Small Drones Research Articles

PENGEMBANGAN KOMPOSIT SERAT SABUT KELAPA SEBAGAI BAHAN PEMBUATAN RANGKA SURVEILLANCE DRONE

Coconut fiber is an alternative material to replace metal. Coconut fiber was chosen because it is easy and widely found in Indonesia and is easily decomposed naturally. Even though its strength is much lower than metal, coconut fiber can be used to make objects that do not require a lot of strength, one of which is drones. In this research, coconut fiber composites were made and tensile tested to determine their strength for use as a frame for small surveillance drones. The composite is made by mixing coconut fiber mat (as fiber) and Unsaturated Polyester (as resin) using the hand lay-up method. Coconut fiber composites are tested according to ASTM D-638 standards. Based on the tensile test results in this research, it was found that the coconut fiber composite had a tensile strength of 18 MPa. Then this number is used as a benchmark in determining the size of the surveillance drone frame to be designed. Based on the results of the calculations carried out, it was found that the thickness required for a drone frame made from coconut fiber is 4 mm

Balancing predictability and flexibility through operation volume-constrained visual flight rule operations in low altitude airspaces

The advancement in uncrewed aircraft systems such as small drones and advanced air mobility vehicles such as Electric Vertical Take Off and Landing aircraft (eVTOL) has called for airspace integration at low altitudes of both traditional aircraft, such as helicopters and new entrants, such as drones and eVTOL. Currently, the trajectories and necessary buffers around them of flights operating under visual flight rules are not possible to predict. This research proposes the use of flight mission characteristics to model the trajectory and evaluates temporal, lateral and vertical deviations to define the safety buffers which can be used to generate operation volumes, geo-fencing such low-altitude flights and separating them from other traffic in a safe and efficient manner. Real flight test data obtained for the purposes of this study and pilots’ interviews assure high fidelity and practicability of the proposal.

Analysis of Structure and Function of Ladybird Leg and Subsequent Design and Fabrication of a Simplified Leg Structure for Robotic Applications.

Many insects are able to walk vertically or upside down on both hard and soft surfaces. In beetles such as the ladybird (Coccinella septempunctata), intermolecular forces between tarsal setae on the footpads of the insects make this movement possible. In prior work, adhesion structures made from polydimethylsiloxane (PDMS) that mimic the action of the tarsal setae have been developed. It is proposed that these adhesion structures could be attached to a simplified version of the leg of a ladybird and used in practical applications. For example, the leg structures could potentially be employed in small surveillance drones to enable attachment to surfaces during flights, in order to preserve battery power. Alternatively, the structures could be used in small robotic devices to enable walking on steeply inclined surfaces. In this program of work, the morphology and movement of the leg of a ladybird were closely studied using a 3D X-ray microscope and a high-speed microscope. The positions of the tendons that facilitated movement were identified. From this knowledge, a simplified leg structure using pin-joints was designed and then fabricated using 3-D printing. The PDMS adhesion structures were then attached to the leg structure. The tendons in the actual insect leg were replicated using thread. Typical detachment forces of about 4 N indicated that the simplified leg structure was, in principle, more than capable of supporting the weight of a small device and then detach successfully. Attachment/detachment movement operations were performed using a linear actuator and controlled remotely. Therefore, proof of concept has been demonstrated for the use of such a simplified ladybird leg structure for the attachment/detachment of small robotic devices to horizontal, inclined, or vertical surfaces.

Use of a DJI Tello Drone as an Educational Platform in the Field of Control Engineering

This paper presents a hands-on pedagogical approach using a DJI Tello drone as an interactive teaching platform in the field of automatic control engineering. The DJI Tello is a small commercial quadcopter drone and includes a software development kit (SDK) that allows developers to control the Tello using various programming languages, including Python. The drone is also equipped with a large number of sensors that can be used in real-time to collect data and analyze how changes in control inputs such as thrust, pitch, roll, and yaw affect its flight path and stability. These features make the Tello a good teaching tool for demonstrating control concepts in an attractive and practical way. Two examples of pedagogical applications are presented in this paper. The first example aims to illustrate in practice how system identification can be used to create a mathematical model of the DJI Tello drone using transfer functions. The second example aims to illustrate how to design a Proportional-Integral (PI) controller and validate it after its implementation on the DJI Tello drone. Through these teaching demonstrations, it was possible to enhance cognitive learning while providing students with a better understanding of the fundamental concepts of modeling and control. It was also observed that even though the students had no background in aeronautics, using an atypical system such as a drone aroused their curiosity, encouraging them to participate, thus making the in-class demonstrations more dynamic.

