Unmanned Aerial Vehicles Research Articles

Pre-occurrence Location-allocation-configuration of Maritime Emergency Resources Considering Shipborne Unmanned Aerial Vehicle (UAV)

Pre-occurrence Location-allocation-configuration of Maritime Emergency Resources Considering Shipborne Unmanned Aerial Vehicle (UAV)

Adaptive quantized finite-time fault-tolerant control for uncertain multi-input multi-output systems and its application

The article proposes a novel state-feedback control method for a multiple-input multiple-output (MIMO) nonlinear system with actuator faults and input quantization. The innovation of the design approach lies in the utilization of fuzzy logic systems (FLSs) to approximate the uncertain intermediate virtual control laws, thereby achieving a simplified virtual control design form. Additionally, finite-time control is employed to enhance the system’s response speed. Different from the existing literatures, the adaptive control scheme of partial loss fault gain is integrated with input quantization, which completes the unknown gain estimation and avoids the assumption condition of unknown control gain. The theoretical analysis combined with Lyapunov stability analysis shows that the tracking error can converge regardless of whether the system experiences a fault, while the closed-loop signal remains stably bounded for a finite time. Finally, the simulation results of the quadrotor unmanned aerial vehicle (UAV) attitude system indicate that this control scheme is effective.

Post-disaster distribution system restoration with unmanned aerial vehicle inspection for uncertain fault locations

Post-disaster distribution system restoration with unmanned aerial vehicle inspection for uncertain fault locations

Investigation of aerodynamic characteristics of concept wing design inspired by the sooty shearwater

Biomimetics, the practice of drawing inspiration from nature to solve engineering challenges, has gained significant traction in aerospace design, particularly in the development of more efficient wing structures. This study investigated the aerodynamic potential of concept wing designs inspired by the Sooty Shearwater (Ardenna Grisea), a seabird renowned for its long-distance migratory capabilities and energy-efficient flight patterns. By leveraging the unique wing morphology of the Sooty Shearwater, three biomimetic wing models were developed using the Goettingen 173 airfoil. These designs were tested in a wind tunnel, where force measurements and flow visualization techniques were employed to evaluate their performance. Force measurement results show that a two-stage stall occurs for both models 1 and 2, with lift coefficient (CL) reaching an intermediate value when the first step occurs. Based on flow visualization results, model 1 demonstrates enhanced aerodynamic performance relative to the other models by dividing the laminar separation bubble into two sections in the spanwise direction as a result of the large stall cell formation. The findings reveal how specific aspects of the shearwater's wing structure can be translated into unmanned aerial vehicle designs, potentially enhancing aerodynamic efficiency in low-speed, low-Reynolds-number flight regimes.

The Design of Improved Series Hybrid Power System Based on Compound-Wing VTOL

Hybrid power systems are now widely utilized in a variety of vehicle platforms due to their efficacy in reducing pollution and enhancing energy utilization efficiency. Nevertheless, the existing vehicle hybrid systems are of a considerable size and weight, rendering them unsuitable for integration into 25 kg compound-wing UAVs. This study presents a design solution for a compound-wing vertical takeoff and landing unmanned aerial vehicle (VTOL) equipped with an improved series hybrid power system. The system comprises a 48 V lithium polymer battery(Li-Po battery), a 60cc internal combustion engine (ICE), a converter, and a dedicated permanent magnet synchronous machine (PMSM) with four motors, which collectively facilitate dual-directional energy flow. The four motors serve as a load and lift assembly, providing the requisite lift during the take-off, landing, and hovering phases, and in the event of the ICE thrust insufficiency, as well as forward thrust during the level cruise phase by mounting the variable pitch propeller directly on the ICE. The entire hybrid power system of the UAV undergoes numerical modeling and experimental simulation to validate the feasibility of the complete hybrid power configuration. The validation is achieved by comparing and analyzing the results of the numerical simulations with ground tests. Moreover, the effectiveness of this hybrid power system is validated through the successful completion of flight test experiments. The hybrid power system has been demonstrated to significantly enhance the endurance of vertical flight for a compound-wing VTOL by more than 25 min, thereby establishing a solid foundation for future compound-wing VTOLs to enable multi-destination flights and multiple takeoffs and landings.

