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Journal articles on the topic 'Drone types'
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1
Yan, Jun, Huiping Hu, Jiangkun Gong, Deyong Kong, and Deren Li. "Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types." Drones 7, no. 4 (2023): 280. http://dx.doi.org/10.3390/drones7040280.
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In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.
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Kong, Hwayeon, Frank Biocca, Taeyang Lee, Kihyuk Park, and Jeonghoon Rhee. "Effects of Human Connection through Social Drones and Perceived Safety." Advances in Human-Computer Interaction 2018 (2018): 1–5. http://dx.doi.org/10.1155/2018/9280581.
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This study investigates whether people perceive social drones differently depending on pilot type and perceived safety. A “drone campus tour guide” social drone service was examined to explore these values. This study involves a between-subjects experiment using two drone control types (human-driven and algorithm-driven) and two levels of perceived safety (low and high). The results demonstrate that the drone pilot type changes the service experience when the drone is flying in an unsafe manner. In the group where the drones were flown in an unsafe manner, participants exhibited higher levels of satisfaction with the algorithm-driven drone guide, while both types of drones received the same level of satisfaction when they were flown safely. The results have implications for understanding how expectations influence service evaluations in relation to human connection.
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Maxamatov, Sanjar Erkin o'g'li. "TYPES OF UNMANNED AERIAL VEHICLES." INTERNATIONAL BULLETIN OF ENGINEERING AND TECHNOLOGY 3, no. 6 (2023): 184–87. https://doi.org/10.5281/zenodo.8058526.
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The development of network technologies in recent years is now increasing interest in multi-purpose unmanned aerial vehicles yani drone per day. Let us give a brief overview of the application of drones in various fields.
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Howell, Lachlan G., Blake M. Allan, Don A. Driscoll, Daniel Ierodiaconou, Todd A. Doran, and Michael A. Weston. "Attenuation of Responses of Waterbirds to Repeat Drone Surveys Involving a Sequence of Altitudes and Drone Types: A Case Study." Drones 7, no. 8 (2023): 497. http://dx.doi.org/10.3390/drones7080497.
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Remotely piloted aircraft systems (RPAS, or ‘drones’ hereafter) have potential for surveying waterbird species and habitats, but there is a risk that the disturbance from drones could compromise count accuracy and bird welfare. We examined the response of 16 waterbird species to repeated up-and-back overhead drone flights (n = 50 flights) at multiple flight heights (80, 60, 40 and 20 m) using three common drone platforms (DJI Matrice 300, DJI Mavic 2 Enterprise Advanced and DJI Phantom 4). A ground observer scored the species’ responses to overhead drone flights, which ranged from no response (no change to initial behavior), vigilance (head turning and tracking), movement within the site (swimming, diving, flight into or on the water) and substantial flight resulting in departure from the pond (fleeing). A total of 280 waterbird encounters with overhead drones were observed. The most common response across all flights was no response (70.7%), followed by vigilance (27.5%), whereas more intense responses were comparatively rare (1.8%). The responses were of higher intensity during earlier overhead drone flights, before moderating substantially during later flights. Thus, our case study provides the first unambiguous evidence of the attenuation of responses of bird species to drones.
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Dung, Nguyen Dinh. "Developing Models for Managing Drones in the Transportation System in Smart Cities." Electrical, Control and Communication Engineering 15, no. 2 (2019): 71–78. http://dx.doi.org/10.2478/ecce-2019-0010.
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AbstractUnmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.
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Hornain, Imran Mohd, and Nik Fadzly N. Rosely. "Evaluating drones as bird deterrents in industrial environments: multirotor vs fixed-wing efficacy." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 3960–67. http://dx.doi.org/10.11591/eei.v13i6.7359.
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Unmanned aerial vehicles (UAVs) or drones have been proposed as deterrent tools to mitigate pest birds’ problems. Many studies have been conducted to evaluate the efficacy of drones, mainly to protect crops, fishponds and airports. Little information can be acquired on using drones in industrial areas. In this study, two types of drones, categorized as multirotor drones and fixed-wing drones, were used to evaluate their efficacy in reducing pest birds, Asian glossy starling (Aplonis panayensis) flocks in one of the semiconductor factories in Kulim Hi-tech Park, Kedah, Malaysia during dusk. Each drone was evaluated during its five minutes of operation time and five minutes after landing. Control data were also taken to compare drone treatment days with no drone treatment days. Our result shows a significant difference between multirotor drone treatment and control treatment but not between fixed-wing drone treatment and control treatment due to different altitudes applied, ambient light intensity and size of flight path covered. We suggest implementing biomimetic design into drones and applying other conventional ground deterrents to prolong the residual effect of post-treatment.
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Peksa, Janis, and Dmytro Mamchur. "A Review on the State of the Art in Copter Drones and Flight Control Systems." Sensors 24, no. 11 (2024): 3349. http://dx.doi.org/10.3390/s24113349.
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This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.
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Appelbaum, Deniz, and Robert A. Nehmer. "Using Drones in Internal and External Audits: An Exploratory Framework." Journal of Emerging Technologies in Accounting 14, no. 1 (2017): 99–113. http://dx.doi.org/10.2308/jeta-51704.
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ABSTRACT: Recently the FAA relaxed restrictions on the use of drones or Unmanned Aircraft Systems (UASs) for commercial purposes. Markets for commercial drone use are in the technology trigger phase of the Gartner Group's Hyper Cycle, with developments occurring rapidly in real estate, agriculture (farming), the film industry, insurance, and other areas. Examination and inspection applications of drones have been proposed in heavy industry and cell tower inspection. Previous research suggests an incremental structure for implementing technological innovations such as continuous auditing (CA). In this paper these proposals are expanded to include the additional requirements to add drone technologies. This structure is extended here by (1) defining the use of drones in audit environments, with emphasis on the continuous versus occasional use of drone technologies, (2) extending the technical adoption architecture to include the use of drones, and (3) considering the types of drone usages amenable to both internal and external audits. A specific architecture is proposed here to prototype inventory counts in large warehouses or open-air inventories and that satisfies the suggested requirements. Additionally, this proposal adds value to the current research by extending the discussion of technology adoption in the Alles, Kogan, and Vasarhelyi (2008) paper to include the use of drones in many different audit environments by enumerating the usage types of drones in audit settings and by considering the prototype of such a system.
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Muhamad, Aidil, Seno Darmawan Panjaitan, and Redi Ratiandi Yacoub. "DESIGN AND DEVELOPMENT OF FLIGHT CONTROLLER FOR QUADCOPTER DRONE CONTROL." Telecommunications, Computers, and Electricals Engineering Journal 1, no. 3 (2024): 279. http://dx.doi.org/10.26418/telectrical.v1i3.73681.
