UAVs Research Articles

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

UAV-Based Phenotyping: A Non-Destructive Approach to Studying Wheat Growth Patterns for Crop Improvement and Breeding Programs

Rising food demands require new techniques to achieve higher genetic gains for crop production, especially in regions where climate can negatively affect agriculture. Wheat is a staple crop that often encounters this challenge, and ideotype breeding with optimized canopy traits for grain yield, such as determinate tillering, synchronized flowering, and stay-green (SG), can potentially improve yield under terminal drought conditions. Among these traits, SG has emerged as a key factor for improving grain quality and yield by prolonging photosynthetic activity during reproductive stages. This study aims to highlight the importance of growth dynamics in a wheat mapping population by using multispectral images obtained from uncrewed aerial vehicles as a high-throughput phenotyping technique to assess the effectiveness of using such images for determining correlations between vegetation indices and grain yield, particularly regarding the SG trait. Results show that the determinate group exhibited a positive correlation between NDVI and grain yield, indicating the effectiveness of these traits in yield improvement. In contrast, the indeterminate group, characterized by excessive vegetative growth, showed no significant NDVI–grain yield relationship, suggesting that NDVI values in this group were influenced by sterile tillers rather than contributing to yield. These findings provide valuable insights for crop breeders by offering a non-destructive approach to enhancing genetic gains through the improved selection of resilient wheat genotypes.

Ecosystem model of development of the unmanned aircraft systems industry

The article analyzes the structural transformations of Russian industry in the context of external constraints and the need to ensure technological sovereignty in high-tech industries. The scientific hypothesis of the work is the assumption of the importance of cross-sectoral coordination of subjects of industrial policy in order to develop the production of critical products. The digital industrial ecosystem of the unmanned aircraft systems (UAS) industry is considered as an object of research. The purpose of the research is to develop the foundations of the structure of the ecosystem model of the development of the UAS industry and substantiate the feasibility of its application in the interests of improving high-tech industries of the Russian Federation. The article offers the author’s interpretations of the concepts of “digital industrial ecosystem” and “cross-industry ecosystem”, substantiates the principles of functioning of the ecosystem model and its architecture. An ecosystem model of the development of the UAS industry was developed, including management bodies, technical infrastructure, organizational and economic tools, technological clusters and a network of participants. The main distinguishing features of the proposed model from the existing ones are: the distribution of ecosystem management bodies that ensure the autonomy of management at all horizontal and hierarchical levels of its organization; the ecosystem is considered as an instrument of the mechanism of strategic development of high-tech industries involved in the creation of civil-purpose UAS, and provides cross-industry interaction of participants in order to accelerate the development of industrial production and the formation of the UAS market. The ecosystem management model will ensure the possession of up-to-date data of actual and planned indicative evaluation indicators of the state and development of the industry on a time scale close to real, based on the principle of digital transparency.

A rapid aerodynamic noise prediction model for multi-rotor unmanned aircraft systems with inclusion of wake effect

In this work, the effect of wake on the rotor aerodynamics and aeroacosutics is studied based on an efficient rapid prediction model, which incorporates the induced effects of tip vortex on the blades into the blade element momentum theory. The tip vortex model is estimated using the free vortex wake method. The aerodynamic flow variables, as well as the unsteady loadings due to the radial flow and induction of tip vortices, are employed to model to equivalent sources and, therefore, noise emission, using Goldstein's acoustic analogy. The comparison between the hybrid algorithm and blade element momentum theory shows that the wake effect is not significant in the case of axial flow only. During forward flight conditions, the wake vortex correction greatly affects high frequency harmonics. In addition, a preliminary case study of a quad-rotor vehicle is also discussed, demonstrating the proposed low-fidelity computational framework is helpful for low-noise design and operation of the multi-rotor UAS vehicles.

Control of a fixed wing unmanned aerial vehicle using a higher-order sliding mode controller and non-linear PID controller

Unmanned aerial vehicles (UAVs) have seen a rise in use during the last few years. Such aircrafts are now a convenient way to complete dangerous, dirty, and tedious tasks. Given that their operation involves a control problem which is non-linear and coupled, it is difficult to analyse. This paper presents the modeling and control of a fixed-wing unmanned aircraft as a contribution to this field. The system’s flight dynamics is derived using Newton’s second law of motion. The system is designed to have a non-linear Proportional Integral Derivative (NPID) controller and a higher-order sliding mode controller (HOSMC). When simulating the system using MATLAB Simulink software, an external disturbance was added to test the robustness of the controllers. Five performance indices which include mean square error (MSE), integral time square error (ITSE), integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE), were used to compare the controllers performance. These indices are used to provide a numerical assessment of the two controllers’ performance. The outcomes demonstrate that the roll, pitch, and yaw states performed better than the super-twisting sliding mode controller. On the airspeed control, the non-linear PID performed better than the super-twisting sliding mode controller.

