Operation Of Unmanned Aerial Vehicles Research Articles

Efficient Threshold Attribute-Based Signature Scheme for Unmanned Aerial Vehicle (UAV) Networks

Unmanned aerial vehicles (UAVs) are highly versatile and cost-effective, making them an attractive option for various applications. In UAV networks, it is essential to implement a digital signature scheme to ensure the integrity and authentication of commands sent to UAVs. Moreover, this digital signature scheme not only maintains the real-time performance of UAVs while executing commands but also protects the identity privacy of the signer. To meet these needs, we propose an efficient threshold attribute-based proxy signature (t-ABPS) scheme that integrates a threshold predicate specifically designed for UAV networks. The formal security proof for the t-ABPS scheme demonstrates its existential unforgeability under selective-attribute and chosen-message attacks (EUF-sA-CMA) in the random oracle model. This scheme also ensures the identity privacy of the signer. Furthermore, we evaluate the computational costs and communication costs associated with the proposed scheme. Our analysis indicates that the t-ABPS scheme is more computationally efficient than other existing attribute-based proxy signature schemes, but it has higher communication costs.

Edge Computing-Aided Dynamic Wireless Charging and Trip Planning of UAVs

In today’s era of rapid technological advancement, unmanned aerial vehicles (UAVs) are transforming sectors such as remote delivery, surveillance, and disaster response. However, challenges related to energy consumption and operational efficiency continue to hinder their broader adoption. To address these issues, this study proposes an integrated system design combining dynamic wireless charging (DWC), intelligent trip planning, and intelligent edge computing (IEC). The proposed system leverages IEC for local data processing to reduce latency and optimize energy management, 6G networks for real-time vehicle-to-infrastructure (V2I) communication, and DWC to enable efficient, on-the-go energy replenishment. Additionally, a dynamic arrival management algorithm is introduced to minimize UAV wait times to enhance operational efficiency. Simulations of this system demonstrated significant improvements: larger UAVs achieved an average charging efficiency of 91.2%, while smaller UAVs achieved 92.75%, with dynamic arrival management reducing wait times by an average of 1.5 min for smaller UAVs and 5.0 min for larger UAVs. These findings underscore the system’s effectiveness in optimizing UAV operations and charging efficiency. This integrated approach offers a scalable framework to enhance UAV capabilities and sets a benchmark for future advancements in operational efficiency and charging technology for urban and environmental applications.

A Cloud Detection System for UAV Sense and Avoid: Analysis of a Monocular Approach in Simulation and Flight Tests

In order to contribute to the operation of unmanned aerial vehicles (UAVs) according to visual flight rules (VFR), this article proposes a monocular approach for cloud detection using an electro-optical sensor. Cloud avoidance is motivated by several factors, including improving visibility for collision prevention and reducing the risks of icing and turbulence. The described workflow is based on parallelized detection, tracking and triangulation of features with prior segmentation of clouds in the image. As output, the system generates a cloud occupancy grid of the aircraft’s vicinity, which can be used for cloud avoidance calculations afterwards. The proposed methodology was tested in simulation and flight experiments. With the aim of developing cloud segmentation methods, datasets were created, one of which was made publicly available and features 5488 labeled, augmented cloud images from a real flight experiment. The trained segmentation models based on the YOLOv8 framework are able to separate clouds from the background even under challenging environmental conditions. For a performance analysis of the subsequent cloud position estimation stage, calculated and actual cloud positions are compared and feature evaluation metrics are applied. The investigations demonstrate the functionality of the approach, even if challenges become apparent under real flight conditions.

Modeling the aerodynamic performance of unmanned aerial vehicle (UAV) propellers with multifidelity method

In this study, an artificial neural network (ANN) based method is discussed to determine the aerodynamic performance of propellers used for Unmanned Aerial Vehicles (UAVs). Here, wind tunnel test data was used to obtain data for propellers without test data. First, wind tunnel test data was converted to a specific format using Python and modeling was done using ANN. With this modeling process, it was seen how close the model obtained with artificial neural networks produced results to the data obtained from wind tunnel tests. This study allows for more precise analysis of the aerodynamic performance of UAV propellers and optimization of their design. This approach provided a very accurate modeling of the aerodynamic performance of UAV propellers and took an important step towards determining the performance of propellers without wind tunnel test data. The obtained data constitutes a valuable resource for optimizing the design and performance of UAVs.

