Unmanned Aerial Vehicle Applications Research Articles

Study of the application of UAV and LiDAR to topographic survey of mountainous forest areas – Case study of Win 3 Wind Power Plant Project, Huong Hoa District, Quang Tri Province, Vietnam

Abstract Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.

Research on the Technology and Application of Remote Wireless Transmission of Digital Image Signal

Abstract. With the increasing demand for remote real-time high-quality video transmission, especially in the context of large area applications of unmanned aerial vehicle (UAV) and remote location instant live broadcast, traditional wired transmission methods have been surpassed by digital transmission methods due to their superior performance in image quality, transmission distance and delay. Therefore, this study focuses on the application of digital image wireless transmission in remote scenarios such as UAVs, including key digital transmission technologies such as Orthogonal Frequency Division Multiplexing (OFDM) and Coded Orthogonal Frequency Division Multiplexing (COFDM), as well as public network link communication technologies such as 4G and 5G, by means of a literature study and a case study. It also analyzes the development of WiFi, Lightbridge and OcuSync graphics transmission systems using DJI brand products as case studies. The analysis shows that digital image wireless transmission, especially COFDM and advanced proprietary systems such as DJI's OcuSync, has significant advantages in terms of transmission distance, interference immunity, encryption techniques, and reusability. However, factors such as building obstruction, frequency interference, and weather conditions can affect the transmission quality. To mitigate these challenges, solutions are proposed, such as auxiliary signal enhancement systems, adaptive frequency hopping and software-defined radio technologies.

A Unified Collision Avoidance Trajectory Planning with Dual Variables for Collaborative Aerial Transportation Systems

As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs.

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

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

LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

Optimizing Olive Harvesting Efficiency Through UAVs and AI Integration

Olive harvesting, integral to global agriculture, faces challenges with traditional gathering methods which are labor-intensive and limited in precision, highlighting the need for innovative approaches. The study explores the application of Unmanned Aerial Vehicles (UAVs) in olive harvesting, with the aim to enhance efficiency and sustainability. Traditional methods, employing wood rakes, sticks, and rudimentary mechanical devices, pose challenges with high labor intensity and potential tree damage. UAVs present a transformative solution, offering aerial surveying, high-resolution imaging, and harvest timing data collection. This study evaluates existing olive harvest technologies, illuminating the superior efficiency of UAVs in minimizing damage and maximizing yield. The Potohar region in Pakistan exemplifies one area of intensive olive production, emphasizing the need for precision Agri-techniques. This study introduces the conceptual design of olive harvesting using drones, demonstrating its potential to revolutionize traditional practices, provide real-time monitoring, and enhance environmental sustainability. Results indicate substantial reductions in non-harvestable fruit and wood damage with drone-based harvesting, underscoring its promise for increased yield and reduced environmental damage. In conclusion, this study advocates for the adoption of precision agriculture techniques, integrating Artificial Intelligence and aerial technologies, to propel the olive industry's productivity and environmental impact forward.

A systematic literature review on the role of UAV-enabled communications in advancing the UN’s sustainable development goals

Unmanned Aerial Vehicles (UAVs), commonly known as drones, show great potential in advancing the United Nations (UN) Sustainable Development Goals (SDGs), a collection of 17 worldwide targets outlined by the United Nations (UN) to tackle diverse social, economic, and environmental issues and foster sustainable development by 2030. As per the Federal Aviation Administration (FAA), UAVs can significantly improve areas of sustainability like wildlife and wildfire monitoring, precision agriculture, as well as healthcare. Furthermore, drones with communication capabilities are seen as key elements of non-terrestrial networks, and as enabling technologies for future 6G communication networks. This systematic literature review focuses on the role of UAV-enabled communications to achieve the SDGs. Our study is based on a thorough selection and analysis of scientific articles. Initially, we performed a broad search across three major databases: IEEE Xplore, Web of Science, and Scopus, yielding a set of 10,499 papers. Following a strict selection methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis - PRISMA), we narrowed this set down to 28 key journal articles. Our analysis highlights six global targets that greatly benefit from UAV-enabled aerial communication. In order of impact, these are: “Sustainable Cities and Communities,” where UAVs improve security and disaster management; “Life on Land,” where drones help with wildlife monitoring and reforestation; “Industry, Innovation, and Infrastructure,” where UAVs (Unmanned Aerial Vehicles) aid in infrastructure inspection; “No Hunger,” where drones improve agricultural practices; “‘Good Health and Wellbeing,” where UAVs assist with disease prediction and monitoring; and “Clean water and sanitation,”where drones play a crucial role in efficiently monitoring water quality and distribution. In summary, this paper offers a detailed look at the applications of UAVs in achieving the SDGs through aerial communications. By identifying areas of major impact and spotting gaps and weaknesses in current research, this review article aims to guide future research and policy-making in sustainable drone applications.

