, considering the current regulation and the impact of its use. This research considers safety, security, and privacy implications, including the reduction of personal harm with low operation costs. In addition, the importance of future implementations in Colombia is discussed as a topic of interest for any electrical company, researchers, and government entities.

Hazard Identification and Safety Requirements Derivation from a Functional Safety Perspective for UAS Conceptual Design: Based on the UK AAIB's Accident Investigation Reports on Unmanned Aircraft Systems

With the 4th Industrial Revolution, the utilization of unmanned aircraft systems is increasing day by day in various fields. However, unmanned aircraft systems are vulnerable to functional errors due to their unique characteristics, such as remote control and automatic flight. In the event of an accident, the system operator and the general public may be injured, and the system manufacturer may be disadvantaged by the Product Liability Act. In addition, with the establishment of international standards related to risk management and functional safety, such as ISO/IEC/IEEE 16085, IEC 61508, and ARP4761, risk analysis and countermeasures have become essential when developing systems. It is time to ensure functional safety at the conceptual design phase to protect public safety and manufacturers' interests. This study reviews international standards to confirm the importance of identifying functional safety hazards and managing requirements. In addition, it also analyzes the accident investigation reports of the UK AAIB to identify hazards of unmanned aircraft systems and derive safety requirements.

Object Recognition of a GCP Design in UAS Imagery Using Deep Learning and Image Processing—Proof of Concept Study

Image-based unmanned aircraft systems (UASs) are used in a variety of geodetic applications. Precise 3D terrain surface mapping requires ground control points (GCPs) for scaling and (indirect) georeferencing. In image analysis software (e.g., Agisoft Metashape), the images can be generated to a 3D point cloud using Structure-from-Motion (SfM). In general, the conventional GCP design for UAS flights is a checkerboard pattern, which is provided in the software and used for automatic marker detection in each image. When changing the pattern, manual work would be required by picking the GCP individually by hand. To increase the level of automation in the evaluation, this article aims to present a workflow that automatically detects a new edge-based GCP design pattern in the images, calculates their center points, and provides this information to the SfM software. Using the proposed workflow based on deep learning (DL) and image processing, the quality of the resulting 3D model can be equated to the result with GCP center points picked by human evaluator. Consequently, the workload can be accelerated with this approach.

Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing.

Radars can be used as sensors to detect the breathing of victims trapped under layers of building materials in catastrophes like earthquakes or gas explosions. In this contribution, we present the implementation of a novel frequency comb continuous wave (FCCW) bioradar module using a commercial software-defined radio (SDR). The FCCW radar transmits multiple equally spaced frequency components simultaneously. The data acquisition of the received combs is frequency domain-based. Hence, it does not require synchronization between the transmit and receive channels, as time domain-based broadband radars, such as ultra wideband (UWB) pulse radar and frequency-modulated CW (FMCW) radar, do. Since a frequency comb has an instantaneous wide bandwidth, the effective scan rate is much higher than that of a step frequency CW (SFCW) radar. This FCCW radar is particularly suitable for small motion detection. Using inverse fast Fourier transform (IFFT), we can decompose the received frequency comb into different ranges and remove ghost signals and interference of further range intervals. The frequency comb we use in this report has a bandwidth of only 60 MHz, resulting in a range resolution of up to 2.5 m, much larger than respiration-induced chest wall motions. However, we demonstrate that in the centimeter range, motions can be detected and evaluated by processing the received comb signals. We want to integrate the bioradar into an unmanned aircraft system for fast and safe search and rescue operations. As a trade-off between ground penetrability and the size and weight of the antenna and the radar module, we use 1.3 GHz as the center frequency. Field measurements show that the proposed FCCW bioradar can detect an alive person through different nonmetallic building materials.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.