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study

Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study

Age and energy aware data collection scheme for urban flood monitoring in uav-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become pivotal in numerous applications, including environmental monitoring, precision agriculture, and disaster response. In the context of urban flood monitoring, utilizing unmanned aerial vehicles (UAVs) presents unique challenges due to the dynamic and unpredictable nature of the environment. The primary challenges involve designing strategies that maximize data collection while minimizing the Age of Information (AoI) to ensure timely and accurate decision-making. Efficient data collection is crucial to capturing all relevant information and providing a comprehensive understanding of flood dynamics. Simultaneously, reducing AoI is essential, as outdated data can lead to delayed or incorrect responses, potentially worsening the situation. Addressing these challenges is critical for the effective use of WSNs in urban flood monitoring. Initially, we formulate the problem as a mixed integer non-linear programming (MINLP) problem. Further, it is solved using a Lagrangian-based branch and bound technique by converting it into an unconstrained problem. Then, for large-scale WSN, we propose a hybrid optimization technique which combines a genetic algorithm with a particle swarm optimization technique to simultaneously maximize the data collection and reduce the AoI of the collected data with the constraint of energy consumption of the UAVs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of both data collection and AoI.

Implementation of Unmanned Aerial Vehicles in the Automated Assessment of Geodetic Database Validity

Implementation of Unmanned Aerial Vehicles in the Automated Assessment of Geodetic Database Validity

Novel Nuclear Power and Fossil Fuel Hybrid Propulsion Systems for Long-Endurance Unmanned Aerial Vehicles: Configuration Comparison and Performance Optimization

Aircraft nuclear propulsion system can significantly enhance the endurance, but its large size and weight become disadvantages against the conventional fossil fuel propulsion system. In this paper, a novel nuclear power and fossil fuel hybrid propulsion system, which combines the lithium-cooled fast reactor and the kerosene-fueled turbine engine, is proposed to meet the power requirements of different flight segments. Performance assessment and comparison among different hybrid configurations is performed. Results indicate that the configuration combining the nuclear power module with a high bypass ratio turbofan engine is the most promising for aircraft. The primary optimization shows that there is a trade-off between the weight of the nuclear power module and the specific thrust, and the optimal weight of nuclear power module is 3942 kg, with the specific thrust of 195.8 N·kg-1·s-1. The aircraft equipped with the hybrid propulsion system operates at the maximum altitude of approximately 18.2 km and reaches a maximum Mach number of 0.71, allowing for sustained flight for several months in cruise segment. The turbofan engine, nuclear reactor, ancillary systems, shield, and heat exchanger account for 11%, 23%, 11%, 17% and 1% of the fuel mass of the original unmodified aircraft.

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

Distributed Neuroadaptive Formation Control for Aerial Base Station-Assisted Hovercraft Systems with Mixed Disturbances

Effectively addressing the formation control of ABS-assisted hovercraft systems with heterogeneities, unavailable leaders’ convex combination states, nonlinearities, and mixed disturbances poses significant challenges. This paper proposes a distributed neuroadaptive formation tracking strategy of ABS-assisted hovercraft systems for the first time, where aerial base stations (ABSs) are composed of unmanned aerial vehicles (UAVs) for data distribution and computation offloading. Firstly, UAVs are designed to track the virtual-leader while shaping a fixed formation, and the observer is devised for each follower hovercraft to estimate the convex combination states of UAVs. Then, output regulation equations are employed to transform heterogeneous systems into a compact form via the Kronecker product, while neural networks (NNs) are introduced to compensate for model nonlinearities. Furthermore, based on random differential equations (RDEs) combined with Lyapunov theory, the noise-to-state practical stability in probability (NSPS-P) property of the error dynamics under mixed disturbances can be obtained. Finally, simulation examples demonstrate that the outputs of follower hovercrafts rapidly achieve a time-varying formation and rotate around convex combination states of leader UAVs simultaneously.

Estimating Summer Maize Biomass by Integrating UAV Multispectral Imagery with Crop Physiological Parameters.