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UAV (Unmanned Aerial Vehicle), also commonly called drone, is a flying robot technology that can be controlled remotely and can also fly autonomously based on the mission given by the operator. Drones are usually used for various purposes such as package delivery, watering plants, land mapping, natural disaster monitoring, photography, videography and others. Drones have many types, one of which is a drone with four motors as the main drive, commonly called a quadcopter drone. Quadcopter drones have evolved a lot based on current needs. Although quadcopter drones have many uses, the development of quadcopter drone research in Indonesia is quite slow, one of the quadcopter drone components whose development is quite slow is the flight controller. Flight controller (FC) is a main controller brain in drones that has complex functions in quadcopter drone control. The function of the FC is to regulate motor speed, stabilize and maintain altitude. In this research, FC is designed to control the stability of quadcopter drones while flying. This FC was developed by applying LoRa technology as an internal receiver. LoRa technology is used to receive control data from the remote control (RC) and simultaneously send sensor data. The purpose of this research is to design FC to improve local products in the field of technology and participate in the development of flying robot technology, especially on quadcopter drones and to determine the performance of LoRa technology after being integrated as an internal transceiver in FC for remote control of quadcopter drones.
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Subbarayalu, Venkatraman, and Maria Anu Vensuslaus. "An Intrusion Detection System for Drone Swarming Utilizing Timed Probabilistic Automata." Drones 7, no. 4 (2023): 248. http://dx.doi.org/10.3390/drones7040248.
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Unmanned aerial vehicles (UAVs), commonly known as drones, have found extensive applications across diverse sectors, such as agriculture, delivery, surveillance, and military. In recent times, drone swarming has emerged as a novel field of research, which involves multiple drones working in collaboration towards a shared objective. This innovation holds immense potential in transforming the way we undertake tasks, including military operations, environmental monitoring, and search and rescue missions. However, the emergence of drone swarms also brings new security challenges, as they can be susceptible to hacking and intrusion. To address these concerns, we propose utilizing a timed probabilistic automata (TPA)-based intrusion detection system (IDS) to model the normal behavior of drone swarms and identify any deviations that may indicate an intrusion. This IDS system is particularly efficient and adaptable in detecting different types of attacks in drone swarming. Its ability to adapt to evolving attack patterns and identify zero-day attacks makes it an invaluable tool in protecting drone swarms from malicious attacks.
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Editya, Arda Surya, Neny Kurniati, Mochammad Machlul Alamin, Anggay Luri Pramana, and Angga Lisdiyanto. "Forensic Analysis of Drones Attacker Detection Using Deep Learning." Scientific Journal of Informatics 11, no. 1 (2024): 219–26. http://dx.doi.org/10.15294/sji.v11i1.48183.
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Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.
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Liu, Ming, Xin Liu, Maoran Zhu, and Feifeng Zheng. "Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service." Sustainability 11, no. 14 (2019): 3871. http://dx.doi.org/10.3390/su11143871.
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Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches.
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Suroso, Indreswari, and Erwhin Irmawan. "Analysis Of Aerial Photography With Drone Type Fixed Wing In Kotabaru, Lampung." Journal of Applied Geospatial Information 2, no. 1 (2018): 102–7. http://dx.doi.org/10.30871/jagi.v2i1.738.
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In the world of photography is very closely related to the unmanned aerial vehicle called drones. Drones mounted camera so that the plane is pilot controlled from the mainland. Photography results were seen by the pilot after the drone aircraft landed. Drones are unmanned drones that are controlled remotely. Unmanned Aerial Vehicle (UAV), is a flying machine that operates with remote control by the pilot. Methode for this research are preparation assembly of drone, planning altitude flying, testing on ground, camera of calibration, air capture, result of aerial photos and analysis of result aerial photos. There are two types of drones, multicopter and fixed wing. Fixed wing has an airplane like shape with a wing system. Fixed wing use bettery 4000 mAh . Fixed wing drone in this research used mapping in This drone has a load ability of 1 kg and operational time is used approximately 30 minutes for an areas 20 to 50 hectares with a height of 100 m to 200 m and payload 1 kg above ground level. The aerial photographs in Kotabaru produce excellent aerial photographs that can help mapping the local government in the Kotabaru region.
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Hong, Tao, Qiye Yang, Peng Wang, et al. "Multitarget Real-Time Tracking Algorithm for UAV IoT." Wireless Communications and Mobile Computing 2021 (August 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9999596.
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Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.
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Shahmoradi, Javad, Elaheh Talebi, Pedram Roghanchi, and Mostafa Hassanalian. "A Comprehensive Review of Applications of Drone Technology in the Mining Industry." Drones 4, no. 3 (2020): 34. http://dx.doi.org/10.3390/drones4030034.
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This paper aims to provide a comprehensive review of the current state of drone technology and its applications in the mining industry. The mining industry has shown increased interest in the use of drones for routine operations. These applications include 3D mapping of the mine environment, ore control, rock discontinuities mapping, postblast rock fragmentation measurements, and tailing stability monitoring, to name a few. The article offers a review of drone types, specifications, and applications of commercially available drones for mining applications. Finally, the research needs for the design and implementation of drones for underground mining applications are discussed.
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Gligorević, Kosta, Milan Dražić, Miloš Pajić, Milan Šunjevarić, Biljana Bošković, and Mićo Oljača. "Overview of the possibility application of some nano drone technologies in modern agriculture." Poljoprivredna tehnika 49, no. 1 (2024): 75–96. http://dx.doi.org/10.5937/poljteh2401075g.
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The use of different types of drones in almost all sectors of the global economy is growing rapidly, but the use of drones in agriculture has suddenly increased. According to some data from the literature, the market for different types of drones in agriculture alone is expected to grow from USD 1.2 billion in 2019 to USD 5.5 billion in 2024. A particularly interesting phenomenon is the significant increase in the use of drones (especially various nano-types) in the world and the possibility of some of them being used in agriculture in the Republic of Serbia. The world of drone technology has taken a huge leap forward with the introduction of nano drones. For example, some modern nano drone solutions have dimensions of less than 2 x 2 cm. Nano drones are ultra-small remote-controlled aircraft that can perform a variety of tasks. They are equipped with advanced sensors and functions such as obstacle avoidance and high-speed maneuverability. Some models are even capable of taking aerial photographs, staying in the air for long periods of time and flying autonomously. Nano drones are now more affordable than ever before. Prices range from a few hundred dollars to several thousand, depending on the model and features. Nowadays, nano drones are affordable for everyday users in various fields. This paper introduces nano drone technology (e.g. the type of nano drones and equipment) as a new application for greenhouses: There are some stages that greenhouse growers can consider for the use of nano drones; Safe inspection of the structural components of greenhouses; Pollination processes (e.g. the role of RobotBee); Application of shading composite glasshouses; Crop monitoring/inventory of greenhouses.
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Monks, Joanne M., Harriet P. Wills, and Carey D. Knox. "Testing Drones as a Tool for Surveying Lizards." Drones 6, no. 8 (2022): 199. http://dx.doi.org/10.3390/drones6080199.
