Operation Of Unmanned Aerial Vehicles Research Articles

Heterogeneous Unmanned Aerial Vehicles Cooperative Search Approach for Complex Environments

This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the search performance of the cluster further. The distributed model predictive control and genetic algorithms are adopted to make online intelligent decisions on UAVs’ search directions. On this basis, a jump grid decision method is proposed to satisfy the maneuverability constraints of UAVs, a parameter dynamic selection method is developed to make search decisions more responsive to task requirements, and a search information transmission method with low bandwidth is designed. This approach can enable UAVs to discover targets quickly, cope with various constraints and unexpected situations, and make adaptive decisions, significantly improving the robustness of search tasks in complex, dynamic, and unknown environments. The proposed approach is tested with several search scenarios, and simulation results show that the cooperative search performance of heterogeneous UAVs is significantly improved compared to homogeneous UAVs.

The Problematic Issues of Unmanned Aerial Vehicle Application in Agriculture

The current rapid development of unmanned aviation is largely driven by the needs of the defense-industrial complex. In light of these events, the Russian industry has currently focused its attention on the development of unmanned aerial vehicles characterized by high payload capacity, autonomous flight range, and navigation precision. Considering the projected increase in the production rates of UAVs and the expansion of their functionalities, there is a pressing need for their active integration into various sectors of the economy, including agriculture. Despite the fact that the application of aerial agro-drones, like other aircraft, is regulated by the "Federal Rules for the Use of Airspace of the Russian Federation" (Government Resolution No. 138 dated March 11, 2010), there are currently no unified regulated approaches and requirements for the operation of unmanned aerial vehicles in agriculture. Given the growing relevance of issues related to the integration of unmanned aerial vehicles into agriculture, there is an increased need for the development of documents that regulate the use of agrochemicals for the treatment of agricultural land using these devices. The research objective if the development of a protocol for the inclusion of pesticides if the catalog approved for use through unmanned aerial vehicles.

Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration

This study introduces a machine learning (ML) framework to predict unmanned aerial vehicle (UAV) energy requirements under diverse environmental conditions. The framework correlates UAV flight patterns with publicly accessible weather data, to yield an energy management tool applicable to a wide range of UAV configurations. The model employs the Cross-industry standard process for data mining and advanced feature engineering, offering an in-depth analysis of meteorological factors and UAV energy demands. The study assesses several multi-regression linear and ML models, whereby ensemble models gradient boosting (GB) and eXtreme gradient boosting demonstrate superior performance and accuracy. Specifically, the GB model achieved a test mean absolute error (MAE) of 0.0395 V (V) for voltage, 0.808 A (A) for current, and 9.758 mA-hours (mAh) for discharge, with prediction accuracy of over 99.9% for voltage and discharge, and 97% for current, derived from the coefficient of determination (R2). A novel integration of real-world UAV logs and weather data underpins the development of a weather-aware ML prediction model for UAV energy consumption. Our framework is capable of concurrently predicting three components of energy and power with almost uniform accuracy, a feature not found in contemporary models. Empirical test flights show a discrepancy of only 0.005 W-hour (Wh) between total predicted and actual energy consumption. This work enhances both efficiency and safety in UAV operations. The resulting energy-predictive flight planning tool sets a new benchmark for artificial intelligence (AI) applications in intelligent automation for UAVs.

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.

Influence of Airfoil Geometry on VTOL UAV Aerodynamics at Low Reynolds Numbers

This study examines the impact of airfoil geometry on the aerodynamic properties of a low Reynolds number flying wing unmanned aerial vehicle (UAV). The investigation was conducted at three Reynolds numbers (1×106, 2×106, and 5×106), all under a constant Mach number of 0.3 during subsonic flight. Four distinct airfoils from the NACA and Selig series were analyzed using XFLR5 v6.61, with the angle of attack varying from -5° to 15°. Among the airfoils, NACA 6409 consistently demonstrated superior aerodynamic performance across all Reynolds numbers. Notably, at a Reynolds number of 1×106, NACA 6409 achieved a peak lift coefficient (Cl) of 1.584 at an angle of attack of 11.1°, indicating high efficiency. Additionally, the study explored the angles of attack where the drag coefficient (Cd) increased sharply, as well as the lift-to-drag ratios (Cl/Cd), providing a comprehensive understanding of stalling behavior and the balance between lift and drag. These findings offer valuable insights into the aerodynamic efficiency of airfoils in the context of vertical takeoff and landing (VTOL) UAV design, underscoring the importance of further research to optimize airfoil designs for enhanced VTOL UAV performance.

