Unmanned Aerial Vehicle Applications Research Articles

Аналіз наявних методів та підходів до пошуку пошкодженої автобронетанкової техніки під час технічної розвідки в сучасних арміях світу

The article proposes the definition of directions and opportunities for the use of technical means of information in the performance of the tasks of rear support of troops in armed conflicts that are taking place today.
 Possible areas of application of unmanned aerial vehicles in the technical intelligence system of the National Guard of Ukraine and the Armed Forces of Ukraine are considered. The analysis of various types of unmanned aerial vehicles revealed the peculiarities of their use. The foreign experience of the use and development of unmanned technologies in the field of search and evacuation of damaged armored vehicles was analyzed. Conclusions were obtained regarding the combination of unmanned aerial vehicles with traditional means of technical intelligence, which showed their significant advantages. Prospective areas of application of aircraft in combination with technical intelligence systems of individual units of the National Guard of Ukraine and military units of the Armed Forces of Ukraine during operations and hostilities have been identified. The possibility of combining aviation technologies and means of technical intelligence in armed conflicts of modern armies of the world is also considered.
 Technical intelligence was analyzed from the point of view of the type of intelligence by tasks, forces, means and methods; and also as an organization of intelligence activities based on the use of technical means. The dependence on the nature of signals detected by technical means is shown, one of the types of technical intelligence is photo and infrared intelligence based on the interception of light waves (species intelligence).
 Species intelligence is considered as the collection of intelligence information based on the analysis of a large number of images obtained with the help of photographic, optical-electronic and radar equipment. According to its specificity, intelligence belongs to technical intelligence and includes air and space intelligence. Species reconnaissance uses photographic images taken in the visible range of the electromagnetic spectrum, infrared photography and multispectral photography. Radar images for species reconnaissance are created by equipment of various electromagnetic ranges synthesized in radar equipment. Spectral reconnaissance should be distinguished from electronic reconnaissance using optical-electronic or radar equipment that does not form an image.
 Military experts of developed countries believe that in the modern combat environment, unmanned aerial vehicles (UAVs) for conducting reconnaissance as a means of technical intelligence can more effectively and quickly solve the tasks of technical intelligence. At the same time, the time required to deliver the received intelligence information to the relevant management bodies is reduced.

Cyber attacks on UAV networks: A comprehensive survey

New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).

Autonomous predictive maintenance of quadrotor UAV with multi-actuator degradation

Abstract With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain.

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery

Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone.

Relative navigation with Bayesian range estimation

The applications of multiple unmanned aerial vehicles (UAVs) require high-precision and reliable relative navigation for flight formation, collision avoidance, and mission performance. Providing high-precision relative navigation is possible by fusing the observations of inertial measurement units, global navigation satellite system receivers, and radio-ranging modules. However, radio-ranging is vulnerable to various types of interference, including additive noise, nonlinear propagation, and multi-user interference. At the same time, UAVs strict energy constraints force us to look for ways to increase interference immunity without increasing the transmitting power of radio-ranging modules. This work is about improving the noise immunity of relative navigation by using a scheme tightly coupled with radio ranging and Bayesian range estimation. We describe the proposed scheme and also carry out a comparative simulation of tightly coupled and conventional, loosely coupled schemes. According to the simulation, a tightly coupled scheme has a quarter smaller RMS error compared to a loosely coupled one at an SNR below -7 dB. The obtained results demonstrate the prospects for further development of the proposed scheme.

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the geomechanical properties and rockfall potential of several rock scarps within a wide area of 50 ha. Traditional methods for evaluating geomechanical parameters on rock scarps involve time-consuming field surveys and measurements, which can be hazardous in steep and rugged environments. By contrast, drone photogrammetry offers a safer and more efficient approach, allowing for the creation of detailed 3D models of a cliff area. These models provide valuable insights into the topography, geological structures, and potential failure mechanisms. This research processed the acquired drone imagery using advanced geospatial software to generate accurate orthophotos and digital elevation models. These outputs analysed the key factors contributing to rockfall triggering, including identifying discontinuities, joint orientations, kinematic analysis of failures, and fracturing frequency. More than 8.9 × 107 facets, representing discontinuity planes, were recognised and analysed for the kinematic failure modes, showing that direct toppling is the most abundant rockfall type, followed by planar sliding and flexural toppling. Three different fracturation grades were also identified based on the number of planar facets recognised on rock surfaces. The approach used in this research contributes to the ongoing development of fast, practical, low-cost, and non-invasive techniques for geomechanical assessment on vertical rock scarps. In particular, the results show the effectiveness of drone-based photogrammetry for rapidly collecting comprehensive geomechanical data valid to recognise the prone areas to rockfalls in vast regions.