To the risk management methodology of unmanned aircraft systems

Operators of unmanned aircraft systems (UAS), performing aeronautical work in accordance with the certification requirements of the Federal Aviation Regulations, are obliged to have a Flight Safety Management System (FSMS), the critical element of which is risk management. The utilization of the risk assessment methods and the development of corrective actions, used in manned aviation, is problematic due to the UAS operational characteristics. The article presents the FSMS methodology for UAS of category B (special category) based on the developments of JARUS group, established with EASA, to address the unmanned aviation operation issues. The proposed methodology integrates the SORA method provisions, developed by JARUS, and comprised into seven documents, into the unified logically related system with consideration to the specific traits of the Russian Federation Aviation legislation and the terminology adopted in the Russian Federation civil aviation. The key problem of assessing the expected effectiveness of risk management actions in the JARUS methodological guidelines does not have a complete solution.The article proposes to solve the problem using professional evaluation by means of the hierarchy analysis method of the fuzzy set theory. This allows us to reasonably build the hierarchy of actions according to their impact on risk mitigation and to classify them into three categories. Such a classification is imperative to evaluate the applicability of actions depending on the amount of total risk. Although the UAS operation of category B is supposed to be performed in the segregated airspace, the methodology takes into consideration not only the risks of collision with objects on the ground but also mid-air collision risks with manned aircraft, since it is usually impractical to create the “ideal” segregated airspace. The proposed methodology can be utilized in the long view when developing risk management practices of UAS operation of category C in open skies.

Numerical and experimental analysis of the influence of solidity on rotor aeroacoustics at low Reynolds numbers

We compare medium and high fidelity numerical simulations to experiments conducted on low Reynolds number rotors typical of small scale Unmanned Aircraft Systems (UAS). We first show that these numerical approaches provide reasonable estimates of the aerodynamic performance and farfield tonal noise and hence apply them for the investigation of the influence of solidity ratio on the aerodynamics and acoustics of small scale rotors operating under hovering, iso-thrust conditions. We show that while solidity ratio has a weak impact on aerodynamic performance, it may help drastically reduce farfield tonal noise. This reduction is however found to depend on the interplay between thickness and loading noise such that increasing the solidity by increasing the number of blades at constant blades’ aspect ratio or by decreasing the blades’ aspect ratio keeping the number of blades constant may yield very different, sometimes opposite, trends.

Measuring Smoke Emissions from Prescribed Rangeland Burning in the Flint Hills Region Using Unmanned Aircraft Systems

Highlights Smoke samples collected with UAS during rangeland prescribed burns were used to develop representative smoke emission factors for rangeland fires. The head fire emission factors of PM2.5 and NOx were consistent with the low end of the range of flaming emission factors in the literature. The combination of high air temperatures and high absolute humidity resulted in higher levels of PM2.5 and O3 in smoke. Abstract.Prescribed burning is an ecological process critical to maintaining and improving rangeland ecosystems. Smoke impacts related to prescribed burning have been the subject of intense discussion and public debate. The objectives of this study were to collect accurate smoke emissions data using unmanned aircraft systems (UAS) and use the data to develop smoke emission factors representative of prescribed rangeland fires in the tallgrass prairie Flint Hills region of Kansas and Oklahoma. The emission factors will be used to improve the input parameters used in smoke modeling tools. Four prescribed burns on unique burn units were sampled for this study. Smoke emission factors were determined using the carbon mass balance method. Average emission factors for head fires were: PM2.5, 11.3±10.8g/kg fuel; NOx as NO, 1.4±0.9 g/kg fuel; CO2, 1569±28 g/kg fuel; CH4, 6.8±4.3 g/kg fuel; NMHC as propane, 3.3±2.5 g/kg fuel; and VOC as propane, 4.5±3.5 g/kg fuel. Compared with head fires, back fires tended to produce lower emissions of PM2.5 but higher emissions of NOx and VOC. Green, high-moisture vegetation present during the growing season fires in September resulted in a larger head fire PM2.5 emission factor compared to April fires. Generally, the combination of high air temperature and high absolute humidity resulted in high PM2.5 and O3 in smoke. Conducting prescribed fires under conditions of cool air temperature and low absolute humidity can reduce the generation of PM2.5 and O3 in smoke, as long as these conditions fall within the prescribed range for the burn. Keywords: Carbon mass balance, Drone, Fire, Grass, Ozone, Pasture, Prescribed burning, Rangeland management, Rangelands, Smoke emission factor, Tallgrass prairie, Unmanned aircraft system.