Optimal Periodic Control of Unmanned Aerial Vehicles Based on Fourier Integral Pseudospectral and Edge-Detection Methods

The endurance of unmanned aerial vehicles (UAVs) is a critical factor in expanding the scope of their applications. Extended flight times enable UAVs to undertake longer missions, cover larger areas and perform tasks such as persistent surveillance, data collection and search and rescue operations. Optimal trajectory planning is a cost-effective method to significantly enhance UAV endurance and performance by minimizing fuel consumption. This study introduces a novel numerical optimization framework to maximize UAV endurance. Specifically, we address the problem of determining optimal thrust and cruise angle of attack for a UAV in 2D space under specific initial, periodic and boundary conditions. By normalizing the free final time optimal control problem and employing Fourier collocation and quadrature, we transform it into a nonlinear programming problem. A key contribution of this work is accurately detecting and reconstructing the thrust history, including jump discontinuities, directly from Fourier pseudospectral data without smoothing techniques. The proposed method outperforms existing approaches in solving the periodic energy-optimal path planning problem for UAVs, as it effectively reconstructs the bang-bang thrust profile, facilitating rapid and efficient thrust adjustments essential for various flight maneuvers. Furthermore, the algorithm aligns with the UAV model, ensuring seamless integration into real-world control systems. The method’s independence from prediction horizon length, due to the use of Fourier collocation on the normalized interval [Formula: see text], is a notable advantage. This characteristic offers potential for future applications in various fields involving nonsmooth optimal control problems. This research generally provides a valuable tool for researchers and engineers working on UAV design and operation, paving the way for more efficient and effective UAV systems.

Optimization of Quadcopter Propeller Aerodynamics Using Blade Element and Vortex Theory

The optimization of quadcopter propeller aerodynamics is crucial for enhancing the efficiency and stability of unmanned aerial vehicles (UAVs), especially in their increasing applications across various sectors, including humanitarian relief and delivery services. This study focuses on optimizing quadcopter propeller performance using Blade Element Theory and Glauert Vortex Theory to analyze the aerodynamic properties and predict thrust generation accurately. Experimental methods, including wind tunnel testing and static thrust measurements, were used to validate the theoretical models. The results indicate a close correlation between theoretical predictions and experimental data, providing insights into improving propeller efficiency and mitigating aerodynamic losses. By understanding the propeller characteristics, this research contributes to the development of advanced drone propulsion systems with enhanced thrust, reduced drag, and better overall flight performance. The findings also highlight the impact of environmental factors such as turbulence and blade geometry on quadcopter stability, pointing toward areas for further improvement in propeller design. This paper serves as a valuable resource for drone hobbyists, engineers, and researchers seeking to enhance UAV performance through optimized propeller aerodynamics.

An iterative up-sampling convolutional neural network for glass curtain crack detection using unmanned aerial vehicles

It is inefficient, subjective, and potentially life-threatening for both inspectors and pedestrians to manually inspect glass curtain cracks of high-rise buildings. Due to the development of image instance segmentation techniques for deep learning and the operation of compact unmanned aerial vehicles (UAVs), automatic, accurate, and safe glass curtain crack inspection can be achieved. A novel approach proposes to segment glass curtain cracks efficiently and accurately, combining a convolutional neural network (CNN), the region of interest alignment (RoIAlign), and an iterative up-sampling method. The approach proposed achieves excellent performance with an accuracy=99.89%, precision=91.1%, recall=72.1%, F1-score=79.7%, and mIoU=78.7%. Comparative studies have been conducted to examine the performance of the proposed approach relative to Solo, Swin, Mask RCNN, and ConvNext V2. The results show that the proposed network outperforms the networks used for comparison and that it can more accurately detect and segment a variety of crack types in complex reflective environments of glass curtains.

Devising a method for integrated dataset formation and selecting a model for recognizing the technical condition of unmanned aerial vehicle

The object of this study is the process of forming a training dataset for diagnosing the technical condition of unmanned aerial vehicles (UAVs) using machine-learning algorithms. UAV flights are extremely important for various aspects of troop deployment. Combat UAV flights are performed under the influence of negative factors that cause flight special cases (FSC), which hinder the execution of combat missions, lead to mission failures, and result in the aircraft damage or loss. The available capabilities of autopilots are not enough for control under complex conditions, and in certain situations, the human operator cannot timely recognize a flight special case, including evaluation of the destructive impact of enemy’s electronic warfare systems on communication channels and operation of UAV. Therefore, the urgent issue is the intellectualization of onboard control systems, particularly towards recognizing the current technical state of UAV using artificial intelligence methods. To design such systems, labeled datasets are required. The procedure for forming datasets that consider the specificity of UAV construction and their combat use under adversarial conditions is not defined, necessitating the development of an appropriate method. Based on the well-known CRISP-DM methodology, a method for dataset formation has been proposed for subsequent use in artificial intelligence systems that use various machine-learning methods. This method differs from existing ones by considering the specificity of combat mission execution under adversarial conditions, which allowed for an 8.0 % increase in the accuracy of recognizing special cases in UAV flights by the onboard system. It also enabled timely detection of electronic warfare impacts on UAV and evaluation of the effectiveness of radio signal receivers jamming

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.