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A lack of effective methods for sampling lizards in terrain that is inaccessible to human observers limits our knowledge of their ecology and conservation needs. Drones are increasingly being used in wildlife monitoring, but their potential use for surveying lizards has not been evaluated. We investigated: (1) the detectability of model lizards using a drone relative to a human observer, and (2) the response of four lizard species to an approaching drone in three habitat types. Model lizards placed in potential basking positions within a defined search area were detected by both the drone operator and human observer, but the probability of detection was lower with the drone. Jewelled geckos (Naultinus gemmeus) in shrubland and grand skinks (Oligosoma grande) in rocky habitats showed surprisingly little reaction to the approaching drone, enabling close approaches (means of 59 cm and 107 cm, respectively) and accurate species identification with photos taken by the drone camera. For highly patterned jewelled geckos, identification was also possible to individual level. However, the drone was unsuccessful at detecting two alpine skink species in a near-vertical cliff habitat. Collectively, our results suggest that drones have potential as a tool for detecting small-bodied lizards in habitats inaccessible to human observers.
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Suroso, Indreswari. "ANALISIS PERAN UNMANNED AERIAL VEHICLE JENIS MULTICOPTER DALAM MENINGKATKAN KUALITAS DUNIA FOTOGRAFI UDARA DI LOKASI JALUR SELATAN MENUJU CALON BANDARA BARU DI KULONPROGO." REKAM: Jurnal Fotografi, Televisi, dan Animasi 14, no. 1 (2018): 17. http://dx.doi.org/10.24821/rekam.v14i1.2134.
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Dunia fotografi sangat erat berkaitan dengan pesawat tanpa awat disebut drone. Drone dipasang kamera sehingga pesawat tersebut dikendalikan pilot dari daratan. Hasil fotografi dilihat pilot setelah pesawat drone tersebut mendarat. Drone adalah pesawat tanpa awak yang dikendalikan dari jarak jauh. Pesawat tanpa awak atau pesawat nirawak (Unmanned Aerial Vehicle atau UAV) adalah sebuah mesin terbang yang berfungsi dengan kendali jarak jauh oleh pilot. Perkembangan teknologi membuat drone juga mulai banyak diterapkan untuk kebutuhan sipil, terutama di bidang bisnis, industri, dan logistik. Dalam dunia industri bisnis, drone telah diterapkan dalam berbagai layanan seperti pengawasan infrastruktur, pengiriman paket barang, pemadam kebakaran hutan, eksplorasi bahan tambang, pemetaaan daerah pertanian, dan pemetaan daerah industri. Berdasarkan jenisnya, terdapat dua jenis drone, yaitu multicopter dan fixed wing. Multicopter adalah jenis drone yang memanfaatkan putaran baling-baling untuk terbang, sedangkan fixed wing memiliki bentuk seperti pesawat terbang biasa yang dilengkapi sistem sayap. Langkah yang digunakan dalam penelitian ini adalah persiapan pembuatan drone, perencanaan ketinggian terbang, pengujian drone di ground, pengaturankalibrasi kamera, pengambilan foto udara, melihat hasil foto udara, kemudian menganalisis hasil foto udara. Drone dalam penelitian ini memiliki empat propeller, yang digunakan untuk pemetaan jalur selatan menuju pintu masuk New International Yogyakarta Airports melalui Desa Plumbon, Kecamatan Temon, Kabupaten Kulonprogo. AbstractRole Analysis of Unmanned Aerial Vehicle Type MultiCopter in Improving the Quality of Aerial Photography Field in the Southern Path towards the Prospective New Airport in Kulonprogo. The world of photography is very closely related to the unattended aircraft called drones. Drones are mounted with camera so that the plane is pilot-controlled from the mainland. Photography results are seen by the pilot after the drone aircraft is landed. Drones are unmanned aircraft controlled remotely. Unmanned aircraft or Unmanned Aerial Vehicle (UAV), is a flying machine which is operated with remote control by the pilot. Technological developments make the drones also start widely applied to civilian needs, especially in the areas of business, industry and logistics. In business industry, drones have been applied in various services such as infrastructure monitoring, freight forwarding, forest fire-fighter, mining exploration, agricultural mapping, and industrial area mapping. Based on its type, there are two types of drones, namely multicopter and fixed wing. Multicopter is the type of drone that utilizes the spin of the propeller, while the fixed wing has an airplane-like shape with a wing system. The steps used in this study were as follows: drone making preparation, fly height planning, ground drone testing, camera calibration settings, air photo capture, air results viewing, and aerial photographs results analyzing. Drone used in this study has fourpropellers used for mapping south path entrance of New Yogyakarta International Airport through Plumbon Village,Temon sub-district, Kulonprogo regency.
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Singha, Subroto, and Burchan Aydin. "Automated Drone Detection Using YOLOv4." Drones 5, no. 3 (2021): 95. http://dx.doi.org/10.3390/drones5030095.
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Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this research, we designed an automated drone detection system using YOLOv4. The model was trained using drone and bird datasets. We then evaluated the trained YOLOv4 model on the testing dataset, using mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score as evaluation parameters. We next collected our own two types of drone videos, performed drone detections, and calculated the FPS to identify the speed of detection at three altitudes. Our methodology showed better performance than what has been found in previous similar studies, achieving a mAP of 74.36%, precision of 0.95, recall of 0.68, and F1-score of 0.79. For video detection, we achieved an FPS of 20.5 on the DJI Phantom III and an FPS of 19.0 on the DJI Mavic Pro.
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Yun, Guhnoo, Hwykuen Kwak, and Dong Hwan Kim. "Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control." Applied Sciences 14, no. 22 (2024): 10230. http://dx.doi.org/10.3390/app142210230.
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Recent progress in hand gesture recognition has introduced several natural and intuitive approaches to drone control. However, effectively maneuvering drones in complex environments remains challenging. Drone movements are governed by four independent factors: roll, yaw, pitch, and throttle. Each factor includes three distinct behaviors—increase, decrease, and neutral—necessitating hand gesture vocabularies capable of expressing at least 81 combinations for comprehensive drone control in diverse scenarios. In this paper, we introduce a new set of hand gestures for precise drone control, leveraging an RGB camera sensor. These gestures are categorized into motion-based and posture-based types for efficient management. Then, we develop a lightweight hand gesture recognition algorithm capable of real-time operation on even edge devices, ensuring accurate and timely recognition. Subsequently, we integrate hand gesture recognition into a drone simulator to execute 81 commands for drone flight. Overall, the proposed hand gestures and recognition system offer natural control for complex drone maneuvers.
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Al-Dhaqm, Arafat, Richard A. Ikuesan, Victor R. Kebande, Shukor Razak, and Fahad M. Ghabban. "Research Challenges and Opportunities in Drone Forensics Models." Electronics 10, no. 13 (2021): 1519. http://dx.doi.org/10.3390/electronics10131519.
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The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic researchers and practitioners towards a guided methodology for drone-related event investigation. Ultimately, it is important to mention that this study presents a background for the development of international standardization for drone forensics.
