Drone Applications Research Articles

Curriculum System Construction for Typical Positions in Drone Application Technology Major

In response to the needs of drone equipment construction in the new situation, a professional committee was established to conduct in-depth research in the military and industry, combined with the characteristics of higher vocational students and military job demands, to reconstruct the curriculum system of Drone Application Technology major. This aims to enable students to better grasp the necessary knowledge related to the profession, broaden students knowledge base, consolidate and enhance their professional knowledge and skills, and provide reference for the construction of related professional curriculum systems in higher vocational colleges.

Based on Online+Offline and Virtual+Reality Research on Unmanned Organization Teaching Model

Experimental courses are an important part of cultivating talents in drone application technology, and drone assembly courses are a highly practical discipline. This article proposes to construct an experimental course teaching model of online+offline and virtual+reality , which can effectively solve the contradiction of insufficient drone equipment resources and slow equipment updates in schools, and also provide reference for the teaching reform of drone related majors.

Drone resupply with multiple trucks and drones for on-time delivery along given truck routes

Drones have been increasingly used for deliveries; however, delivering packages directly to customers is still challenging, particularly in densely populated urban areas. To overcome this, drone resupply has been proposed, where drones carry packages to trucks en route, and then trucks make the final deliveries. Existing studies have only considered the case of one truck and one drone. In this study, we investigate drone resupply with multiple trucks and drones for the on-time delivery of late-available packages. Considering practical operations in urban areas, we focus on a situation where trucks deliver packages along given routes. This raises the drone resupply scheduling problem, which aims to maximise the total value of the late-available packages that can be delivered on time. We formally define the problem and analyse its optimality properties. Then, we develop an exact solution approach based on a time-expanded network flow model and design a greedy heuristic algorithm. We validate the efficiency and effectiveness of the proposed solutions through computational studies. Operational insights and guidance are derived for the application of drones in on-time delivery.

Systematic review and best practices for drone remote sensing of invasive plants

Abstract Drones have emerged as a cost‐effective solution to detect and map plant invasions, offering researchers and land managers flexibility in flight design, sensors and data collection schedules. A systematic review of trends in drone‐based image collection, data processing and analytical approaches is needed to advance the science of invasive species monitoring and management and improve scalability and replicability. We systematically reviewed studies using drones for plant invasion research to identify knowledge gaps, best practices and a path toward advancing the science of invasive plant monitoring and management. We devised a database of 33 standardized reporting parameters, coded each study to those parameters, calculated descriptive statistics and synthesized how these technologies are being implemented and used. Trends show a general increase in studies since 2009 with a bias toward temperate regions in North America and Europe. Most studies have focused on testing the validity of a machine learning or deep learning image classification technique with fewer studies focused on monitoring or modelling spread. Very few studies used drones for assessing ecosystem dynamics and impacts such as determining environmental drivers or tracking re‐emergence after disturbance. Overall, we noted a lack of standardized reporting on field survey design, flight design, drone systems, image processing and analyses, which hinders replicability and scalability of approaches. Based on these findings, we develop a standard framework for drone applications in invasive species monitoring to foster cross‐study comparability and reproducibility. We suggest several areas for advancing the use of drones in invasive plant studies including (1) utilizing standardized reporting frameworks to facilitate scientific research practices, (2) integrating drone data with satellite imagery to scale up relationships over larger areas, (3) using drones as an alternative to in‐person ground surveys and (4) leveraging drones to assess community trait shifts tied to plant fitness and reproduction.

4D printing of fiber-reinforced auxetic structures: the building blocks: A review

Abstract Four-dimensional (4D) printing has recently received much attention in the field of smart materials. It concerns using additive manufacturing to obtain geometries that can change shape under the effect of different stimuli. Such a technique enables the fabrication of 3D printed parts with the additional functionality of scalable, programmable, and controllable part shapes over time. 
This review provides a comprehensive examination of advances in the field of 4D printing, emphasizing the integration of fiber reinforcement and auxetic structures as crucial building blocks. 
The incorporation of fibers enhances structural integrity, while auxetic design principles contribute unique mechanical properties, such as negative Poisson's ratio and great potential for energy absorption due to their specific deformation mechanisms. Therefore, they present potential applications in aerospace, drones, and robotics.
The objective of this review article is first to describe the distinctive properties of shape memory polymers (SMPs), auxetic structures, and composite (fiber-reinforced) materials. A review of applications that use combinations of such materials is also presented when appropriate. The goal is to get a grip on the delicate balance between the different properties achievable in each case. The paper concludes by describing recent advances in 4D printing of fiber-reinforced auxetic structures.