Automated wireless system for monitoring the technical condition of chimneys

The present paper considers the technology of monitoring the technical condition of chimneys in order to identify areas of increased heat losses. The technology involves an automated wireless monitoring system that includes an unmanned aircraft with additional equipment. Aim. To study the operation safety of reinforced concrete chimneys used in hazardous industrial facilities. The presented monitoring technology tackles the task of prompt identification of defects and damages to reduce the risks of potentially hazardous conditions. While developing technology for monitoring the technical condition, special attention is paid to its autonomy and intelligence. At the first stage, sensors, coordinators and remote thermometry system are installed with the transmission of data on the temperature of the chimney trunk to the control center. At the next stage, the outer surface of the chimney is examined using an unmanned aicraft equipped with a thermal imaging camera to verify information about heat losses. The final stage involves creating a thermogram of the object and a scheme of the possible locations of the identified defects in order to plan repair works. In addition, the paper introduces main advantages of the suggested technology.

Redundant Telemetry Link System for Uncrewed Aerial Vehicles

Thanks to their flexibility in research, prototyping, and development, Uncrewed Aerial Vehicles (UAV) are seeing increasing use in agriculture, healthcare, logistics, and other industries. With the increase in the use of UAV in civilian sectors, comes an increase in the possibility of errors in telemetry connections. Issues with the telemetry connection may be catastrophic and cause accidents and crashes and may also adversely affect the performance of the subsystems connected to the telemetry. In this study, a low-cost software-based redundant telemetry link system for UAV is proposed. The proposed system minimises the received packet loss using packet deduplication and is compared with a dynamic link switching method. In the proposed system, both fault tolerance and robustness are introduced into the UAV telemetry link. The analysis is performed using the ArduPilot UAV Simulation environment as a producer and a Golang implementation of the proposed methods.

Applicability of Relatively Low-Cost Multispectral Uncrewed Aerial Systems for Surface Characterization of the Cryosphere

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features.

Evaluating the reliability of UAV-based carbon dioxide measurements in the lower troposphere

Vertical profiles of carbon dioxide (CO2) were captured in rural Ontario, Canada, using an Aeroqual Series 500 monitor mounted onboard an Uncrewed Aerial Vehicle (UAV). Measurements at various altitudes within the Transport Canada regulations height limit (5m – 95m) were compared to simultaneous measurements taken by an identical sensor at ground-level. Results demonstrate a high degree of agreement between onboard and ground-level observations, with a mean different of -6.02 ppm, and the majority of differences falling within the sensor’s factory calibration accuracy (±10 ppm + 5%). The study also demonstrates the ability to capture the homogeneity of CO2 in the lower troposphere, with Intraclass Correlation Coefficients (ICC) exceeding 0.75 at all altitudes, without the addition of turbulence by UAV flight. The vertical profiles captured in this study validate the use of UAV-based measurements for understanding the distribution and transport of greenhouse gases near the surface of the Earth. This research has implications for routine air quality monitoring to improve atmospheric models, environmental impact assessments and the development of targeted emission reduction strategies.

On the development of an autonomous hexacopter drone for animal detection and collision avoidance system

On the development of an autonomous hexacopter drone for animal detection and collision avoidance system

Ship-based RPA operations for cetacean research in Antarctica: progress, opportunities and challenges

Data collection facilitated by remotely piloted aircraft (RPA) has proven to be revolutionary in many disciplines including for research in extreme environments. Here we assess current use and utility of small multirotor remotely piloted aircraft (RPAs) for the challenging role of facilitating ship-based cetacean research in Antarctica. While such aircraft are now used routinely in sheltered environments in and off Antarctica, a comprehensive literature review found that RPA-mediated cetacean research conducted from ships at sea and outside of the Antarctic Peninsula region was relatively uncommon. In order to determine the potential utility of ship-based multirotor RPA operations for cetacean research, we repeatedly deployed small RPAs during a multidisciplinary research voyage in maritime East Antarctica to collect scientific data contributing to an understanding of krill and krill predator interactions. RPA flight metrics (duration, height, length, speed, distance from ship, battery drainage, satellites acquired) were compared to ship underway environmental sampling data. At a mean duration of 12 minutes, these 139 RPA flights were relatively short yet adequate to achieve the science intended, namely a range of cetacean related data streams including photogrammetry, photo identification, behavioural observations and whale blow sampling in addition to water sampling and collection of general scenic imagery. RPA flight operations were constrained by wind speed but not by air temperature with flights undertaken throughout the full range of air temperatures experienced (down to –9.5°C) but not throughout the full range of wind speeds experienced. For a 12-minute flight duration, battery drainage was around 60% indicating that the RPAs were rarely pushed to their operational limit. There was little evidence that the cold impacted RPA lithium battery performance with estimated maximum flight time within approximately 10% of expected flight time for the RPA platforms most used. Whist small multirotor RPAs are rarely applied to cetacean related research in maritime East Antarctica, we demonstrate their value and potential to deliver data critical to address knowledge gaps that challenge the effective management of both krill and their predators.