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Moskalenko, B. O. "BIOTECHNOLOGICAL APPROACHES IN CONSTRUCTION OF DRONES FOR MEDICAL PURPOSES." Biotechnologia Acta 18, no. 2 (2025): 63–67. https://doi.org/10.15407/biotech18.02.063.
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Aim. Construction of an unmanned controlled complex (drone) with a container for medical care in extreme conditions with the use of biotechnological approaches. Methods. The methods of analysis and object-oriented programming; use of the Python language; construction of the structure of the container for medical care; development of program supply for object recognition and operations with a container for medical purposes. Results. The structure of a drone as a carrier of a container for medical care was scrutinized as well as the versions of drones’ modules for medical purposes. Simultaneously, the various structures of such drones and containers for medical purposes were studied; the possibilities of biotechnological methods use were examined. A new version of the container for transportation by drone was constructed. Appropriate samples of the software for performing individual tasks of medical care in extreme conditions for various types of drone vehicles started to develop. Conclusions. The drone with a container for medical care in extreme conditions with the use of biotechnological methods, as well as techniques of object recognition, was developed successfully.
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Sahithi, N. "Drone Detection and Classification Using Music (Multiple Signal Classification) Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 767–71. http://dx.doi.org/10.22214/ijraset.2023.53462.
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Abstract: The rapid augmentation of drones in different sectors has raised concerns regarding privacy, security, and safety. This project proposes a novel drone detection and classification approach by integrating the YOLOv5 framework with a MUSICbased algorithm to address the above issues. The aim is to postulate an efficient and accurate model capable of identifying and classifying different types of drones in real time. The project leverages the YOLOv5 architecture incorporated with the MUSIC algorithm for superior speed and accuracy. By training YOLOv5 on a large dataset of drone images, the model can learn to identify visual features and specific characteristics unique to drones, permitting effective detection and classification
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Zhou, Zhengqi, Yonghong Tan, Yongda Lin, et al. "Aerodynamic Optimization and Wind Field Characterization of a Quadrotor Fruit-Picking Drone Based on LBM-LES." AgriEngineering 7, no. 4 (2025): 100. https://doi.org/10.3390/agriengineering7040100.
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Picking fruits from tall fruit trees manually is laborious and inefficient. Rotary-wing drones, a low-altitude carrier platform, can enhance the picking efficiency for tall fruit trees when combined with picking robotic arms. However, during the operation of rotary-wing drones, the wind field changes dramatically, and the center of gravity of the drone shifts at the moment of picking, leading to poor aerodynamic stability and making it difficult to achieve optimized attitude control. To address the aforementioned issues, this paper constructs a drone and wind field testing platform and employs the Lattice Boltzmann Method and Large Eddy Simulation (LBM-LES) algorithm to solve the high-dynamic, rapidly changing airflow field during the transient picking process of the drone. The aerodynamic structure of the drone is optimized by altering the rotor spacing and duct intake ratio of the harvesting drone. The simulation results indicate that the interaction of airflow between the drone’s rotors significantly affects the stability of the aerodynamic structure. When the rotor spacing is 2.8R and the duct ratio is 1.20, the lift coefficient is increased by 11% compared to the original structure. The test results from the drone and wind field experimental platform show that the rise time () of the drone is shortened by 0.3 s, the maximum peak time () is reduced by 0.35 s, and the adjustment time () is accelerated by 0.4 s. This paper, by studying the transient wind field of the harvesting drone, clarifies the randomness of the transient wind field and its complex vortex structures, optimizes the aerodynamic structure of the harvesting drone, and enhances its aerodynamic stability. The research findings can provide a reference for the aerodynamic optimization of other types of drones.
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Okulski, Michał, and Maciej Ławryńczuk. "A Small UAV Optimized for Efficient Long-Range and VTOL Missions: An Experimental Tandem-Wing Quadplane Drone." Applied Sciences 12, no. 14 (2022): 7059. http://dx.doi.org/10.3390/app12147059.
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Most types of Unmanned Aerial Vehicle (UAV, drone) missions requiring Vertical-Take-Off-and-Landing (VTOL) capability could benefit if a drone’s effective range could be extended. Example missions include Search-And-Rescue (SAR) operations, a remote inspection of distant objects, or parcel delivery. There are numerous research works on multi-rotor drones (e.g., quadcopters), fixed-wing drones, VTOL quadplanes, or tilt-motor/tilt-wing VTOLs. We propose a unique compact VTOL UAV optimized for long hover and long-range missions with great lifting capacity and manoeuvrability: a tandem-wing quadplane with fixed motors only. To the best of our knowledge, such a drone has not yet been researched. The drone was designed, built, and tested in flight. Construction details, its advantages, and issues are discussed in this research.
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Kim, Ga-Young, Dae-Gwan Won, Yong-Hee Kim, Sang-Eun Jeon, Sang-Ho Kim, and Seung-Soo Lee. "Investigation of physiological responses on drone noise in a laboratory condition." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 7 (2023): 1116–21. http://dx.doi.org/10.3397/in_2023_0168.
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This study analyzed the impact of drone noise on human physiology in a laboratory environment using recorded data. It focused on the noise generated by drones during hovering flight. Two drone noises with various spectral characteristics were collected through the field measurements. To assess the effect of immersion, subjects were exposed to both auditory and visual stimuli, including images and videos of the drones. Physiological responses were recorded using a 14-channel EEG device and a wrist-worn device that monitored HR, IBI, EDA, and skin temperature. A preliminary experiment was conducted with four subjects to evaluate the differences in drone types and individual physiological responses. The study also analyzed the relationship between physiological responses and physical noise characteristics and explored methods to reduce noise during physiological response collection.
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Dai, Xuanze. "Drone detection with radio frequency signals and deep learning models." Applied and Computational Engineering 47, no. 1 (2024): 92–100. http://dx.doi.org/10.54254/2755-2721/47/20241230.
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The widespread use of drones raises security, environmental, privacy, and ethical issues; therefore, effective detection by drones is important. There are several methods for detecting drones, such as wireless signal detection, photoelectric detection, radar detection, and sound detection. However, these detection methods are not accurate enough to identify drones for use. To solve this question, more robust drone detection method are needed. In addition, for different types of drones and application scenarios, different technical means need to be used for detection and identification. Based on 2-class ,4-class and 10-class problems on an open ratio frequency (RF) signal dataset, we compared the drone detection and classification performances of different machine learning with deep learning models and multi-task models which is proposed by combining different RF methods with Convolutional neural networks (CNNs). Our experimental results show that the XGBoost model achieved the latest results on this groundbreaking dataset, with 99.96% accuracy for 2-class problem, 92.31% accuracy for 4-class problem, and 74.81% accuracy for 10-class problem, which exhibits the best performance for drone detection and classification.
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Hulaj, Astrit, Eliot Bytyci, and Veronë Kadriu. "An Efficient Tasks Scheduling Algorithm for Drone Operations in the Indoor Environment." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 11 (2022): 42–57. http://dx.doi.org/10.3991/ijoe.v18i11.29977.