TRANSMISSION LINE INSPECTION WITH UAVS: A REVIEW OF TECHNOLOGIES, APPLICATIONS, AND CHALLENGES

Regular inspection of energy transmission lines plays a critical role to ensure the safety of energy infrastructures and minimise failure risks. While traditional inspection methods have limitations such as high cost, long duration and hazardous working conditions, unmanned aerial vehicles (UAVs) are emerging as an innovative alternative in this field. This paper provides a comprehensive review of the use of UAVs in the inspection of power transmission lines, focusing on the sensor technologies, artificial intelligence algorithms and field applications. The integration of LiDAR, thermal camera and multispectral sensors into UAVs offers many advantages such as three-dimensional modelling of power lines, detection of thermal anomalies and assessment of environmental risks. In addition, deep learning and reinforcement learning algorithms have been observed to improve the performance of UAVs by accelerating data processing and improving autonomous navigation. In this study, different approaches and case studies in the literature are analysed in detail, and the strengths and limitations of UAV-based inspection systems are comparatively evaluated. Accordingly, environmental challenges, sensor integration and legal regulations stand out as the main obstacles faced by these technologies in field applications. However, it is emphasised that significant improvements in data processing processes can be achieved with the integration of 5G technology and edge computing systems. This study not only evaluates the current status of UAVs in the inspection of energy infrastructures, but also provides recommendations for future research. More widespread adoption of UAV-based inspection systems will contribute to a more reliable, efficient and sustainable management of the energy sector.

Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring

Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance

As a fundamental part of water management, water sampling treatments have recently been integrated into unmanned aerial vehicle (UAV) technologies and offer eco-friendly, cost-effective, and time-saving solutions while reducing the necessity for qualified staff. However, the majority of applications have been conducted with rotary-wing configurations, which lack range and sampling capacity (i.e., payload), leading scientists to search for alternative designs or special configurations to enable more comprehensive water assessments. Hence, in this paper, the conceptual design of a novel long-range and high-capacity WIGE UAV capable of autonomous water sampling is presented in detail. The design process included a vortex lattice solver for aerodynamic investigations, while analytical and empirical methods were used for weight and dimensional estimations. Since the mission involved operation inside maritime traffic, potential obstacle avoidance scenarios were discussed in terms of operational safety, and the aim was for autonomous trajectory tracking performance to be improved by means of a stochastic optimization algorithm. For this purpose, an artificial intelligence-integrated concurrent engineering approach was applied for autonomous control system design and flight altitude determination, simultaneously. During the optimization, the stability and control derivatives of the constituted longitudinal and lateral aircraft dynamic models were predicted via a trained artificial neural network (ANN). The optimization results exhibited an aerodynamic performance enhancement of 3.92%, which indicates a remarkable improvement in trajectory tracking performance for both the fly-over and maneuver obstacle avoidance modes, by 89.9% and 19.66%, respectively.

Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework

This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted for its ability to balance multiple objectives, including path length, smoothness, collision avoidance, and real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction in path length compared to A*, achieving an average path length of 450 m. Its angular deviation of 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm and Particle Swarm Optimization (PSO). Moreover, AMOPP achieves a 0% collision rate across all simulations, surpassing heuristic-based methods like Cuckoo Search and Bee Colony Optimization, which exhibit higher collision rates. Real-time responsiveness is another key strength of AMOPP, with an average re-planning time of 0.75 s, significantly outperforming A* and RRT*. The computational complexities of each algorithm are analyzed, with AMOPP exhibiting a time complexity of O(k·n) and a space complexity of O(n), ensuring scalability and efficiency for large-scale operations. The study also presents a comprehensive qualitative and quantitative comparison of 14 algorithms using 3D visualizations, highlighting their strengths, limitations, and suitable application scenarios. By integrating weighted optimization with penalty-based strategies and spline interpolation, AMOPP provides a robust solution for UAV path planning, particularly in scenarios requiring smooth navigation and adaptive re-planning. This work establishes AMOPP as a promising framework for real-time, efficient, and safe UAV operations in dynamic environments.