VDMNav: Software Architecture for Aerodynamically Constrained Navigation on Small Fixed-Wing Drones

VDMNav: Software Architecture for Aerodynamically Constrained Navigation on Small Fixed-Wing Drones

Distance examination of livestock with drones - an effective method for assessing health and welfare.

Distance examination is an important part of veterinary investigation into ruminant herd health and welfare. The Department of Primary Industries and Regional Development (DPIRD) explored the use of drones to conduct assessments of the health and welfare status of sheep and cattle. Three methods of distance examination were compared comprising observations; from a vehicle, a "micro" category drone and a "very small" category drone. The disturbance and behavioural reactions caused by the methods were compared. Assessments of adverse health and welfare conditions by each method were compared to observations made at yarding. The preferred method was the use of the very small drone which had the best sensitivity for detection of conditions potentially associated with adverse health or welfare and the best optics at a distance that did not disturb the animals. The optics of the very small drone enabled distance examination without disturbance in both cattle and sheep. Cattle were more sensitive to the presence of the drones than sheep. The micro drone was unable to approach cattle close enough to allow undisturbed distance examination. All methods had similar specificity, however, sensitivity varied markedly. The very small drone had the best sensitivity 86% which was statistically greater than the micro drone (44%, P = 0.05) and better than the vehicle observations, which had sensitivity of 77% (not statistically significant). The selection of an appropriate drone model is essential for accurate distance examination. Distance examination of livestock with drones of suitable optic quality and resolution represents an effective method for assessing animal health and welfare.

Lightweight high output power C-band FMCW SIMO radar system for small drones

ABSTRACT Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth.

Classification of Small Drones Using Low-Uncertainty Micro-Doppler Signature Images and Ultra-Lightweight Convolutional Neural Network.

Many studies have attempted to classify small drones in response to threats posed by the technical progress of small drones. Recently, small drones have been classified utilizing convolutional neural networks (CNNs) with micro-Doppler signature (MDS) images generated from frequency-modulated continuous-wave (FMCW) radars. This study proposes a comprehensive method for classifying small drones in real-time using high-quality MDS images and an ultra-lightweight CNN. The proposed comprehensive method comprises an MDS image generation technique, which can improve the quality of MDS images generated via FMCW radars, and the ultra-lightweight CNN with high accuracy performance despite its remarkable lightness. Experimental results show that the proposed MDS image generation technique increases the accuracy of CNNs by enhancing the MDS image quality. This is further verified using the results of uncertainty quantification. The proposed ultra-lightweight CNN significantly decreases the computational cost while achieving high accuracy. Finally, we demonstrate that the proposed comprehensive method successfully classifies small drones from far distances with high efficiency and accuracy: the maximum and average accuracies for classification are 100% and 99.21%, respectively, and the numbers of parameters, nodes, and floating-point operations of the proposed ultra-lightweight CNN are approximately 4.88 K, 21.51 K, and 31.52 M, respectively.

NFE-YOLO: A Lightweight and Efficient Detection Network for Low, Slow, and Small Drones

NFE-YOLO: A Lightweight and Efficient Detection Network for Low, Slow, and Small Drones

Novel Approach For Intrusion Detection Attacks on Small Drones Using ConvLSTM Model

Novel Approach For Intrusion Detection Attacks on Small Drones Using ConvLSTM Model

Drone noise differs by flight maneuver and model: implications for animal surveys