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This research proposes an efficient algorithm that can be applied to drones to transport materials in indoor environment. This algorithm optimizes the time and reduces energy consumption during sharing and completing tasks between different drones. In this research, the results will be achieved based on the "Earliest Time Algorithm". We have modified this algorithm, where we have reached to get much better results in terms of saving time while performing various tasks from the drone. The performance of the algorithm is tested and analyzed for three different types of tasks and depending on the weight the drone carries.
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Restás, Ágoston. "Drone Applications Fighting COVID-19 Pandemic—Towards Good Practices." Drones 6, no. 1 (2022): 15. http://dx.doi.org/10.3390/drones6010015.
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Of the recent epidemics, the impact of the COVID-19 pandemic has been particularly severe, not only putting our health at risk, but also negatively affecting our daily lives. As there are no developed algorithms for the use of drones in epidemiological situations, it is ideal to analyze the experience gained on drones so far and outline the effective methods for future good practice. The author relies on a method of analyzing widely available open information, such as images and videos available on the Internet, reports from drone users, announcements by drone manufacturers and the contents of newspaper articles. Furthermore, the author has relied on the results of the relevant literature, as well as previous experience as a drone user and fire commander. The study reveals numerous possibilities associated with drone usage in epidemic related situations, but previous applications are based on previous experience gained during a non-epidemic situation, without developed algorithms. Applications can be divided into different types of groups: drones can collect data for management and provide information to the public, perform general or special logistical tasks to support health care and disinfect to reduce the risk of spreading the epidemic.
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Loyce, Natukwasa. "Effectiveness of Drone Technology in Mapping and Controlling Malaria Outbreaks in Sub-Saharan Africa." RESEARCH INVENTION JOURNAL OF BIOLOGICAL AND APPLIED SCIENCES 4, no. 2 (2024): 60–63. https://doi.org/10.59298/rijbas/2024/426063.
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Malaria remains a critical public health issue in Sub-Saharan Africa, disproportionately affecting vulnerable populations. Innovative approaches are essential for effective surveillance and control of this vector-borne disease. This review explored the effectiveness of drone technology in mapping and controlling malaria outbreaks in the region. Drones, equipped with advanced imaging and sensing capabilities, provide significant advantages for identifying mosquito breeding sites, monitoring environmental conditions, and delivering interventions. This review examined the types of drones used, their various applications in malaria control, and the benefits they offer, such as improved data collection, enhanced operational efficiency, and increased accessibility to remote areas. However, challenges such as regulatory barriers, technical limitations, and community acceptance are also discussed. The review highlighted future prospects for drone technology, including integration with health information systems and advancements in sensor technology. A comprehensive literature review and case study analysis were conducted to assess the current state of drone applications in malaria control. This analysis underscores the transformative potential of drones in enhancing malaria control efforts and improving public health outcomes in Sub-Saharan Africa. Keywords: Drone Technology, Malaria Control, Surveillance, Vector-Borne Diseases, Public Health.
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Ashaq, Mohd, Lalit Upadhyay, Lopamudra Jena, et al. "Drones for Monitoring Soil Moisture and Optimizing Irrigation Scheduling in Horticultural Farms." Journal of Scientific Research and Reports 30, no. 11 (2024): 1118–35. http://dx.doi.org/10.9734/jsrr/2024/v30i112639.
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The efficient management of irrigation is crucial for the sustainability and productivity of horticultural farms. Traditional methods of monitoring soil moisture and scheduling irrigation can be labor-intensive and imprecise. The advent of unmanned aerial vehicles (UAVs), commonly known as drones, has opened up new possibilities for precision agriculture. Drones equipped with remote sensing technologies can provide high-resolution spatial and temporal data on soil moisture variability across a farm. This data can be used to optimize irrigation scheduling, leading to water savings, improved crop yields, and reduced environmental impact. This article reviews the current state of drone technology for soil moisture monitoring and irrigation management in horticulture. It discusses the principles of drone-based remote sensing, the types of sensors used, and the data processing and interpretation techniques involved. Case studies of successful applications of drones for irrigation optimization in various horticultural crops are presented. The article also addresses the challenges and limitations of drone-based irrigation management, including regulatory issues, data accuracy and resolution, and the need for specialized expertise. Future directions for research and development in this field are explored. With ongoing advancements in drone technology and data analytics, drones are poised to become an indispensable tool for precision irrigation management in horticulture.
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Andonov, Vasil, and Yordan Shterev. "IDENTIFYING AND ANALYZING DJI DRONE SIGNALS." ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference 5 (June 8, 2025): 43–49. https://doi.org/10.17770/etr2025vol5.8486.
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The widespread use of drones in commercial, industrial, military and security applications has led to a growing need for techniques to analyse their signals. Understanding the communication signals of drones is essential for applications such as airspace monitoring, counter-unmanned aerial vehicles technologies and electronic warfare. This defines the topicality of the topic. That is why the purpose of the study focuses on the identification and analysis of DJI drone signals using software defined radio. The research aims to find their frequencies usage, look for the drone activities in spectrogram, record them and characterize modulation types of drones, specifically the DJI Air 3 and Phantom 4. The working methods are based on using HackRF One software defined radio alongside the DragonOS operating system and HackRF Spectral Analyzer, SDR++ and Inspectrum software. Signal identification is performed in controlled urban and non-urban environments, allowing for the examination of telemetry signal. Different signal processing techniques are used including spectral analysis and modulation classification are applied to identify DJI drone ID. By analysing frequency bands, bandwidth requirements, and transmission structures, the study indicates how both drones communicate and adapt to environmental factors such as interference. Main conclusions from this paper are revealing that DJI drones use frequency hopping, orthogonal frequency division multiplexing modulation adapting itself with quadrature phase shift keying, 16 and 64 quadrature amplitude modulation depending on the enviroment. They also use Zadoff-Chu sequencies for synchronizing their drone ID packets. Having in mind this, the signal width and strength also chages based on the urban or no-urban environments that the drone is.
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Mo, Matthew, and Katarina Bonatakis. "An examination of trends in the growing scientific literature on approaching wildlife with drones." Drone Systems and Applications 10, no. 1 (2022): 111–39. http://dx.doi.org/10.1139/dsa-2021-0003.
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Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the publication types, scientific journals that works are published in, purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 86 scientific journals. The largest proportion of peer-reviewed research articles related to aquatic mammals or aquatic birds and the use or trial of drone flights for conducting population surveys, animal detection, or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary among taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and (or) locations, particularly where knowledge gaps exist.
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Kumari, Jyoti, Ravishankar M, Mandar Jatkar, Prajjwal Kumar, Poornima Arya, and Ankit Garg. "Military Grade FPV Drone for Enemy Recognition." Journal of Cyber Security, Privacy Issues and Challenges 2, no. 1 (2023): 7–13. http://dx.doi.org/10.46610/jcspic.2023.v02i01.002.