Information system for assessing reliability a decoder remote piloting station

Under conditions of armed aggression by Russia against Ukraine, which escalated into a full-scale military invasion on February 24, 2022, national security is facing the greatest threat, as it is about destroying Ukrainian statehood. As experience in preparing and conducting intelligence operations by the Armed Forces of Ukraine during the large-scale invasion by the armed forces of the Russian Federation into your country has shown, using class I unmanned aerial vehicles has proven their effectiveness, which has raised issues of improving intelligence gathering with their help. A key role in the operations of a first-class unmanned aerial vehicle is played by a remote pilot station decoder. Quality operation of airborne reconnaissance assets depends on its efficiency. The article considers main reliability indicators of a human-machine system operator. The analysis results of the existing methods for controlling the humanmachine system operator, which shows that indicators of his performance are not measured, but are determined by monitoring his functional state with further evaluation of performance indicators. Based on results of the analysis, it was found that using fuzzy set theory and convolution according to a nonlinear trade-off scheme in tasks of evaluating efficiency of human-machine systems makes it possible to identify reliability status of a remote piloting station decoder in real time, taking into account human performance and equipment efficiency. The article develops an information system for assessing the reliability of a remote piloting station decoder based on a generalized mathematical model in the form of a multilevel hierarchical tree of logical inference that reflects classification of indicators and intermediate estimates. The roots of the trees correspond to evaluation results, and tops correspond to reliability indicators of a remote piloting point. This process is based on mathematical apparatus of fuzzy logic and is carried out using available expert information in form of logical rules "IF - THEN" that link fuzzy terms of remote piloting point reliability indicators and the assessment result. Reliability of the obtained data is achieved by forming a fuzzy knowledge base using fuzzy terms that take into account specifics of process obtaining intelligence information at remote piloting points.

Real-time actuator fault detection and isolation for quadrotor UAV via directional residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

A multivariable adaptive formation control for multiple UAVs with external uncertain disturbances

Purpose A multivariable model reference adaptive control method is proposed to solve a distributed leader–follower formation control problem of unmanned aerial vehicles (UAVs) with uncertain parameters and unknown external disturbances for both leader and followers. Design/methodology/approach A case of uncertain stochastic external disturbances for UAVs is considered, and based on the distributed communication network of UAVs, a state-feedback adaptive controller is proposed to maintain the formation of UAVs consistently. Then, the stability and asymptotic tracking performance of the UAV formation control system are analyzed by the Lyapunov function. Findings The simulation results demonstrate that this formation control scheme can effectively solve the stochastic external disturbance problem of UAVs and ensure the stability of their formation. Originality/value The proposed multivariable model reference adaptive control method reduces the error of formation control system and improves the stability and control performance of UAV compared with fixed control.

The Role of Vibrations in Unmanned Aerial Vehicles Efficiency Measurement Techniques and Performance Effects

Vibrations in unmanned aerial vehicles (UAVs) significantly impact flight stability, sensor accuracy, and structural integrity, with common sources including propeller rotation, motor dynamics, and aerodynamic forces. Addressing these vibrations is essential to enhancing UAVs performance and operational durability. This paper examines both theoretical and experimental vibration analysis techniques such as frequency analysis, modal analysis, and finite element analysis as key tools for understanding and controlling vibration dynamics. Various strategies for vibration mitigation are explored, including structural optimization, flight control systems, and active/passive isolation systems, all aimed at improving stability and durability. By accurately identifying vibration sources and effects, along with applying effective engineering solutions, UAVs can achieve enhanced performance, extended operational longevity, and broadened applications in sectors requiring high precision and stability. Consequently, vibration mitigation emerges as a critical factor not only for performance improvement but also for the reliable deployment of UAVs technology in demanding environments.

Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station.

Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.

The main scientific and practical results of the study at the Military Medical Academy of combat pathology during the Special Military operation

The development of problems of combat pathology is a constant focus of scientific interests of scientists of the S.M. Kirov Military Medical Academy. With the beginning of a Special military operation, it received a new impetus for development both due to the large clinical material and due to changes in the structure and nature of modern combat pathology. The relevant teams have joined forces to develop the most priority tasks. Based on the analysis of the mortality of wounded on the battlefield, the principles of “tactical medicine” were developed and implemented in practice, aimed at combating the most common causes of death at the pre-hospital stage, and training cycles for instructors for the whole country were organized in the established Center for Tactical Medicine. Principles of anesthesia and shock control at the stages of medical evacuation have been developed. Based on the study of the structure of combat surgical pathology, the tactics of screening diagnosis of hidden injuries during medical triage are proposed, the principles of staged surgical treatment of wounds (the principle of damage control) are clarified, an integrated system of treatment of wounded with injuries of the musculoskeletal system is developed. At the hospital stage, improved methods for the prevention of thromboembolic complications, treatment of phantom pain syndrome, and stress-associated mental disorders are proposed. The spectrum of wound infection pathogens and existing antibiotic resistance has been studied, new wound dressings have been developed. A prototype of the system of professional psychological selection and support of operators of unmanned aerial vehicles has been formed. Guidelines on military field surgery and military field therapy have been developed, more than 50 methodological recommendations on the most in-demand issues of organization and provision of medical care. Thus, in a little more than 900 days, the scientists of the S.M. Kirov Military Medical Academy has carried out a huge amount of work only in scientific terms, introducing the results into the practice of military doctors. This makes it possible to achieve the best performance indicators of the medical service of the Armed Forces of the Russian Federation in comparison with previous conflicts.