A Comprehensive Review of Unmanned Aerial Vehicle (UAV) Sprayers Used in Modern Agriculture

Global demand for food is increasing daily due to increase in population growth. Hence to meet these food demands for the growing population crop productivity must be enhanced. Plant pests and diseases are major threats to crop productivity causing an annual reduction of 20 to 40% in global crop production. For management of these pest and diseases spraying using manual sprayers is quite laborious and also not precise. Further manual spraying is hazardous to farmer’s health, World Health Organization (WHO) reported one million incidences of illness as a result of manually application of insecticides to crop fields. As a result, new automated drone technology techniques were unveiled. This review examines the application of Unmanned Aerial Vehicle (UAV) sprayers in modern agriculture, highlighting their transformative role in enhancing crop management and increasing efficiency. UAV technology has emerged as a vital tool for precision agriculture, offering benefits such as targeted pesticide and fertilizer application, reduced labor costs, and minimized environmental impact. This paper discusses the various types of UAV sprayers, their operational mechanisms, and the advantages they provide over traditional methods. Additionally, we explore challenges associated with UAV adoption, including regulatory issues, technical limitations, and the need for operator training. The review emphasizes the future potential of UAV sprayers in sustainable agriculture, suggesting that continued advancements in technology and regulatory frameworks will further integrate UAVs into mainstream agricultural practices.

Data-Driven Fault Detection and Diagnosis for UAV Swarm Systems

With the widely application and promotion of Unmanned aerial vehicle (UAV) technology, UAV swarms are widely used in military and civilian fields by taking advantage of group collaboration. It has huge economic and national defense value. The safety and reliability requirements of the UAV swarm system are extremely strict, and real-time fault detection and diagnosis is one of its important supporting technologies. In this paper, a fault diagnosis method based on statistical model and improved broad learning system (BLS) is proposed. The behavior characteristics of the UAV swarm system under normal and different failure modes are characterized by multivariate data statistical analysis, and the improved BLS model is adopted to achieve accurate and fast fault diagnosis. And a high-fidelity simulation verification platform is developed to verify the rationality and effectiveness of the proposed method.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

A review of the application development and key technologies of rotary engines under the background of carbon neutrality

Rotary engines possess advantages such as light weight, high power density, and strong fuel adaptability, which are essential for powertrain systems. They hold great potential in the field of power machinery. However, the development of rotary engines has been constrained by increasingly stringent emission regulations. Despite this, numerous scholars and institutions continue to optimize rotary engines, leading to a proliferation of related research. It is necessary to synthesize these findings to guide future promotion and application efforts. This review examines the current applications and development status of rotary engines globally. It summarizes the development of key technologies such as sealing techniques, rotor configurations, and ignition devices, which have achieved lower energy consumption and emissions. The suitability of rotary engines for automotive and unmanned aerial vehicle applications has also been reported. To further reduce carbon emissions, researchers have explored zero-carbon fuel rotary engines. Hydrogen-fueled rotary engines have demonstrated higher combustion efficiency. By integrating EGR, load control strategies, and ignition system layouts, they have addressed issues such as NOx emissions, knock, and leakage. The development of ammonia-hydrogen hybrid fuels is also a potential effective solution. Continuous innovative research indicates that, with their inherent advantages and the integration of alternative fuels and advanced ignition technologies, rotary engines have the potential to meet future energy and environmental requirements. This positions them as strong contenders in future diversified power systems.

The efficiency of unmanned aerial vehicles application for rapeseed productivity in Ukraine

The efficiency of unmanned aerial vehicles application for rapeseed productivity in Ukraine

Survey on Unmanned Aerial Vehicle Communications for 6G

Although the application of the fifth-Generation (5G) mobile communication has brought tremendous innovations to the daily life of human beings, e.g., autonomous vehicles and internet of everything, the upcoming huger data requirement leads to the emergence of the sixth-Generation (6G) mobile communication. Compared to 5G, the transmission rate, time delay, and wireless coverage need to be improved significantly. Thus, in this paper the applications of Unmanned Aerial Vehicles (UAVs) to the ubiquitous, intelligent and coupling 6G network are surveyed. First, the utilization of UAVs in the framework of space-air-ground-sea integrated network is demonstrated, and the roles and functions of UAVs in different scenarios are emphasized, e.g., the swarm base stations, the deployment for holographic projection, the long-distance relaying and the data collection. Then, the potential 6G key techniques of terahertz, ultra-massive multiple-input and multiple-output, endogenous artificial intelligence, Intelligent Reflecting Surface (IRS), intelligent edge computing, blockchain and integrated sensing and communication for UAV communications are investigated. Finally, the future challenges of UAV communications for 6G, including the limited duration, integration of networks, compatibility of IRS, development of THz communications, and user security are discussed.