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The drone is self-made using raspberry pi and different drone components like a flight controller, gold plated motors, jio-fi Router, battery, camera, and much more equipment. The raspberry-pi present in the drone is equipped with ML-Algos which uses the camera to detect and recognize enemies and the operator using an android application which remotely controls all these. It will render real-time video from the Raspberry-pi with the help of a jio-fi router and will show which enemies are detected. Moreover, due to the first-person view, the operator will be able to know the expected position of the enemies too far planning his/her prior approaches. Moreover, the drone is equipped with a modern Network Jammer, made using the knowledge of cybersecurity, which will disable all the networks in the area so that there cannot be any reinforcement from the target enemy side. Drone helps in the surveillance process by rendering over an area and providing us with information about the place. The use of CNN and some more algorithms of machine learning helps the drone to function well in regards to recognizing any object and especially the faces of the person. Rendering of the video will be done through the android base application which will provide the rendering of the environment. The drone can be controlled in two ways. One of which is through manual use and the other way is by being fully controlled by the application that is the software. In this process, the control is given in the hand of the software where it would be controlled by the android-based application. Drones are of two types civil drones and Military drones and both them can be different in their usage as well as the manufacturing process in civil drones the payload will be less because it is not used for the officials' purpose, in the military grade FPV drone, the payload should be of the few measurements so that it can be used for the officials' purposes and others.
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Hyun Kim, Bong. "Implementation of the centralized control system for drone training." International Journal of Engineering & Technology 7, no. 3.3 (2018): 379. http://dx.doi.org/10.14419/ijet.v7i2.33.14190.
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Background/Objectives: The drones became a representative item in the IoT era. However, there is no drone pilot test system that can safely train this in the education field. Drones have very dangerous structural problems, so it is very necessary to practice them easily. Therefore, it is necessary to develop a system that can control the drones safely and easily while controlling them.Methods/Statistical analysis: In this paper, we will develop software for controlling a dedicated board platform that can securely perform ground testing by mounting four drones of motor and drive on a board (PCB). To this end, we supported various control IMU (Inertial Measurement Unit) boards for attitude control by using sensor which is the core technology of drone flight control. Also, Acceleration Data, Angular Velocity Data, Earth Magnetic Field Data, and Atmospheric Pressure Data for maintaining the altitude were used for the drone flight.Findings: In the implemented central control system, the AT chip is built in and designed to perform all control related to the flight of the drone. In addition, since it is an embedded system, we have programmed the attitude control using the sensor, the motor output setting, and the controller connection information. The CPU required for drones control can be replaced with various types of controllers besides Fno Arduino, UNO, Muiltiwii. For this purpose, the main PCB is designed so that the power supply terminal can be used for each CPU. Finally, it was developed as a setup program to correct the sensor and output of the drone.Improvements/Applications: The system implemented in this paper can easily control the drone. In addition, acceleration, angular velocity, geomagnetic field, air pressure sensor, GPS, etc. necessary for drone control can be utilized by stabilizing the initial set value. In other words, the zero point of the sensor can be captured and the signal appropriate to the current state of the drone can be stored in the processor.
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Turkanov, G. I. "Trends in State Support of Drone Aircraft Industry." Vestnik of the Plekhanov Russian University of Economics, no. 1 (January 28, 2025): 231–37. https://doi.org/10.21686/2413-2829-2025-1-231-237.
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The article deals with the development of drone aircraft industry, which is based on a number of fundamental economic and legal theories. Economic theories substantiate the necessity of state support stimulating innovation and protection of national markets, while theories of state and law provide legal base for safe and legal use of drone aircrafts. All together these theoretical approaches form the integral basis for efficient regulation and sustainable development of industry. In Russia the drone aircraft sphere is developing with considerable state support. In response to growing demand for drones in different sectors, such as transport, logistics, agriculture and defense the government take steps aimed at stimulation of production and introduction of drone technologies. Economic theories provide ideas how state and private sector could develop drone aircraft efficiently. They include concepts of state interference, competitive advantage, innovation and economy on scale. Clear understanding of economic aspects of investing in drone aircraft, the role of state grants and other types of support is important for estimating the potential of this industry and its economic impact. The development of drone aircraft systems can both strengthen defense possibilities and stimulate economic growth by creating new jobs and professional trends.
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Nishat, Humaira, Shakeel Ahmed, and Devarapally Akanksha. "Drone Detection in Restricted Areas Using Deep Learning." Jurnal Kejuruteraan 37, no. 2 (2025): 609–16. https://doi.org/10.17576/jkukm-2025-37(2)-05.
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As the use of drones becomes more widespread, the corresponding rise in drone-related intrusions poses a growing threat to public safety and privacy. Traditional anti-drone systems typically rely on radio-frequency sensors for drone tracking. This paper investigates the fusion of deep learning-based detection algorithms with surveillance cameras within the framework of radio-frequency anti-drone systems. The primary aim is to assess the efficacy of contemporary models and training methodologies in achieving precise and real-time drone detection. One of the central challenges addressed in this paper is the detection of small drones at extended distances, coupled with the demand for real-time performance. To overcome the scarcity of small drone datasets, the research team constructed a real-world dataset for comprehensive evaluations. Various iterations of the YOLO (You Only Look Once) models were compared using this dataset, with specific modifications implemented to enhance small object detection. Additionally, the image sources are diversified for training, incorporating bird images to mitigate false positives and enhance model robustness. Among the YOLO models tested, YOLOv5 exhibited superior precision, recall, and F1 score. The work delves into the impact of additional detection layers on precision, recall, and F1 score, revealing trade-offs between these metrics. The inclusion of bird images in the background training process demonstrated improvements in accuracy and recall, underlining the importance of diverse training data. An intriguing finding was observed when excluding extremely small drones and birds from the analysis, resulting in heightened precision but diminished recall. This highlights the delicate balance required in optimizing detection algorithms for different scenarios. The paper also acknowledges the need for further investigation into the generalizability of the proposed approach across various drone types.
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Roberti, Roberto, and Mario Ruthmair. "Exact Methods for the Traveling Salesman Problem with Drone." Transportation Science 55, no. 2 (2021): 315–35. http://dx.doi.org/10.1287/trsc.2020.1017.
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Efficiently handling last-mile deliveries becomes more and more important nowadays. Using drones to support classical vehicles allows improving delivery schedules as long as efficient solution methods to plan last-mile deliveries with drones are available. We study exact solution approaches for some variants of the traveling salesman problem with drone (TSP-D) in which a truck and a drone are teamed up to serve a set of customers. This combination of truck and drone can exploit the benefits of both vehicle types: the truck has a large capacity but usually low travel speed in urban areas; the drone is faster and not restricted to street networks, but its range and carrying capacity are limited. We propose a compact mixed-integer linear program (MILP) for several TSP-D variants that is based on timely synchronizing truck and drone flows; such an MILP is easy to implement but nevertheless leads to competitive results compared with the state-of-the-art MILPs. Furthermore, we introduce dynamic programming recursions to model several TSP-D variants. We show how these dynamic programming recursions can be exploited in an exact branch-and-price approach based on a set partitioning formulation using ng-route relaxation and a three-level hierarchical branching. The proposed branch-and-price can solve instances with up to 39 customers to optimality outperforming the state-of-the-art by more than doubling the manageable instance size. Finally, we analyze different scenarios and show that even a single drone can significantly reduce a route’s completion time when the drone is sufficiently fast.