Blockchain Integration in UAV Networks: Performance Metrics and Analysis.

Blockchain technology has revolutionized the management of Unmanned Aerial Vehicle (UAV) networks by enhancing security, enabling decentralized control, and improving operational efficiency. This study assesses the efficiency of private blockchain architectures in UAV networks, specifically examining important performance metrics such as throughput, latency, scalability, and packet size. Furthermore, we evaluate the effectiveness of UAV networks when integrating private blockchain technologies, focusing particularly on key performance indicators such as area, altitude, and data rate. The scope of our work includes extensive simulations that employ a private blockchain to assess its impact on UAV operations. In the blockchain network, throughput decreased as the number of UAVs and transactions increased, while delay remained constant up to a certain point. In contrast, the UAV network saw improved throughput but increased delay with more UAVs and transactions. Changes in area and altitude had little impact on the blockchain network but increased delays in the UAV network. Higher data rates enhanced the UAV network by reducing latency and improving throughput, though this effect was less pronounced in the blockchain network. The aforementioned results highlight the potential and limitations of private blockchains in enhancing the durability and efficiency of UAV networks.

Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint

The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.

Validating Lithium-Polymer Battery Discharge Models to Ensure Uav Flight Safety and Performance

ABSTRACT This article proposes a methodology for assessing the adequacy of mathematical models for the discharge characteristics of lithium-polymer batteries (LPABs) used in unmanned aerial vehicles (UAVs). The methodology is based on ISO 5725 standards, focusing on the evaluation of trueness and precision under repeatability and reproducibility conditions. Experimental studies were conducted to collect data on LPAB discharge behavior and surface temperature dynamics across a range of environmental conditions (-20°C to +50°C). Key analyses included systematic error evaluation, statistical variance assessment, and the formulation of precision criteria using tools such as pairwise differences, mean square deviation, and statistical tests. The results validate the developed regression and simulation models, confirming their reliability for predicting battery performance under dynamic operational conditions. The study provides a robust framework for UAV battery monitoring, enabling accurate prediction of flight duration and ensuring safe and efficient UAV operation across diverse environmental scenarios.

Recharging the battery of an unmanned aerial vehicle, providing reliable automatic charging during flight

The authors earnestly present a novel charging device designed for an aircraft-type unmanned aerial vehicle (UAV). This innovative system includes two monoelectret blocks, one with a positive charge and the other with a negative charge, strategically installed in the UAV's wing. The setup also comprises a threshold device, supercapacitor with control unit, and electrically insulated skin sheets on the UAV wing's console and surface. As the UAV flies, it encounters aerodynamic drag influenced by factors such as flight speed and air density. This interaction generates static charges through triboelectric effects—primarily at the wing edges—resulting in significant electrostatic potential. The design ingeniously harnesses this potential: electrons knocked from air molecules accumulate on structural elements like wing consoles. However, when managed effectively by this system, they recharge the supercapacitor. This process extends flight duration and range by periodically boosting battery levels. Moreover, during flight-induced frictional interactions between air and structure surfaces—aided by secondary electron emissions from treated aluminum layers—a positive charge forms on specially designed skin sheets. These sheets continuously recharge the positively charged monoelectret block throughout flights. This earnest proposal underscores how leveraging natural phenomena can enhance UAV performance significantly while maintaining structural integrity through careful management of electrostatic interactions.

Research on Cooperative Arrival and Energy Consumption Optimization Strategies of UAV Formations

The formation operation of unmanned aerial vehicles (UAVs) is a current research hotspot, particularly in specific mission scenarios where UAV formations are required to cooperatively arrive at designated task areas to meet the needs of coordinated operations. This paper investigates the issues of cooperative arrival and energy consumption optimization for UAV formations in such scenarios. First, focusing on rotorcraft UAVs, the flight energy consumption optimization model and cooperative arrival model are derived and constructed. Next, to address the challenges in solving these models, the multi-objective non-convex functions are transformed into single-objective continuous functions, thereby reducing computational complexity. Furthermore, an interior-point-method-based solving strategy is designed by estimating the initial values of the solving parameters. Finally, simulation experiments validate the feasibility and effectiveness of the proposed method. The experimental results show that when optimizing the energy consumption of a formation of five UAVs, the algorithm converges in just 16 iterations, demonstrating its suitability for practical applications.

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class II). This rigorous approach facilitated a thorough evaluation of our model’s performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities in UAVs applications. By enhancing the capabilities of lightweight UAVs, we are not merely improving a technical aspect, but revolutionizing the potential applications of UAVs across industries. Our work sets the stage for safer, more reliable, and precise UAVs operations, marking a pivotal moment in the evolution of aerial technology in an increasingly UAV-dependent world.