Unmanned Aerial Vehicles (UAVs) in Landslide Investigation and Monitoring: A Review

Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. We discuss the functions and limitations of various types of UAVs and sensors (RGB cameras, multi-spectral cameras, thermal IR cameras, SAR, LiDAR), outlining their roles and data processing methods in landslide applications. This review focuses on the UAVs’ roles in landslide geology surveys, emphasizing landslide mapping, modeling and characterization. For change monitoring, it provides an overview of the temporal and spatial evolution through UAV-based monitoring, shedding light on dynamic landslide processes. Moreover, this paper underscores UAVs’ crucial role in emergent response scenarios, detailing strategies and automated detection using machine learning algorithms. The discussion on challenges and opportunities highlights the need for ongoing UAV technology advancements, addressing regulatory hurdles, hover time limitations, 3D reconstruction accuracy and potential integration with technologies like UAV swarms.

Compact inductive and capacitive combined wireless power transfer system for unmanned aerial vehicle applications

Compact inductive and capacitive combined wireless power transfer system for unmanned aerial vehicle applications

A review of perception sensors, techniques, and hardware architectures for autonomous low-altitude UAVs in non-cooperative local obstacle avoidance

Unmanned Aerial Vehicles (UAVs) can detect and communicate with cooperative obstacles through established protocols. However, non-cooperative obstacles pose a significant threat to UAVs during low-flight operations. These obstacles include static obstacles like buildings, trees, or communication towers and dynamic objects like other UAVs. The application of autonomous UAVs in low-altitude surveillance has motivated research into non-cooperative local obstacle avoidance. This paper provides an overview of such solutions that have been proposed within the last decade. Unlike most literature that limits obstacle avoidance to algorithms, this work provides an in-depth review of obstacle avoidance components, namely the perception sensor, techniques, and hardware architecture of the obstacle avoidance system. This review categorizes the non-cooperative obstacle avoidance techniques into four groups: gap-based methods, geometric methods, repulsive force-based methods, and Artificial Intelligence (AI) based methods. This paper provides a comprehensive resource for researchers working on collision-free surveillance by autonomous UAVs at low altitudes.

Mechanical and electromagnetic interference shielding properties of natural fiber reinforced polymer composite with carbon nanotubes addition

AbstractMechanical and electromagnetic interference (EMI) shielding properties are investigated for developed synthesized green composite. The varying content of kenaf fiber‐reinforced high‐density polyethylene (HDPE) composite has been fabricated using microwave‐assisted compression molding techniques with and without the addition of multi‐walled carbon nanotubes (MWCNTs). X band range has been used for EMI shielding characterization. Optimization for mechanical strength has been done using tensile test, impact test, and translaminar fracture toughness test. Fractography by scanning electron and optical microscopy concluded that interfacial adhesion between kenaf fiber and HDPE matrix and the degree of ductility of the matrix are primary governing factors for change in mechanical properties. The differential scanning calorimetry confirmed no change in the degree of crystallization of the matrix with the addition of MWCNTs. Composite with EMI shielding effect above 30 dB along with superior mechanical properties is obtained at 16% (v/v) kenaf fiber content with the addition of 5% (wt.) conductive MWCNTs for possible application in electronic equipment casing, overhead bin, or unmanned aerial vehicles.

Circuit simulation-based comparison of power electronics devices in a five-level converter for UAV applications

<p>In this paper, the performance of a 5-level cascaded H-bridge inverter in unmanned aerial vehicle (UAV) applications is analized to identify, at the converter design stage, the better device choice depending on different UAV operation scenarios. Considering that regardless of the specific application there are some typical operations, such as take-off, climb, land, cruise, and potential recurring climbs and descents, the results can support the choice by considering the typical working conditions of the specific application where the UAV would be used. The results have been obtained by simulating the H-bridge inverter considering the circuit models of insulated-gate bipolar transistors (IGBTs), GaN high-electron-mobility transistors (HEMTs), and Si and SiC metal-oxide-semiconductor field-effect transistors (MOSFETs) provided by manufacturers. The study has highlighted that the choice of the device depends on the UAV usage, switching frequency, and load conditions. More specifically, considering the typical devices and systems costs in the case of a selective harmonic elimination procedure operating at the fundamental switching frequency, the Si devices should be used. Moreover, the preference for using IGBTs or Si MOSFETs depends on the typical working conditions of the UAV application. In the case of phase-shift carrier modulation technique, at 4 kHz the MOSFET is the best device and the choice between Si and SiC devices depends on the UAV application's main operation scenarios. At 20 kHz the SiC MOSFET is the best device, while at higher frequencies the GaN HEMT cost should be faced to take advantage of its best performance.</p>

Разработка и производство гражданских беспилотных летательных аппаратов: опыт КНР для реализации стратегии развития беспилотной авиации РФ

The study aims to review the development and application of civil unmanned aerial vehicles (UAVs) based on the experience of China. An example of DJI, a leading global UAV developer, is provided. The interaction between China's higher education institutions and modern national businesses, which demonstrates positive outcomes, is examined. The potential for collaboration in the education of students inclined towards entrepreneurial activities in high tech business and business representatives is explored. The study proposes the establishment of strategic cooperation between representatives of Russia and China in the development and application of UAVs and systems based on UAV technology, serving the interests of both nations.