End-to-End GPS Tracker Based on Switchable Fuzzy Normalization Codec for Assistive Drone Application

End-to-End GPS Tracker Based on Switchable Fuzzy Normalization Codec for Assistive Drone Application

Medical Supplies Delivery Autonomous Drone with Security

Abstract: Drones and UAVs have gained a lot of attention in the recent years. Being fast and efficient, they have the capability to carry out a multitude of tasks efficiently and quickly. One of the critical applications of drones is in the field of healthcare is the delivery of medical supplies. In case of severe natural calamities, only a drone is suitable for providing the basic essential supplies that can save the lives of constrained survivors. In rural and underdeveloped areas, a drone is ideal for delivery. The usage of a medical drone is not restricted as it can easily be used in day-to-day urban and sub-urban areas. It is an efficient method to deliver medicines and aid to patients and those in need.

Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle.

In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.

Experimental Evaluation of an SDR-Based UAV Localization System.

UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

Drone: The key to the next era of war

With the rapid development of the economic level, the weapons, equipment, and military combat capabilities of countries worldwide are gradually increasing, and unmanned combat systems will inevitably become the main development direction of future wars. This article takes the application of drones in the Russo-Ukrainian War as an example, aiming to analyze the changes brought about by using drones in modern warfare.

UAV Control Based on Pattern Recognition in Aquaculture Application

This study proposes a drone application for the net cage aquaculture industry. A visual control structure is applied to the drone to obtain water-quality information surrounding the net cages. This study integrates a hexacopter, camera, onboard computer, flight control board, servo motor, and global positioning system’s auto-cruise function to adjust the drone position and control the servo motor retractable sensor to reach the desired target at an accurate location. In object identification, a deep learning neural network is used to identify the net cages. An onboard computer calculates the horizontal distance between the drone and the net cage. A “You only look once” (YOLO) neural network is used to detect the net cage images. Considering the hardware calculation speed and ability, an onboard computer is applied to process the flight control board and control the drone. In the mission, an aerial camera detects targets (net cage) and provides visual information to the drone for the target approaching control process. After executing the water-quality measurement, the drone will end the mission and return to the base. This study modifies the architecture of YOLO, compares it with the original model, and then finds a proper architecture for this mission. This study aims to assist cage aquaculture operators by using drones to measure water quality, which can reduce aquaculture’s labor costs.

REVIEWING THE ROLE OF AI IN DRONE TECHNOLOGY AND APPLICATIONS

This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors.
 Keywords: Role, AI, Drone, Applications, Technology.

Two-Stage Delivery System for Last Mile Logistics in Rural Areas: Truck–Drone Approach

In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural areas, a truck–drone distribution model was developed based on the specific conditions of rural regions. The improved fuzzy C-means algorithm (FCM) and genetic simulated annealing algorithm (GASA) were employed to tackle real−world cases in rural areas. The focus of the truck–drone system is to optimize the rural logistics distribution process, reduce delivery time, and minimize costs while considering factors such as maximum mileage of trucks and drones as well as customer priority. Compared to traditional methods, this system has demonstrated notable improvements in distribution efficiency and cost reduction, offering valuable insights for practical drone applications in last mile rural logistics.

Detection of Drones using SDR as a RADAR

With the evolution of technology, the application of drones in Fourth Generation Warfare has increased manifolds. Drones offer many advantages as they are inexpensive to operate, can fly for longer periods, and are less vulnerable to enemy fire. This makes them an ideal, inconspicuous, and low-cost weapon for both conventional and non-state actors to carry out targeted attacks on valuable targets. Detection of drones is a major challenge as they offer minimum Radio signature and low Radar cross-section. A credible method to detect incoming drones is by employing Software Defined Radio as a RADAR

Flying high for conservation: Opportunities and challenges of operating drones within the oldest National Park in the Alps

Abstract Drones have emerged as an essential tool in various conservation applications. Despite the great potential, drone use by protected area managers is still scarce or accompanied by scepticism. The ongoing debate revolves around whether drones are fancy gadgets or if they can effectively guide management strategies and align with the overarching goals of protected areas. Here, we present a practical overview of how novel drone applications contribute to the goals of the oldest national park in the Alps, along with the associated challenges. To do so, we review our seven‐year experience as park employees flying drones within the park and its surroundings. First, we provide background information receiving limited attention in the existing literature such as our motivation behind a drone purchase, costs and an overview of our flight operations. Second, we show three examples that demonstrate the potential of drones as a valuable tool in addressing the goals of the park: managing the area, researching natural processes and facilitating communication. Third, we reflect on operational challenges and provide valuable lessons for addressing the specific challenges of flying drones in an alpine and protected environment. Practical implication: Our experience supports the benefits of drones for protected area management, but it also highlights the need for certain precautions, increased focus on operational challenges and further research on wildlife‐drone interactions.