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Huang, Xuan, Xu Xia, Zhibo Wang, and Mugen Peng. "Joint Drone Access and LEO Satellite Backhaul for a Space–Air–Ground Integrated Network: A Multi-Agent Deep Reinforcement Learning-Based Approach." Drones 8, no. 6 (2024): 218. http://dx.doi.org/10.3390/drones8060218.
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The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.
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Ináncsi, Mátyás. "Cybersecurity Challenges of the Civilian Unmanned Aircraft Systems." Hadmérnök 17, no. 2 (2022): 205–16. http://dx.doi.org/10.32567/hm.2022.2.14.
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Nowadays unmanned aircrafts are widely available at a reasonable price for civilians. This change in the market raises cybersecurity related concerns. In this paper we arefocusing on three aspects of the cybersecurity challenges: data protection element, cyberattack element and general concerns over drones from the Asian market. The first element is extremely important when it comes to ethical and rightful drone use. A drone fitted with a camera or a video recording device can easily violate personal data. The cyberattack element aims to make sure the user understands that their device can be hacked, and not just simply the drone itself but various devices connected to them. Lastly, we are focusing on raising awareness of using drones from the Asian market. These types of products sometimes get into the spotlight due to built-in cyberissues. This part is aimed to raise general awareness over data protection coming from third party device use.
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Frid, Alan, Yehuda Ben-Shimol, Erez Manor, and Shlomo Greenberg. "Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks." Sensors 24, no. 8 (2024): 2427. http://dx.doi.org/10.3390/s24082427.
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The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of −10 dB using the LSTM network and fused features.
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Bogolin, Amy P., Drew R. Davis, Richard J. Kline, and Abdullah F. Rahman. "A drone-based survey for large, basking freshwater turtle species." PLOS ONE 16, no. 10 (2021): e0257720. http://dx.doi.org/10.1371/journal.pone.0257720.
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Conservation concerns are increasing for numerous freshwater turtle species, including Pseudemys gorzugi, which has led to a call for more research. However, traditional sampling methodologies are often time consuming, labor intensive, and invasive, restricting the amount of data that can be collected. Biases of traditional sampling methods can further impair the quality of the data collected, and these shortfalls may discourage their use. The use of unmanned aerial vehicles (UAVs, drones) for conducting wildlife surveys has recently demonstrated the potential to bridge gaps in data collection by offering a less labor intensive, minimally invasive, and more efficient process. Photographs and video can be obtained by camera attachments during a drone flight and analyzed to determine population counts, abundance, and other types of data. In this study we developed a detailed protocol to survey for large, freshwater turtle species in an arid, riverine landscape. This protocol was implemented with a DJI Matrice 600 Pro drone and a SONY ILCE α6000 digital camera to determine P. gorzugi and sympatric turtle species occurrence across 42 sites in southwestern Texas, USA. The use of a large drone and high-resolution camera resulted in high identification percentages, demonstrating the potential of drones to survey for large, freshwater turtle species. Numerous advantages to drone-based surveys were identified as well as some challenges, which were addressed with additional refinement of the protocol. Our data highlight the utility of drones for conducting freshwater turtle surveys and provide a guideline to those considering implementing drone-mounted high-resolution cameras as a survey tool.
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Çetin, Ender, Alicia Cano, Robin Deransy, Sergi Tres, and Cristina Barrado. "Implementing Mitigations for Improving Societal Acceptance of Urban Air Mobility." Drones 6, no. 2 (2022): 28. http://dx.doi.org/10.3390/drones6020028.
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The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space.
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Sazdic-Jotic, Boban, Milenko Andric, Boban Bondzulic, Slobodan Simic, and Ivan Pokrajac. "FLEDNet: Enhancing the Drone Classification in the Radio Frequency Domain." Drones 9, no. 4 (2025): 243. https://doi.org/10.3390/drones9040243.
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Researchers are actively pursuing advancements in convolutional neural networks and their application in anti-drone systems for drone classification tasks. Our study investigates the hypothesis that the accuracy of drone classification in the radio frequency domain can be enhanced through a hybrid approach. Specifically, we aim to combine fuzzy logic for edge detection in images (the spectrograms of drone radio signals) with convolutional and convolutional recurrent neural networks for classification tasks. The proposed FLEDNet approach introduces a tailored engineering strategy designed to tackle classification challenges in the radio frequency domain, particularly concerning drone detection, the identification of drone types, and multiple drone detection, even within varying signal-to-noise ratios. The strength of this tailored approach lies in implementing a straightforward edge detection method based on fuzzy logic and simple convolutional and convolutional recurrent neural networks. The effectiveness of this approach is validated using the publicly available VTI_DroneSET dataset across two different frequency bands and confirmed through practical inference on the embedded computer NVIDIA Jetson Orin NX with radio frequency receiver USRP-2954. Compared to other approaches, FLEDNet demonstrated a 4.87% increase in accuracy for drone detection, a 13.41% enhancement in drone-type identification, and a 7.26% rise in detecting multiple drones. This enhancement was achieved by integrating straightforward fuzzy logic-based edge detection methods and neural networks, which led to improved accuracy and a reduction in false alarms of the proposed approach, with potential applications in real-world anti-drone systems. The FLEDNet approach contrasts with other research efforts that have employed more complex image processing methodologies alongside sophisticated classification models.
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Salamh, Fahad E., Umit Karabiyik, and Marcus K. Rogers. "RPAS Forensic Validation Analysis Towards a Technical Investigation Process: A Case Study of Yuneec Typhoon H." Sensors 19, no. 15 (2019): 3246. http://dx.doi.org/10.3390/s19153246.
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The rapid pace of invention in technology and the evolution of network communication has produced a new lifestyle with variety of opportunities and challenges. Remotely Piloted Aerial Systems (RPAS) technology, which includes drones, is one example of a recently invented technology that requires the collection of a solid body of defensible and admissible evidence to help eliminate potential real-world threats posed by their use. With the advent of smartphones, there has been an increase in digital forensic investigation processes developed to assist specialized digital forensic investigators in presenting forensically sound evidence in the courts of law. Therefore, it is necessary to apply digital forensic techniques and procedures to different types of RPASs in order to create a line of defense against new challenges, such as aerial-related incidents, introduced by the use of these technologies. Drone operations by bad actors are rapidly increasing and these actors are constantly developing new approaches. These criminal operations include invasion of privacy, drug smuggling, and terrorist activities. Additionally, drone crashes and incidents raise significant concerns. In this paper, we propose a technical forensic process consisting of ten technical phases for the analysis of RPAS forensic artifacts, which can reduce the complexity of the identification and investigation of drones. Using the proposed technical process, we analyze drone images using the Computer Forensics Reference Datasets (CFReDS) and present results for the Typhoon H aerial vehicle manufactured by Yuneec, Inc. Furthermore, this paper explores the availability and value of digital evidence that would allow a more practical digital investigation to be able to build an evidence-based experience. Therefore, we particularly focus on developing a technical drone investigation process that can be applied to various types of drones.