Application of Unmanned Aerial Vehicles in Construction Industry

Technological advances in the field of electronics, such as miniature electromechanical devices and small powerful electric motors, have made it possible to develop small and light devices, such as unmanned aerial vehicles (UAVs). Recently, civilian UAVs are rapidly gaining popularity. Undoubtedly, UAVs will be used for many services in the future. There is already a growing demand for such fields of application of unmanned aerial vehicles as agriculture, emergency services, energy, fuel, mining, construction, geodesy (cartography), transportation, etc. Thanks to modern technologies it possible to produce light and low-power but accurate sensors that can be used by controllers with high computing power and low energy consumption. This makes it possible to develop complex control systems for UAVs that can be implemented on board. Today’s quadcopters are used for design, surveillance, search, construction inspections, and a variety of other applications.

Forest Fire Detection using UAV Imaginary Data

Forest fires are a major threat to ecosystems and human lives. Early detection of forest fires can greatly reduce their impact and detection is crucial in minimizing their damage. In recent years, the application of unmanned aerial vehicles (UAVs) with cameras with high resolution has become an effective tool for forest fire detection. However, manual detection and analysis of these images can be time-consuming and errorprone. In this study, we propose a forest fire detection method that uses Convolution Neural Networks (CNNs) to automatically analyze UAV imagery. The proposed method involves preprocessing the images to enhance the features of interest and then feeding them into the CNN for classification. We trained & tested our model using a dataset of UAV images and obtained a classification precision of 95%. The results indicate that our proposed method is effective in detecting forest fires using UAV imagery and can provide a fast and accurate means of early detection, which is critical in preventing the spread of wildfires. Forests are critical components of biosphere protection all around the planet. They provide significant contributions to the carbon cycle worldwide and support a diverse range of the animal and plant kingdoms. Fires in forests are among the most serious risks to living organisms in many parts of the world; they jeopardise the environment, including people, plants, and animals. Wildfires ravaged the regions of North Africa and the Mediterranean last year. Early detection of forest fires is critical for saving lives and property. The detection and prediction of forest fires are challenging undertakings given that wildfires begin tiny and are difficult to spo tin the distance. Fires can start small and spread swiftly to become enormous and dangerous. The combination of drones and high accuracy wildfire detection can be achieved by deep learning utilizing photos. UAVs can be used to help determine the fire’s location and the extent of its spread region, even though deep learning can be utilised to determine the fire’s qualities. This pairing is a critical building block for developing a system capable of more precise detection of wildfires. The recent published state-ofthe-art research publications on employing drones and deep learning to identify forest fires are examined in this paper.

A Statistical Study of Arien Sequences for an H.264 Prediction Module Lower Complexity

Abstract Advanced video coding, or H.264/AVC has been proved to possess a highly complex encoder with a block motion estimation (ME) scheme, entropy coding and a prediction module. Typical applications of unmanned aerial vehicles (UAVs) require real-time video compression. As a result, the current study aims to develop an approach to decrease the computing complexity of the prediction module. The software algorithm that was developed helps basic functions to be achieved in the prediction module. In fact, the user can set up, manage, deploy, and monitor the UAVs in real time. Evaluations obtained in this paper have shown that the adopted approach can significantly improve the execution time of coding and, consequently, the network performance. When comparing the original method of the Laboratory of Electronics and Information’s software with the suggested approach, experimental results show that the proposed method had achieved an increase in execution time from 3.544–15.574 %.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture

Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture

Resilient Neural Control Based on Event-Triggered Extended State Observers and the Application in Unmanned Aerial Vehicles

In the real world, many systems can be written as multiple-input multiple-output (MIMO) systems, such as unmanned aerial vehicles (UAVs), unmanned vehicles, etc. Therefore, this is a problem for the generality of the research method, and this paper aimming at the control problem of MIMO nonlinear systems with system uncertainties and external disturbances, a resilient control approach is proposed based on the event-triggered extended state observer (ETESO) with neural networks (NNs) in this paper. Firstly, the constraint problem of the tracking error is transformed into an unconstrained problem based on the performance function, then the NNs are employed to approximate the uncertainties, and the ETESO with NNs is designed to estimate the combination of the approximation error and the external disturbance. Secondly, the perturbation of the controller gain is described by an external system, which is estimated by a fault observer. To avoid the problem of the differential explosion, the resilient controller is designed by the backstepping control method based on the command filter. Through the Lyapunov stability analysis, all signals of the entire system are uniformly ultimate bounded. Finally, the flight control experiment is shown to demonstrate the feasibility of the control scheme.

Application of unmanned aerial vehicle with computer vision as a tool for welfare monitoring of cage-cultured, river-based hybrid red tilapia

Application of unmanned aerial vehicle with computer vision as a tool for welfare monitoring of cage-cultured, river-based hybrid red tilapia