Drone Kit-Python for Autonomous Quadcopter Navigation

Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.

A heterogenous-source geoinformation system to manage climate-induced modifications on the landscape for sustainable development

Historical landscapes in Italy have been slowly changing over the centuries and this is because their features, once fixed into specific shapes, were perpetuated until new economic and social developments occurred. Yet, in the Alpine region, this territorial organisation underwent sudden changes after World War II (WW II), resulting in a loss in population and traditional agropastoral production in favour of skiing plants and holiday houses. Moreover, the loss of traditional knowledge pertaining to environmental behaviour has resulted in the urbanisation of lands that are vulnerable to extreme events. In turn, this has hindered sustainable urban development. Nowadays, modern mapping technologies enable the state of the landscape to be assessed before, during, and after extreme events, the increased frequency of which may be related to climate change. The case study examined in this paper is the flood that hit Limone Piemonte, Italy, between the 2nd and 3rd October 2020. On that occasion, an aerial survey of the affected areas was carried out using Uncrewed Aerial Vehicles (UAV) a few weeks after the event. Spatial analyses were also performed based on very high-resolution satellite imagery acquired a few days after the event in order to determine where to plan more detailed three-dimensional (3D) surveys. This has enabled assessments of damages at different map scales to be performed. Thanks to the availability of pre-event multitemporal cartographic reference datasets, it was possible to monitor the historical evolution of the affected areas. It was possible to assess the vulnerable areas before the event, as well as to evaluate the morphological and settlement changes after the disaster. Therefore, one of the goals of this manuscript is to demonstrate that geoinformation systems are among the primary technical tools used to analyse environmental and climatic alterations that impact landscapes. Finally, a 3D model of the affected areas was produced, fulfilling another goal of the research by providing the public administration with a sustainable and innovative tool for territorial and landscape management, in accordance with the 11th pillar of the United Nations Sustainable Development Goals (UN SDGs).

Artificial olfactory memory system based on conductive metal-organic frameworks

The olfactory system can generate unique sensory memories of various odorous molecules, guiding emotional and cognitive decisions. However, most existing electronic noses remain constrained to momentary concentration, failing to trigger specific memories for different smells. Here, we report an artificial olfactory memory system utilizing conductive metal-organic frameworks (Ce-HHTP) that integrates sensing and memory and exhibits short- and long-term memory responses to alcohols and aldehydes. Experiments and theoretical calculations show that distinct memories are derived from the specific combinations of Ce-HHTP with O atoms in different guest. An unmanned aircraft equipped with this system realized the sensory memories in established areas. Moreover, the fusion of portable detection boxes and wearable flexible electrodes demonstrated the immense potential in off-site pollution monitoring and health management. This work represents an artificial olfactory memory system with two specific sensory memories under simultaneous conditions, laying the foundation for bionic design with qualities of human olfactory memory.

Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.

Improved Multi-Sensor Fusion Dynamic Odometry Based on Neural Networks

High-precision simultaneous localization and mapping (SLAM) in dynamic real-world environments plays a crucial role in autonomous robot navigation, self-driving cars, and drone control. To address this dynamic localization issue, in this paper, a dynamic odometry method is proposed based on FAST-LIVO, a fast LiDAR (light detection and ranging)–inertial–visual odometry system, integrating neural networks with laser, camera, and inertial measurement unit modalities. The method first constructs visual–inertial and LiDAR–inertial odometry subsystems. Then, a lightweight neural network is used to remove dynamic elements from the visual part, and dynamic clustering is applied to the LiDAR part to eliminate dynamic environments, ensuring the reliability of the remaining environmental data. Validation of the datasets shows that the proposed multi-sensor fusion dynamic odometry can achieve high-precision pose estimation in complex dynamic environments with high continuity, reliability, and dynamic robustness.

An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments

Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment.

Preanalytic Integrity of Blood Samples in Uncrewed Aerial Vehicle (UAV) Medical Transport: A Comparative Study

The integration of unmanned aerial vehicles or uncrewed aerial vehicles (UAVs)—commonly known as drones—into medical logistics offers transformative potential for the transportation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation. Blood samples, including EDTA whole blood, serum, lithium-heparin plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing–Bablok regression and Bland–Altman plots. The results demonstrated that drone transportation maintains blood sample integrity comparable to traditional car transportation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, lithium-heparin, and citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges.

Trajectory Planning of a Mother Ship Considering Seakeeping Indices to Enhance Launch and Recovery Operations of Autonomous Drones

Trajectory Planning of a Mother Ship Considering Seakeeping Indices to Enhance Launch and Recovery Operations of Autonomous Drones

Evaluating the Role of Area Exclosure on Soil Loss Reduction Using Uncrewed Aerial Vehicle: In Case of Guho Sub-Watershed, Tigray Region, Northern Ethiopia

Evaluating the Role of Area Exclosure on Soil Loss Reduction Using Uncrewed Aerial Vehicle: In Case of Guho Sub-Watershed, Tigray Region, Northern Ethiopia