Applications of Drone and Unmanned Aerial Vehicle (UAV) Surveying for Planning For Cities

Abstract: The field of town planning is one of the many areas in city life where Unmanned Aerial Vehicles (UAVs) and drones could find use. When compared to traditional surveying options, drones may offer planners a more accurate, precise, flexible, and economical means of obtaining information. Unfortunately, current Indian government restrictions around the use of drones, particularly in the town planning sector, are vague and prioritize safety above advancing technology toward a system that is more efficient. To maximize the public benefit from the use of drone technology in town planning, town planners and surveyors require flexible, practical drone regulations that address the numerous concerns surrounding the use of UAVs in urban areas. This research discusses the present applications of UAVs in many areas of urban planning.

High Speed Fighter UAV with Electric Coil Gun

The paper focus on "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant milestone in the realm of unmanned aerial vehicles (UAVs). This project aimed to design, develop, and demonstrate a cutting-edge high-speed fighter drone equipped with an electromagnetic coil gun and an advanced payload deployment mechanism. The primary objectives were to enhance drone maneuverability, achieve precision targeting capabilities, and enable efficient payload deployment, thus expanding the potential applications of UAV technology. This project involved an intricate blend of engineering, innovation, and technology integration. The drone's design and construction incorporated a lightweight yet durable frame, high-speed motors, and advanced flight control systems, ensuring exceptional agility and stability during flight. The electromagnetic coil gun, a pivotal component, was meticulously designed for precision targeting, delivering impressive accuracy and firing control. Moreover, the payload deployment mechanism, a vital feature of the system, was developed to ensure the precise and reliable delivery of payloads, expanding the drone's versatility across various domains, including defense, surveillance, and emergency response Throughout the project's lifecycle, rigorous testing, calibration, and data analysis were conducted to validate the drone's performance, accuracy of the electromagnetic coil gun, and efficiency of the payload deployment mechanism. The project yielded promising results, demonstrating the drone's exceptional flight performance, electromagnetic coil gun's precision targeting, and payload deployment's reliability. This project report provides a comprehensive overview of the project's objectives, methodologies, results, and significance. It explores the intricate system design, integration processes, testing procedures, and detailed analyses of outcomes. Furthermore, it discusses the challenges faced during development and the potential impact of this high- speed fighter drone in various industries. In conclusion, the "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant leap forward in UAV technology. Its successful realization opens up new possibilities for drone applications, promising advancements in precision targeting, payload deployment, and the overall versatility of UAVs in both civilian and military contexts

Classification of Genetics Based on Machine Learning Algorithms: A Review

The multifaceted applications of drones in addressing humanitarian challenges, enhancing governance services, medical assistance, and security considerations. Drones are showcased as adaptable and swift responders in conflict or disaster-affected areas, mitigating risks for humanitarian workers and delivering crucial supplies to remote locations. The integration of digital technology in governance services is discussed, emphasizing transparency, efficacy, and reduced corruption. The study also introduces a taxonomy for GPS- guided drones in medical supply delivery, highlighting challenges in accuracy and cost reduction. Drones' wide- ranging potential uses, from police operations to advertising and shipment transportation, are outlined. A comprehensive evaluation of drone security, from consumer drones to military systems, is provided, along with preventive suggestions. Machine learning algorithms for drone detection and classification, showcasing the proposed DDI system's capability to accurately identify intruding drones and their operational modes. machine learning in the context of used drones, focusing on detection and classification. The study assesses various machine learning algorithms, including image processing, sound analysis, and RF signal-based techniques, to identify and classify drones effectively. Data from diverse sensors are utilized for feature extraction, employing algorithms such as Deep Neural Networks, Support Vector Machines, and deep belief networks. The proposed DDI system adopts an RF-based approach and integrates a Deep Learning algorithm for precise detection and identification of used or intruding drones.

UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications.

This article presents an analysis of current state-of-the-art sensors and how these sensors work with several mapping algorithms for UAV (Unmanned Aerial Vehicle) applications, focusing on low-altitude and high-speed scenarios. A new experimental construct is created using highly realistic environments made possible by integrating the AirSim simulator with Google 3D maps models using the Cesium Tiles plugin. Experiments are conducted in this high-realism simulated environment to evaluate the performance of three distinct mapping algorithms: (1) Direct Sparse Odometry (DSO), (2) Stereo DSO (SDSO), and (3) DSO Lite (DSOL). Experimental results evaluate algorithms based on their measured geometric accuracy and computational speed. The results provide valuable insights into the strengths and limitations of each algorithm. Findings quantify compromises in UAV algorithm selection, allowing researchers to find the mapping solution best suited to their application, which often requires a compromise between computational performance and the density and accuracy of geometric map estimates. Results indicate that for UAVs with restrictive computing resources, DSOL is the best option. For systems with payload capacity and modest compute resources, SDSO is the best option. If only one camera is available, DSO is the option to choose for applications that require dense mapping results.