Unmanned Aerial Vehicle Applications Research Articles

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.

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.

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

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

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.

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.

Fuzzy synthetic evaluation of the critical drivers of UAVs’ deployment for construction in Nigeria

PurposeThe slow adoption of unmanned aerial vehicles (UAVs) in the construction industry, particularly in developing countries like Nigeria, underscores the need for a deeper understanding of the critical factors influencing their adoption. This study aims to identify these factors using the Technology-Organization-Environment (TOE) framework and address uncertainties in their prioritization through Fuzzy Synthetic Evaluation (FSE). The utility of this approach lies in its ability to provide construction organizations with actionable insights to enhance operational efficiency and competitiveness through effective UAV adoption.Design/methodology/approachA post-positivist philosophical stance was adopted, wherein quantitative data were gathered from construction professionals in Nigeria via a questionnaire survey. The collected data were analyzed using the Cronbach alpha test as a measure of internal consistency and the FSE test to synthesize critical drivers for the adoption of UAVs.FindingsThe study found that drivers related to technology and organization are the most critical drivers. This implies that variables related to technology and organization warrant a higher level of focus if UAVs are to continue gaining popularity within the construction industry. Additionally, this study identified that logistic management, construction monitoring and site surveying represent the most critical areas of UAV application within the construction industry.Practical implicationsThe emphasis on technology and organizational drivers as critical factors suggests that construction companies should prioritize investments in technology infrastructure and cultivate an organizational culture that embraces innovation. This may involve providing training to construction professionals to enhance their technological skills and fostering a leadership culture that champions technology adoption.Originality/valueThis study introduces novelty by applying the TOE framework, which has received limited attention in UAV adoption studies within construction. Additionally, the use of FSE addresses uncertainties in prioritizing critical drivers, particularly relevant in developing countries facing unique technological challenges. By assigning priority to these factors, this research lays the groundwork for a more informed and strategic approach to UAV adoption.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

Runway-Free Recovery Methods for Fixed-Wing UAVs: A Comprehensive Review

Fixed-wing unmanned aerial vehicles (UAVs) have the advantages of long endurance and fast flight speed and are widely used in surveying, mapping, monitoring, and defense fields. However, its conventional take-off and landing methods require runway support. Achieving runway-free recovery is necessary for expanding the application of fixed-wing UAVs. This research comprehensively reviews the various techniques and scenarios of runway-free recovery of fixed-wing UAVs and summarizes the key technologies. The above methods cover parachute recovery, net recovery, rope recovery, SideArm recovery, deep stall recovery, towed drogue docking recovery, and robotic arm recovery methods within runway-free recovery. Finally, this research discusses the future research directions of runway-free recovery.

Dual-Band SIW Antenna with High Frequency Ratio for UAV Detection Applications

A dual-frequency antenna based on substrate integrated waveguide (SIW) for unmanned aerial vehicle (UAV) applications is proposed, which provides a new idea for the design of multi-frequency antennas by stimulating multiple high-order modes. A group of fan-shaped patches are added to the circular slots on both sides of the dumbbell slots to form modified dumbbell slots. Two resonant modes are excited by this slot structure, and a dual-frequency response is achieved. Different opening angles and offset angles of fan-shaped patches can cause the deviation of the dual-frequency response bands. Therefore, the antenna working frequency band can be freely designed within a certain frequency band, which improves the flexibility of antenna design. The designed antenna is analyzed theoretically and optimized by simulation. Finally, the antenna was manufactured, and the experimental results were basically consistent with the simulation optimization results. The antenna worked in the X-band and the Ku-band, and the relative working bandwidth of the two bands was 6.06% (9.6–10.2 GHz) and 1.41% (14.06–14.26 GHz), respectively. The maximum gain of simulation in low frequency and high frequency operating bands is 5.9dBi and 6.13dBi, respectively.

Disaster Rescue Drone Based on YOLOv4 Algorithm

Abstract With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative verification, the system has demonstrated high-efficiency in target detection and tracking in various disaster environments.

Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review

The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.

Small Object Detection in UAV Images Based on YOLOv8n

With the rapid development of unmanned aerial vehicle (UAV) technology, there is an urgent need for high-performance aerial object detection algorithms that are tailored for deployment on drones with limited computing capabilities. This paper proposes a series of improvements to the state-of-the-art YOLOv8 object detector to enhance its detection accuracy and speed for small, partially occluded objects in complex environments. Specifically, we introduce multi-scale feature fusion through additional detection layers, employ conditionally parameterized convolutions to increase representational capacity, and import a dynamic non-monotonic loss function named Wise-IoU to enable more effective regression of bounding boxes. Experiments conducted on the large-scale UAV benchmark dataset VisDrone demonstrate that the improved model achieves state-of-the-art accuracy of 37.6% mAP with 3 M parameters, outperforming other lightweight YOLO detectors and two-stage detectors like Faster R-CNN. The improved model also reaches 40 FPS on an embedded edge device, validating its efficiency and suitability for real-time UAV applications. Through comprehensive quantitative experiments and visual results, this work provides valuable insights and techniques to tailor object detection algorithms for robust and efficient deployment on UAVs with limited onboard computing power.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

Classification, military applications, and opportunities of unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are cutting-edge technologies used for military purposes world-wide at tactical, operational, and strategic levels. This study provides an overview of the history and current state of military drones, considering a global and Ecuadorian background. Then, a classification of the UAVs developed and built in Ecuador is conducted based on their endurance, altitude, and wing span to understand the national context and progress. The research also delves into the applications of UAVs in several military operations and missions, aiming to create a framework that aligns UAV capabilities with specific operational needs; this permits the identification of the challenges and opportunities the country faces. Unmanned aerial systems have changed the battlefield, and the government needs to adapt to a national strategy that incorporates this technology; this research analyzes and provides insights to improve military capabilities such as exploring modern UAV military applications, technical updates in communication, navigation, and data acquisition systems; and the integration of emerging technologies like smart materials, artificial intelligence, and electric propulsion systems. This study provides valuable insights into the Ecuadorian UAVs that enhance the country’s military operations and offer some applications and uses of this technology for national security.

Research On Vision Based SLAM Technology For UAV

The application of Simultaneous Localization and Mapping (SLAM) technology in the field of UAVs is becoming more and more extensive and in-depth. This paper reviews its rapid development and breakthroughs in recent years, lists some problems that can still be improved, and prospects the development of SLAM technology in the future. The SLAM technology is a powerful and highly flexible technology, whose application in Unmanned Aerial Vehicles (UAVs) can improve their operation safety and autonomous work ability. This paper lists some current research results which show the application of SLAM technology based on vision in UAVs plays a key role in obstacle avoidance, positioning and path planning. At the same time, in order to further enhance the help of vision based SLAM technology to UAVs, many emerging technologies are introduced to integrate with it. This paper reviews the impact of integrating SLAM technology based on vision with GPS positioning technology, inertial measurement technology and other technologies on UAVs during operation as well. The relevant research in this paper will play a significant role in the application of vision based SLAM technology in UAVs and the integration with other technologies.