A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.

Effects of Spray Adjuvants on Droplet Deposition Characteristics in Litchi Trees under UAV Spraying Operations

In the last decade, unmanned aerial vehicles (UAVs) for plant protection have rapidly developed worldwide as a new method for pesticide application, especially in China and other Asian countries. To improve the deposition quality in UAV applications, adding appropriate types of spray adjuvants into pesticide solutions is one of the most effective ways to facilitate droplet deposition and control efficacy. At present, research on spray adjuvants for UAVs are mainly based on droplet drift and laboratory tests. Few studies have been conducted on the physicochemical properties of spray adjuvants and the effects of droplet deposition characteristics. To explore the properties of four different kinds of spray adjuvants (Mai Fei, Bei Datong, G-2801, and Agrospred 910) and the deposition characteristics of spray adjuvants on litchi leaves, an automatic surface tension meter, a contact angle measuring device, an ultraviolet visible spectrophotometer, and a DJI AGRAS T30 plant protection UAV was used to measure the surface tension, contact angle, and droplet deposition characteristics on litchi under UAV spraying operations. The results showed that the addition of spray adjuvants could significantly reduce the surface tension of the solution. The surface tension value of the solution after adding the spray additives was reduced by 53.1–68.9% compared with the control solution. Among them, the Agrospred 910 spray adjuvant had the best effect on reducing the surface tension of the solution. The contact angle of the control solution on the litchi leaves varied from 80.15° to 72.76°. With the increase in time, the contact angle of the spray adjuvant solution gradually decreased, the Agrospred 910 spray adjuvant had the best effect, and the contact angle decreased from 40.44° to 20.23° after the droplets fell on the litchi leaves for 60 s. The adjuvant solutions increased the droplet size, but the uniformity of the droplet size decreased. The Dv0.5 of different spray solutions ranged from 97.3 to 117.8 μm, which belonged to the fine or very fine droplets, and the Dv0.5 of adjuvants solutions were significantly greater than that of the control solution. The RSs of adjuvant solutions were very similar and ranged from 0.92 to 0.96, all of which were significantly greater than the result of the control solution (0.57). Compared with the deposition of the control solution, the Mai Fei, Bei Datong, and G-2801 solutions clearly increased spray deposition, with total depositions of 0.776, 0.705, and 0.721 μL/cm2, which are all greater than the total deposition of the control solution of 0.645 μL/cm2. The addition of tank-mixed adjuvants could effectively increase the uniformity of the spray deposition, and all the average CVs of adjuvant solutions were lower than 96.86%. On the whole, Mai Fei performed best in increasing the spray deposition and promoting penetration, followed by Bei Datong and G-2801. Meanwhile, the test can also provide a reference for improving the utilization rate of UAV pesticide applications.

An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

PurposeUnlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.Design/methodology/approachThe paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.FindingsSimulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.Originality/valueConventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

Summarize of the Development of UAV

An unmanned aerial vehicle is an unmanned aerial vehicle that, compared to a piloted aircraft, does not require a pilot to fly, and is therefore also called an unmanned aerial vehicle. UAVs have a wide range of applications in both military and civilian fields. In the military field, UAVs are capable of performing a variety of missions such as reconnaissance, surveillance, target location. This article will analyze the current application of unmanned aerial vehicles in military and civilian fields from the development history and current situation of unmanned aerial vehicles, and prospect its future development trend.

Heavy fuel preparation effects on the operation of a spark ignition unmanned aerial vehicle engine

PurposeThis paper aims to conduct an experimental and theoretical investigation into fuel pre-delivery effects for a heavy fuel crankcase scavenged spark ignition two-stroke cycle engine for unmanned aerial vehicle application.Design/methodology/approachOne-dimensional computational fluid dynamic modelling of the engine system using WAVE software supported by experimental dynamometer testing of the subject engine with kerosene JET A-1 and gasoline and fuels.FindingsThe experimental research has shown performance improvements using fuel preheating via use of auxiliary transfer port fuel injection. Computational simulation has allowed comparisons with auxiliary transfer port injection and direct in-cylinder injection to be made.Practical implicationsWhile some heavy fuel engines are now available for unmanned aerial vehicles the best solution to meet the military equipment single fuel policy remains an area of evolving research. The findings within this study show possibilities for fuel pre-treatment.Originality/valueOne-dimensional computational fluid dynamic modelling of the engine system using WAVE software supported by experimental dynamometer testing of the subject engine with kerosene JET A-1 and gasoline fuels.