The aboveground biomass (AGB) of summer maize is an important indicator for assessing crop growth status and predicting yield, playing a significant role in agricultural management and decision-making. Traditional on-site measurements of AGB are limited, due to low efficiency and a lack of spatial information. The development of unmanned aerial vehicle (UAV) technology in agriculture offers a rapid and cost-effective method for obtaining crop growth information, but currently, the prediction accuracy of summer maize AGB based on UAVs is limited. This study focuses on the entire growth period of summer maize. Multispectral images of six key growth stages of maize were captured using a DJI Phantom 4 Pro, and color indices and elevation data (DEM) were extracted from these growth stage images. Combining measured data such as summer maize AGB and plant height, which were collected on the ground, and based on the three machine learning algorithms of partial least squares regression (PLSR), random forest (RF), and long short-term memory (LSTM), an input feature analysis of PH was carried out, and a prediction model of summer maize AGB was constructed. The results show that: (1) using unmanned aerial vehicle spectral data (CIS) alone to predict the biomass of summer maize has relatively poor prediction accuracy. Among the three models, the LSTM (CIS) model has the best simulation effect, with a coefficient of determination (R2) ranging from 0.516 to 0.649. The R2 of the RF (CIS) model is 0.446-0.537. The R2 of the PLSR (CIS) model is 0.323-0.401. (2) After adding plant height (PH) data, the accuracy and stability of model prediction significantly improved. R2 increased by about 25%, and both RMSE and NRSME decreased by about 20%. Among the three prediction models, the LSTM (PH + CIS) model had the best performance, with R2 = 0.744, root mean square error (RSME) = 4.833 g, and normalized root mean square error (NRSME) = 0.107. Compared to using only color indices (CIS) as the model input, adding plant height (PH) significantly enhances the prediction effect of AGB (aboveground biomass) prediction in key growth periods of summer maize. This method can serve as a reference for the precise monitoring of crop biomass status through remote sensing with unmanned aerial vehicles.

ПОВЫШЕНИЕ ДЛИТЕЛЬНОСТИ ПОЛЕТА БЕСПИЛОТНИКОВ СЕЛЬСКОХОЗЯЙСТВЕННОГО НАЗНАЧЕНИЯ

The analysis and review of methods for solving the problem of short flight duration of unmanned aerial vehicles (UAVs) for agricultural purposes is performed. The state of the issue of insufficient flight time and the need to charge drones to ensure full automation and continuity of the work process is studied. The main types and models of batteries used in UAVs as energy sources are characterized. The main methods of their refueling are considered in a comparative aspect. Particular attention is paid to the study of the advantages and disadvantages of technical solutions to the designated problem. Such methods as: using an automatic charging station for drones, designing drones called "flying batteries", using a laser and a high-power electromagnetic field are noted and described. A method for increasing the flight duration of a drone by installing a payload and a power source on an accompanying vehicle (TS) is proposed. It is recommended to provide a wired connection between the drone and the TS for charging the drone and refueling it with chemical liquids for spraying crops. The following advantages are noted: continuous power supply to the drone from the vehicle, increased duration of spraying without refueling with chemicals, the ability to perform various tasks by drones, no need for expensive drones due to the possibility of using low-power engines due to the location of power sources on the vehicle. To solve the above problem, an automated device for replacing UAV batteries is proposed, which will increase the flight duration. This device consists of two drums for batteries in which feed springs are installed, a control module, a drum shaft drive, and a platform for automatic drum change and battery charging.

Optimizing Wing-In-Ground Effect UAVS for Enhanced Search and Rescue Operations: A Comprehensive Review

Abstract: Wing-In-Ground Effect (WIG) Unmanned Aerial Vehicles (UAVs) represent a unique convergence of aerodynamic principles and unmanned systems technology, offering promising solutions for maritime operations. This paper explores the historical development, theoretical foundations, and potential applications of WIG UAVs. The phenomenon of ground effect, which enhances lift and reduces drag when flying close to water surfaces, has been observed since the early 20th century. Soviet engineer Rostislav Alexeyev's pioneering work on the Lun-class ekranoplan in the 1960s demonstrated the viability of WIG technology for military logistics. Advancements in materials science, propulsion systems, and computer-aided design have further refined WIG vehicle performance. The integration of autonomous capabilities has expanded the operational scope of WIG UAVs, enabling missions in challenging maritime environments. The aerodynamic investigation of WIG vehicles reveals the complex interplay between the vehicle's wings and the water surface, leading to enhanced lift-to-drag ratios and improved efficiency. Computational Fluid Dynamics (CFD) simulations and wind tunnel experiments have provided valuable insights into the optimization of WIG UAV designs. The potential applications of WIG UAVs span maritime surveillance, coastal patrolling, search and rescue operations, and cargo transportation. Their ability to collaborate with other unmanned systems, such as aerial drones and surface vessels, enhances overall mission effectiveness. As the demand for sophisticated maritime capabilities grows, the development of WIG UAVs presents a promising frontier for innovation, offering unique solutions to address evolving challenges in maritime security and operational effectiveness

PII-GCNet: Lightweight Multi-Modal CNN Network for Efficient Crowd Counting and Localization in UAV RGB-T Images

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment.

AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging

Illegal logging poses a significant threat to environmental conservation, contributing to deforestation, ecosystem disruption, and economic losses. Traditional detection methods, such as ground patrols and satellite imagery, are often ineffective, expensive, and time-consuming. This paper introduces a novel Unmanned Aerial Vehicles (UAV)-based IoT system for chainsaw noise detection and prevention. The system uses low-cost IoT nodes with microphones and microcontrollers to detect chainsaw sounds via a Convolutional Neural Network (CNN) model. These nodes reduce power consumption, allowing the battery to last at least 8 years. Detected chainsaw sounds are transmitted to a LoRaWAN gateway and forwarded to a cloud server. The cloud server processes the data and dispatches a drone equipped with a camera to the site. The drone streams live video to the cloud, where AI algorithms analyze the footage for human presence. If detected, the drone activates sound and light alarms to deter illegal logging. Experimental results show that IoT nodes can detect chainsaw sounds up to 100 m, with the CNN model achieving an accuracy of 99.37 % and a loss of 0.0185. Comparisons with Logistic Regression, Decision Tree, Random Forest, and SVM demonstrated the superior performance of CNN. This UAV-IoT system offers a fast, reliable, scalable, and real-time solution for detecting and preventing illegal logging. Leveraging advanced AI and IoT technologies, this system enhances environmental monitoring and conservation efforts. It represents a significant step forward in the fight against illegal logging, providing a practical, effective, and sustainable solution.

A Novel Robust Position Integration Optimization-Based Alignment Method for In-Flight Coarse Alignment.

In-flight alignment is a critical milestone for inertial navigation system/global navigation satellite system (INS/GNSS) applications in unmanned aerial vehicles (UAVs). The traditional position integration formula for in-flight coarse alignment requires the GNSS velocity data to be valid throughout the alignment period, which greatly limits the engineering applicability of the method. In this paper, a new robust position integration optimization-based alignment (OBA) method for in-flight coarse alignment is presented to solve the problem of in-flight alignment under a prolonged ineffective GNSS. In this methodology, to achieve a higher alignment accuracy in case the GNSS is not effective throughout the alignment period, the integration of GNSS velocity into the local-level navigation frame is replaced by the GNSS position in the Earth-centered, Earth-fixed frame, which avoids the need for complete GNSS velocity data. The simulation and flight test results show that the new robust position integration method proposed in this paper achieves higher stability and robustness than the conventional position integration OBA method and can achieve an alignment accuracy of 0.2° even when the GNSS is partially time-invalidated. Thus, this greatly extends the application of the OBA method for in-flight alignment.

Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers

Cooperative MIMO communication systems play an important role in the development of future sixth-generation (6G) wireless systems incorporating new technologies such as massive MIMO relay systems, dual-polarized antenna arrays, millimeter-wave communications, and, more recently, communications assisted using intelligent reflecting surfaces (IRSs), and unmanned aerial vehicles (UAVs). In a companion paper, we provided an overview of cooperative communication systems from a tensor modeling perspective. The objective of the present paper is to provide a comprehensive tutorial on semi-blind receivers for MIMO one-way two-hop relay systems, allowing the joint estimation of transmitted symbols and individual communication channels with only a few pilot symbols. After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). Some new variants of nested models are introduced. Uniqueness and identifiability conditions, depending on the algorithm used to estimate the parameters of these models, are established. Two families of algorithms are presented: iterative algorithms based on alternating least squares (ALS) and closed-form solutions using Khatri–Rao and Kronecker factorization methods, which consist of SVD-based rank-one matrix or tensor approximations. In a second part of the paper, the overview of cooperative communication systems is completed before presenting several two-hop relay systems using different codings and configurations in terms of relaying protocol (AF/DF) and channel modeling. The aim of this presentation is firstly to show how these choices lead to different nested tensor models for the signals received at destination. Then, by capitalizing on these models and their correspondence with the generic models studied in the first part, we derive semi-blind receivers to jointly estimate the transmitted symbols and the individual communication channels for each relay system considered. In a third part, extensive Monte Carlo simulation results are presented to compare the performance of relay systems and associated semi-blind receivers in terms of the symbol error rate (SER) and channel estimate normalized mean-square error (NMSE). Their computation time is also compared. Finally, some perspectives are drawn for future research work.

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the Acute Angle Test (AAT) is proposed,
which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighborselection mechanism can reduce the redundant communication burden between large scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.