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Li, Meng, Yifei Zhao, Xinglong Xiong, and Yuzhao Ma. "Comprehensive optimization of the synchronous delivery network in the model of OTMD for traveling salesman problem with drone." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7505–19. http://dx.doi.org/10.3233/jifs-200818.
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Synchronous delivery with different vehicles, as an emerging concept of the delivery network, improves the efficiency of the modern logistics system significantly, which gradually gives birth to a new issue: the traveling salesman problem with drone (TSP-D). In this paper, we propose a one-truck-multiple-drone (OTMD) model on the base of the TSP-D. Compared with the traditional one-truck-one-drone (OTOD) and multiple drones models, our scheme introduces a united objective function into the optimization calculation. In terms of the proposed multiple levels iterative theory, we can compute the optimal synchronous delivery network that takes both the total delivery time and the number of drones into consideration. Four types of customer distributions are employed to investigate the OTMD model and its associated calculation approaches. Comparing the parameters of the optimal network in different delivery models, we study the relationship among the total delivery time, customer distribution and the number of serving drones. These simulation results verify the feasibility and practicality of the OTMD, and demonstrate the features of optimization calculation with different customer distributions, being beneficial to improve the efficiency of the model logistics system.
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KRAVTSOVA, YEVHENIIA, IRYNA UDOVENKO, and MYKHAILO SHEMIAKIN. "Analysis of Legal Regulation and Liability for the Use of Drones in Various Areas, Including Commercial Use and Public Safety." Journal of Law and Political Scienes 42, no. 3 (2024): 383–402. https://doi.org/10.5281/zenodo.14328742.
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This topic is relevant in the context of rapid technological advancements and the proliferation of drones across various sectors. The legal regulation and accountability of drone usage is crucial for ensuring safety, protecting personal and public interests, and fostering new opportunities in commercial applications. The aim of this study is to analyse the legal regulations and accountability determinations for drone usage in Ukraine, including commercial use and public safety. The key tasks involve highlighting the relevance of the topic, analysing current legislation, identifying potential gaps and issues, and formulating conclusions regarding the effectiveness of existing legal approaches and possibilities for further improvement. This study analysed the current legislation, identified the types of accountability for violating drone usage rules, and uncovered potential problems and challenges faced by aviation entities and government authorities in Ukraine.
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Panjaitan, James P., Ghani Mahendratama, and Vincentius Siregar. "Benthic habitats mapping in Pari Island, Kepulauan Seribu, Indonesia using drone and sentinel-2B imagery with object based image analysis method." BIO Web of Conferences 168 (2025): 05004. https://doi.org/10.1051/bioconf/202516805004.
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Benthic habitats are living places and shelters for various types of aquatic organisms, including seagrass, seaweed, algae, dead corals, and living corals with different types of substrates, such as rubble, sand, and mud. This habitat has the potential to change, so it is necessary to monitor it regularly by mapping shallow-water habitats using remote sensing technology such as satellite imagery and drones. This study aims to classify and map benthic habitats in the shallow waters of Pari Island using drone and satellite images using the object-based image analysis (OBIA) classification method. A total of 1052 aerial photographs with a resolution of 3.6 cm / pixel and Sentinel-2B images with a spatial resolution of 10 m were processed using the support vector machine (SVM) algorithm. Both images produced six classes: live corals, macroalgae, seagrass, dead corals with algae, sand, and rubble. The overall accuracy test results for drone imagery and satellite imagery were 81.88% and 69.57%, respectively. Based on the overall accuracy, the drone images proved to be better than Sentinel-2 imagery.
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Mohd Zaki Bahrom, Bukhari Manshoor, Badrul Aisham Md Zain, et al. "Thrust Force for Drone Propeller with Normal and Serrated Trailing Edge." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 101, no. 1 (2023): 160–73. http://dx.doi.org/10.37934/arfmts.101.1.160173.
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The drone becomes more recognized in the civilian sector; the drone's popularity becomes increases as time goes by. Nevertheless, despite the excitement of flying drones, several types of issues occur caused by the drone. In some circumstances, the aeroacoustics noise is a big concern, and quiet drone propellers would be more environmentally friendly to the surrounding area. Moreover, the noise from the drone can be a nuisance for the surrounding population and animals. Therefore, a solution needs to be proposed to reduce the sound level produced by the drone so that drone can be piloted in a surrounding area without breaking any noise level limit set by the government. Hence, the propeller's serrated trailing edge type is the proposed solution to this problem. The serrated trailing edge propeller can reduce several drone noise decibels based on past research. Thus, an investigation is conducted to study the thrust force between the normal propeller and the serrated propeller. The aerodynamic performance of the serrated propeller is analysed using computational fluid dynamic simulation and compared to that of the normal propeller. Ansys Fluent 2021 is used to solve the dependable RNG k-epsilon turbulence model. The thrust force, thrust coefficient, and lift coefficient operating on both propellers were all simulated. The results obtained by the transient approach for propellers have been validated by earlier experimental studies.
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Alkhalil, Omar. "Assessment of Factors Affecting the Use of Drones in Map Production." Advances in Environmental and Engineering Research 3, no. 3 (2022): 1. http://dx.doi.org/10.21926/aeer.2203029.
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Multi-rotor and fixed-wing drones are extensively used to collect the data needed for producing large-scale topographic maps and plans. Several types of drone products are available, and the most important one for surveyors is an orthophoto. Flight planning, the quality of the control data, the assessment of drone products, and the image processing software need to be considered when using thes e drones. In this study, we explained these concepts and discussed their theoretical and practical importance in providing standards and tools that might help drone users, surveyors, and others, in obtaining the products they desire. We also proposed a detailed methodology for evaluating the accuracy of the image processing products of the drones as aerial triangulation and orthophoto. We discussed the importance of the Ground Sampling Distance (GSD) and the ground control points to assess the absolute accuracy of the aerial triangulation of the images and the importance of performing statistical tests before evaluating the absolute horizontal accuracy of the orthophoto. The checkpoints measured using more accurate methods than drone photogrammetry were used for the assessment. In this study, we evaluated the dependence of the differences between the coordinates of these points and the coordinates of the corresponding points measured on the orthophoto based on a normal distribution, and the correlation between them, before applying international standards for determining the absolute horizontal accuracy of the orthophoto. An orthophoto covering 6.57 hectares with a horizontal accuracy of 0.362