Joint Design of Transmit Waveform and Altitude for Unmanned Aerial Vehicle-Enabled Integrated Sensing and Wireless Power Transfer Systems

Recently, unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has received great attention as a promising technology for providing stable power to energy-constrained devices by navigating three-dimensional (3D) space, particularly in challenging environments such as maritime networks and smart cities. Additionally, UAV-enabled radar sensing has gained significant attention as a key technology for future 6G networks, as it enables high-accuracy sensing for various applications, such as target detection and tracking, surveillance, and environmental monitoring, as well as autonomous UAV operation. In this regard, we investigated UAV-enabled integrated sensing and wireless power transfer (ISWPT) systems that combine radar sensing and WPT operations on a unified hardware platform, sharing the same spectrum of resources. In order to accurately sense multiple targets and efficiently transfer power to multiple devices at the same time, we propose a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between radar sensing performance with the desired beam pattern and WPT performance with the desired harvested power of the devices. We first developed an effective method to obtain the optimal waveform and altitude by solving a challenging non-convex optimization problem. Based on this, we developed another efficient, low-complexity method by exploring a novel transmit waveform and optimizing its parameters to reduce computational complexity and thereby lower power consumption in UAVs. The numerical results verify that the proposed method significantly improves both radar sensing and WPT performance, as well as substantially reduces computational complexity.

Hybrid Machine Learning and Reinforcement Learning Framework for Adaptive UAV Obstacle Avoidance

This review explores the integration of machine learning (ML) and reinforcement learning (RL) techniques in enhancing the navigation and obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs). Various RL algorithms are assessed for their effectiveness in teaching UAVs autonomous navigation, with a focus on state representation from UAV sensors and real-time environmental interaction. The review identifies the strengths and limitations of current methodologies and highlights gaps in the literature, proposing future research directions to advance UAV technology. Interdisciplinary approaches combining robotics, AI, and aeronautics are suggested to improve UAV performance in complex environments.

Optimal trajectories for UAV three-dimensional path planning using a hybrid ABC-RRT* algorithm

Path planning is critical to the effective operation of unmanned aerial vehicles (UAVs) in complex environments. It is crucial to quickly determine the best path for UAV navigation. In this paper, a novel approach for unmanned aerial vehicle (UAV) path planning is presented by combining the robust artificial bee colony (ABC) algorithm with the flexible rapidly exploring random tree star (RRT*) algorithm. The main objective of this approach is to ensure effective obstacle avoidance. The proposed hybrid algorithm, ABC-RRT*, is compared with several current approaches, namely Improved ABC (IABC), ABC, Particle Swarm Optimization (PSO), ABC-PSO and RRT. ABC-RRT* combines the exploration capabilities of ABC with the route optimization capabilities of RRT* to efficiently determine the best trajectories for UAVs by balancing exploration and exploitation. ABC integration increases the effectiveness of exploration, while RRT* excels at identifying shorter paths and adapting to different conditions. ABC-RRT* has been shown to outperform other methods in terms of path length and flexibility when traversing complicated terrain with many obstacles. This was proven by extensive simulations in various situations. The results demonstrate the effectiveness, fast convergence and robustness of ABC-RRT* and make it a viable solution for obstacle avoidance of UAVs in practice. The fusion of ABC and RRT* for obstacle avoidance shows great potential for UAV route planning and offers remarkable advantages compared to conventional approaches.

Extremum Seeking-Based Radio Signal Strength Optimization Algorithm for Hoverable UAV Path Planning

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link.

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.

Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.

Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.

Adaptive quadrotor control using online dynamic mode decomposition

Precision control of Unmanned Aerial Vehicles (UAVs) is essential for deployment in a wide range of applications. However, real-world flight conditions often deviate from ideal operating scenarios, presenting uncertainties such as external disturbances and unmodeled dynamics. These can dramatically impact tracking accuracy and stability. This study proposes a novel adaptive control technique for quadrotors based on Windowed Dynamic Mode Decomposition (DMDc) techniques. This techniques efficiently identifies dynamic models directly from data, and updates this model in real-time, allowing the controller to compensate for changing conditions. To facilitate realistic validation, the proposed system is integrated within a Hardware-in-the-Loop (HiL) framework. In a series of simulated experiments, the adaptive controller demonstrates improvement in trajectory tracking under disturbances when compared to a conventional inverse dynamics approach. This research underscores the promise of DMDc-based techniques combined with adaptive control to enhance UAV operation, enabling safer and more robust performance in demanding scenarios.

Detecting Denial of Service Attacks (DoS) over the Internet of Drones (IoD) Based on Machine Learning

The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs’ wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations.