Unmanned Aerial Vehicle Applications Research Articles

URS-YOLOv5s: object detection algorithm for UAV remote sensing images

The application of UAV (Unmanned Aerial Vehicle) remote sensing and aerial photography technology is more and more widely. Aiming at the problem of object detection in remote sensing or aerial images, the paper proposes an object detection algorithm for UAV remote sensing images based on YOLOv5s, called URS-YOLOv5s (UAV Remote Sensing - YOLOv5s). Firstly, the paper designs Cross-Connected Dense Network (CCDNet), where each convolution layer is concatenated to the second convolution layer. Secondly, the paper designs Across-Path Fusion Network (APFNet), which the last feature fusion path is directly fused with the backbone network. It increases the location information and semantic information of deep features. Finally, the loss function of the original algorithm is replaced by EIoU (Focal and Efficient IoU), which makes the calculation method of the overall loss more appropriate. On AI-TOD and Visdrone2019 datasets, experiments show that the accuracy of URS-YOLOv5s on mAP@0.5 is 6.3% and 9% higher than YOLOv5s. In addition, compared with YOLOv3 and YOLOv5l which have good detection effect, URS-YOLOv5s has the characteristics of faster detection speed and lower computational cost. Meanwhile, URS-YOLOv5s is more suitable for deployment to mobile devices, such as drones with limited performance.

Self-powered system for real-time wireless monitoring and early warning of UAV motor vibration based on triboelectric nanogenerator

Unmanned aerial vehicle (UAV) is widely used in various industries due to their high flexibility and large maneuverable space. Abnormal vibration of UAV motors can prevent it from working properly due to the abnormal state of the motors and the harsh external environment. There is no suitable method to monitor the UAV motors vibration in real time. At the same time, commercial sensors require an additional power supply to power the sensor, which indirectly limited their application in UAVs. A vibration sensor (AV-TENG) with magnetic spring structure was proposed in this paper for monitoring the vibration of UAV motors. The experimental results show that the magnetic spring has excellent stability. AV-TENG can achieve frequency detection in a wide frequency band. It also has excellent linearity with a maximum error of only 0.0062 %. Simultaneously, to verify the performance of AV-TENG in various environments and simulate its loading, the results demonstrate that AV-TENG can adapt well to the working conditions of UAV. Finally, this paper constructs a low-cost (less than $7), easy-to-manufacture signal acquisition system, and conducts a field demonstration, realizing the monitoring of the vibration frequency and acceleration of the UAV motors, and realizing the early warning, which is expected to promote the development of the field of UAV as well as the practical application of the triboelectric nanogenerator.

Comprehensive review of unmanned aerial vehicle application to safety mining management

The increasing interest of users in unmanned aerial vehicle (UAV) technology has developed new fields of application for it. Presently, UAVs are working in so many areas and the mining industry has shown grown interest in the use of UAVs for routine operations in surface, underground mines, and mine waste dumps. In this paper, a thorough review has been performed on the application of UAVs in mine safety management. Systematic reviews were conducted to summarize the results of over 60 papers published during the 2010-2021 period. The study offers a review of the most common applications involving mine safety, the styles of flying control, and the UAV types exploited. The paper also contributes to the identification of prevalent data acquired by sensors and points out the most popular processing techniques of aerial imagery. The obtained results revealed that UAVs are used at mining sites for safety purposes including mine surface displacements, stability monitoring, working environment, coal fire identification, drill and blast, and mine waste dump operations. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring to manage mine safety, as well as an assessment of applications in mining activities.

Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

Autopilot drone in construction: A proof of concept for handling lightweight instruments and materials

Applications of Unmanned Aerial Vehicles (UAVs) in construction have been increased in recent years mainly for purposes of automatically collecting field data. In the market, there is a growing opportunity for using UAVs in material handling. On the other side, vertical transportation in construction sites is a crucial challenge that leads to a drop in productivity efficiency. This paper presents a proof of concept for automating the lifting procedure of lightweight instruments and materials in construction sites. To do so, a platform is developed that could automatically manage material/instrument handling requests via an autopilot drone. The aircraft position is tracked with an onboard GPS module which is not accurate in urban areas. So inaccurate GPS signals could deviate the drone from the predefined path. To tackle this problem, a machine vision algorithm is utilized to calibrate the real-time position of the aircraft. A self-supervised learning algorithm is integrated into the system to handling collision avoidance during the flight. Finally, the developed methods are tested with an UAV in a real world environment to demonstrate the potentials of hiring autopilot drones in construction.

Investigation on situation awareness model of unmanned aerial vehicle groups communication network based on adversarial network

With the widespread application and development of unmanned aerial vehicle (UAV) technology, ensuring the security and stability of UAV swarm communication networks has become crucial. Given the diverse forms of interference and attacks in current networks, this poses a serious threat to the normal operation of UAV swarm communication. Therefore, how to accurately identify and effectively counter these network threats has become the focus of research. This study comprehensively evaluates the core technology of UAV swarm communication network situational awareness and constructs a situational awareness model based on adversarial networks. The model utilizes adversarial network technology and combines data collection and processing to design four experiments to comprehensively evaluate the performance of the model in different scenarios. The experimental results show that as the amount of data gradually increases, the performance of the model also improves. When processing 100, 1000, and 10,000 data points, the model achieved accuracies of 0.955, 0.962, and 0.982, respectively. Furthermore, the experimental results also indicate that effective noise suppression measures can significantly improve the accuracy and stability of the situational awareness model. Additionally, it is noted that different model structures will affect training duration, accuracy, and stability. Although increasing network scale may lead to increased computational complexity and latency, its accuracy is correspondingly improved. The adversarial network-based situational awareness model proposed in this study can accurately identify and effectively counter interference and attacks in UAV swarm communication networks, thereby providing solid protection for the collaborative combat and information sharing of UAV swarms.

Trends of Unmanned Aerial Vehicles in Logistics Delivery

The integration of unmanned aerial vehicles (UAVs) in logistics operations has attracted significant attention due to their potential to revolutionize delivery processes. This abstract provides an overview of the trends and advancements in utilizing UAVs for logistics. The study explores the current research landscape, identifies key challenges, and offers insights into the future applications of UAVs in logistics. By analyzing a range of scholarly articles, this abstract aims to illuminate the evolving role of UAVs in optimizing delivery efficiency, reducing costs, and addressing logistical complexities. Additionally, the abstract highlights the necessity for further research to address emerging issues and maximize the benefits of UAV technology in the logistics sector.

Первые результаты инфракрасной съемки с БПЛА термальных объектов Курильских островов в 2023 г.

For monitoring hard-to-reach thermal facilities, remote surveillance techniques from unmanned aerial vehicles (UAVs) equipped with a thermal infrared camera represent a progressive platform for rapid information collection. This paper presents the first results of such an application of UAVs to study five thermal objects on the Kuril Islands. Four of them (Kipyashchaya Riwer on Iturup Island, Lake Biryuzovoye on Simushir Island, Yankich Island and Yurieva River on Paramushir Island) are associated with active volcanoes. Heat maps with precise geographical reference have been obtained for all sections of gas hydrothermal discharge. For Kipyashchaya River and Yankich Island verification of air thermometry by ground data was performed and real atmospheric attenuation coefficients were obtained for different flight altitudes under observed meteorological conditions. The first data on the parameters of the thermal plume from the hot Yurievа River flowing into the Sea of Okhotsk were obtained.

A bioinspired triboelectric wireless anemometer with low cut-in wind speed for meteorological UAVs

Currently, unmanned aerial vehicles (UAVs) play a pivotal role in meteorological wind speed measurements. Nevertheless, existing anemometers exhibit notable issues, including excessive weight, high cost and high cut-in wind speed. This investigation introduces a biomimetic owl wing airfoil wind speed sensor utilizing the principles of the triboelectric nanogenerator (BOW-TENG) designed for UAV applications. Inspired by owl wings, an airfoil configuration with superior aerodynamic characteristics is ascertained, subsequently fabricating it into an impeller, and a wind speed sensor with low weight, low cost and low cut-in wind speed is developed. Experimental results affirm the BOW-TENG’s precision in depicting wind speed across the range of 1.6–10.7 m/s, featuring a sensitivity of 21.893 Hz/(m·s−1), a goodness of fit R2 of 0.9995, and a remarkable resolution of 0.057 m/s. The sensor weighs only 30 g. Furthermore, a cost-effective triboelectric signal processing system is devised for integrating the BOW-TENG into the UAV, as well as the optimal positioning for BOW-TENG on the UAV is identified through simulations. The relative wind direction can be calculated from signals of the BOW-TENGs on the four arms of the UAV. Ultimately, application experiments demonstrate the feasibility of employing BOW-TENG for wireless wind speed transmission from UAVs. This work incorporates bionics and proposes a practical application of triboelectric sensors for UAVs, as well as a solution for developing wind speed sensors in the field of meteorological UAV detection.

Lift–Drag Performance of a New Unmanned Aerial Vehicle in Different Media and Ground Effect

Water–air trans-media unmanned vehicle is a kind of aircraft, which can freely fly in the air, sail in the water and pass through free surface. For trans-media aircrafts, the development process from air–surface to air–underwater and from submarine-launched drive to autonomous drive is investigated. By analyzing the characteristic of manta ray, flying fish and existing aircraft, this paper proposes a new water–air trans-media unmanned vehicle with flat dish-airfoil-shaped main body and telescopic NACA-type wing. Then the numerical method to calculate the lift and drag forces is established and validated by the results of classic NACA cases. On this basis, the flow field around the new vehicle is numerically simulated, and its lift–drag performances in different media (air and water) and ground effect are analyzed, comparing it with a model inspired by the Blackwing Unmanned Aerial Vehicle (UAV). The findings illustrate the superior performance of the new vehicle in terms of lift and drag forces, offering an innovative design framework for water–air trans-media UAV applications.

The Progress and Trend of UAV Power Line Inspection: A Bibliometrics-based Visualization Analysis

<p> Background: The application of Unmanned Aerial Vehicle (UAV) is a major turning point in the history of power line inspection and is of increasing interest to a growing number of researchers. </p><p> Objective: This study aimed to clarify the research status, hotspots, and evolutionary trends of UAV power line inspection, to help researchers understand the dynamic evolution of research topics and provide guidance for future research directions. </p><p> Methods: 737 high-quality papers were collected from the Web of Science Core Collection (WoSCC) database from 2001 to 2023, and descriptive statistical analysis, cooperation network analysis, keyword co-occurrence analysis, keyword clustering analysis, and keyword citation burst analysis were conducted using VOSviewer and CiteSpace. </p><p> Results: The popularity of research in this field is increasing. Chinese academic institutions and scholars have made outstanding contributions. The level of cooperation between different countries, academic institutions, and scholars is low. The research on UAV power line inspection has a wide range of topics and obvious interdisciplinary characteristics, mainly focusing on fault detection and diagnosis, path planning, and the application of deep learning in intelligent inspection. The research hotspot and trend is the integration of artificial intelligence, deep learning, and modern information technology to achieve UAV autonomous intelligent inspection. Some of the challenges faced in the development of the field are also summarized and possible solutions are proposed. </p><p> Conclusion: This study provides a comprehensive and systematic review, and the results can provide a quick overview of the research status, hotspots, and evolutionary trends.

Numerical simulation of agricultural unmanned helicopter corn canopy spraying

Plant protection by unmanned aerial vehicle (UAV) is a highly complicated process, especially considering the vast and diversified crop canopy. Furthermore, the droplet dispersal mechanism above and inside the crop canopy remains unclear. Owing to this, the authors used the lattice Boltzmann method (LBM) to simulate the application of plant protection UAV. The LBM is suitable not only to simulate the rotor tip vortex of UAV but also to model the crop canopy with detailed geometry. It is found that the streamwise and spanwise speed of the downwash flow achieve an extreme value both on top of the corn canopy and near the ground. When the droplet diameter decreased from 400 to 50 μm, its deposit region width doubled and its floating time increased from 3 s to more than 12 s. Small droplets are more likely to be lifted by the secondary flow near the ground and drift for a longer distance, while larger droplets are deposited directly. The increase in application height weakened the secondary spanwise flow near the ground and caused less droplet re-lifting and spanwise movement. The best point to initiate and terminate spraying is 2.5 m after passing the target corn canopy region. The droplet penetration rate in the canopy decreased with an increase in droplet size. This study used the LBM to model the unmanned helicopter application on a full corn canopy. Droplet deposition and distribution correspond to droplet size and canopy structure. The simulation results establish a foundation to optimize agricultural aerial spraying in an efficient and methodical manner.

Adaptive maximum available energy evaluation for lithium battery in hydrogen-electric hybrid unmanned aerial vehicle applications considering dynamic ambient temperature and aging level

Accurate evaluation of the maximum available energy (MAE) of the lithium battery is crucial for energy management and optimization in hydrogen-electric hybrid unmanned aerial vehicle (UAV) applications. However, uncertainties in ambient temperature and cell aging level would affect the MAE of the lithium battery, posing challenges for its accurate state evaluation. This paper presents an adaptive MAE evaluation method based on an optimized sliding mode observer (OSMO) for the lithium battery. The study begins by conducting comprehensive long-term experimental tests to analyze the dependencies of the lithium battery’s MAE on different ambient temperatures and cell aging levels. Subsequently, an OSMO with a Kalman filter is developed to estimate the variations of the battery’s open circuit voltage (OCV) on the basis of a dynamic lithium battery model. Furthermore, the real-time OCV is successfully used to achieve accurate adaptive MAE evaluation for the lithium battery. The originality of this paper is that the model parameters and proportional coefficients can be updated adaptively based on the OSMO with the Kalman filter. In this case, the OCV can converge to their actual value such that accurate MAE evaluation can be guaranteed, even with uncalibrated model parameters under dynamic ambient temperature and uncertain aging levels. Finally, experimental and simulation results verify that the proposed method exhibits a convergence speed four times higher than the traditional method. The mean absolute percentage error (MAPE) of MAE evaluation is less than 1.5 % under dynamic ambient temperature and uncertain aging levels.

EUAVDet: An Efficient and Lightweight Object Detector for UAV Aerial Images with an Edge-Based Computing Platform

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.

Optimization of Endurance Performance for Quadrotor Unmanned Aerial Vehicles Driven by a Hybrid System of Solar Photovoltaic Cells and Energy Storage Batteries

The potential applications of unmanned aerial vehicles (UAVs) are vast and exciting, yet their scope could be significantly expanded if their flight endurance could be extended. The primary objective of this study is to design a hybrid power system combining solar energy and lithium batteries to enhance the endurance and energy management efficiency of unmanned aerial vehicles (UAVs). Through comparative analysis of series, parallel, and hybrid power systems, a parallel hybrid power system was chosen as the power solution for UAVs. In this paper, we detail the selection process for solar panels and lithium batteries and elucidate the design of a solar endurance enhancement system, including the application of anti-backflow and voltage stabilization modules. Additionally, we propose an energy management strategy based on finite state machines, which ensures efficient and stable operation of the system through real-time monitoring and dynamic adjustment. The methodology of this study provides an innovative and practical framework for the design and implementation of solar and energy storage battery hybrid energy management systems for UAVs.

Depth-Guided Dehazing Network for Long-Range Aerial Scenes

Over the past few years, the applications of unmanned aerial vehicles (UAVs) have greatly increased. However, the decrease in clarity in hazy environments is an important constraint on their further development. Current research on image dehazing mainly focuses on normal scenes at close range or mid-range, while ignoring long-range scenes such as aerial perspective. Furthermore, based on the atmospheric scattering model, the inclusion of depth information is essential for the procedure of image dehazing, especially when dealing with images that exhibit substantial variations in depth. However, most existing models neglect this important information. Consequently, these state-of-the-art (SOTA) methods perform inadequately in dehazing when applied to long-range images. For the purpose of dealing with the above challenges, we propose the construction of a depth-guided dehazing network designed specifically for long-range aerial scenes. Initially, we introduce the depth prediction subnetwork to accurately extract depth information from long-range aerial images, taking into account the substantial variance in haze density. Subsequently, we propose the depth-guided attention module, which integrates a depth map with dehazing features through the attention mechanism, guiding the dehazing process and enabling the effective removal of haze in long-range areas. Furthermore, considering the unique characteristics of long-range aerial scenes, we introduce the UAV-HAZE dataset, specifically designed for training and evaluating dehazing methods in such scenarios. Finally, we conduct extensive experiments to test our method against several SOTA dehazing methods and demonstrate its superiority over others.

Structural Analysis and Testing of a Flexible Rudder Using a Cosine Honeycomb Structure

This paper introduces a new type of flexible rudder surface based on the cosine-type zero Poisson’s ratio honeycomb to enhance the adaptive capabilities of aircraft and enable multi-condition, rudderless flight. The zero Poisson’s ratio honeycomb structure exhibits exceptional in-plane and out-of-plane deformation capacities, as well as a high load-bearing capability. To investigate the deformation characteristics of flexible rudder surfaces utilizing cosine honeycomb structures, this study undertakes a comprehensive investigation through finite element simulation and 3D printing experiments. Moreover, this study analyzed the impact of honeycomb parameters and layout on the deflection performance and weight. The flexible rudder surface, fabricated from nylon, achieves smooth and consistent chordwise bending deformation, as well as uniform spanwise deformation within a tolerance of ±25°, and the maximum equivalent stress observed was 31.99 MPa, which is within the material’s allowable stress limits (50 MPa). Finite element simulation results indicate that once the deflection angle of the rocker exceeds 15°, a discernible deviation arises between the actual deflection angle of the flexible control surface and that of the rocker. Furthermore, this deviation escalates with increasing rocker rotation angles, and this discrepancy can be mitigated by augmenting the number of cosine honeycomb cells within the flexible rudder surface. Finally, a prototype of the flexible rudder surface was successfully produced using 3D printing technology, and the experimental results confirmed the deformation behavior, aligning with simulation outcomes with a deviation of less than 20%. These findings confirm the effective deflection performance of the designed flexible rudder surface, highlighting its potential application in small unmanned aerial vehicles.

Comparison of urban physical environments and thermal properties extracted from unmanned aerial vehicle images and ENVI-met model simulations

Identifying the thermal properties of urban areas and integrating these properties into spatial planning is essential for sustainable development. Continuous and efficient improvements of urban thermal environments require novel studies, which are limited to date. This study addresses this gap by presenting a comparison of the physical environments and thermal properties of an urban area (Changwon National University, Gyeongsangnam-do, South Korea) based on unmanned aerial vehicle (UAV) images and ENVI-met model simulations. We predicted the thermal environment of the study area using UAV images and explored the potential application of UAVs in spatial planning. The comparison revealed that the coefficient of determination (R2) between mean radiant temperature (Tmrt) values simulated by the ENVI-met model and predicted from UAV images was 0.168–0.320. Significant differences were observed in shaded areas and vegetated regions with tall trees, where UAVs encountered difficulties in collecting accurate information due to obstructed visibility. The discrepancies between the simulated and predicted Tmrt values highlight the limitations of UAVs in capturing data in shaded and densely vegetated areas, as well as disparities in the physical environments determined by the two methods. Despite these limitations, UAVs can be valuable tools for urban and environmental planning. Future advancements addressing these limitations may enable UAVs to efficiently diagnose urban thermal environments and guide improvement efforts.

Sensing and Control Integration for Thrust Vectoring in Heavy UAVs: Real-World Implementation and Performance Analysis

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.