Drones are becoming a common tool for animal monitoring; however, sound emitted from drones may disturb animals and bias survey results. Understanding noise levels produced by different flight maneuvers, altitudes (i.e., above ground level (AGL)), and drone models could mitigate animal disturbance during surveys. We measured maximum sound (dB) emitted during three flight maneuvers (hovering, flyover, and turning) among eight AGLs (15–120 m) and two vertical maneuvers (ascending and descending) for four commercially available quadcopter drone models (DJI Matrice 300, Matrice 200, Phantom 3, and Autel Evo II), accounting for wind speed and comparing to ambient (background) noise. Ascending, descending, and hovering produced more noise compared to flyover and turning maneuvers. One large drone (Matrice 200, 4.7 kg) produced more noise than the two smaller drones (Evo II, 1.2 kg and Phantom 3, 1.1 kg). However, the largest drone (Matrice 300, 6.4 kg) produced noise similar to smaller models and was the quietest among all models from 75 to 120 m AGL, providing potential size advantages with less noise disturbance. Our results indicate that flights consisting of flyover and turning maneuvers likely cause less noise disturbance than surveys with prolonged hovering over animals.

Enhanced Small Drone Detection Using Optimized YOLOv8 With Attention Mechanisms

Enhanced Small Drone Detection Using Optimized YOLOv8 With Attention Mechanisms

A Novel Magnetic Coupler with PQI Cores for Wireless Power Transfer

The magnetic coupler is a key component of the wireless power transfer (WPT) system, which greatly affects the performance of the WPT. This paper proposes a novel magnetic coupler with ferrite PQI cores for the WPT of small drones, which can enhance the magnetic coupling and improve power transfer efficiency. This magnetic coupler includes the transmitting (TX) side and receiving (RX) side. The TX side is made of a helical coil and a ferrite PQ core; and the RX side is made of a helical coil and a ferrite I core plate. Finite element simulations are used to investigate the performance of the proposed magnetic coupler with PQI cores, and compare it with a conventional planar magnetic coupler with two I core plates. In addition, an experimental platform is built to prove the validity of the proposed magnetic coupler with PQI cores. The results show that the coupling coefficient can reach 0.97, and it can exceed 0.446 even under the worst coupling conditions. Meanwhile, power transfer efficiency increases by 10.3% using the magnetic coupler with PQI cores.

Necessity and securing plan of airfield exclusive for test and evaluation infrastructure in military aviation fields

This study addresses the problem of the absence of an airfield dedicated to flight tests, particularly since the need for flight tests has increased significantly with the development of the domestic aviation industry. In particular, the development of unmanned aerial vehicles, including small drones, along with the development of manned aircraft such as KF-21 “Boramae,” marine attack helicopters, and mine-sweeping helicopters, continues to increase flight test requirements. However, developers are not able to secure sufficient flight test time owing to program schedules. Accordingly, industry/academic/research/military and programrelated institutions involved in aircraft development strongly hope to establish an airfield infrastructure dedicated to flight tests. However, to secure a new airfield, several problems must be addressed, such as civil complaints in the region and securing a large budget. Accordingly, this study analyzed various measures to secure a flight test airfield in the defense sector and proposed that a flight test airfield connected to a military airport relocation project could be the most reasonable alternative for resolving regional conflicts and securing budgets.

Statistical analysis of the radar cross section of two small fixed‐wing drones using typical flights

AbstractThe presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones.

Secure session key pairing and a lightweight key authentication scheme for liable drone services

Recent advancements in drone technology have created new application opportunities, particularly for small drones. However, these advancements raise concerns about security, adaptability, and consistency. Data security is jeopardized by flying intelligent devices. The distributed nature of drones, their accessibility, mobility, adaptability, and autonomy will all have an effect on how security vulnerabilities and threats are identified and controlled. However, attackers and cybercriminals have begun to employ drones for malevolent reasons in recent years. These attacks are frequent and can be fatal. There is also the matter of prevention to consider. The communication entities of the drone network can communicate securely via authentication procedures. Such solutions, however, must strike a balance between security and portability. However, the proposed technique is implemented to improve security to avoid attacks and provides a secure, lightweight, and proven solution to a key agreement for drone communication. A novel certificate-less Drone integration approach that depends on trusted authorities centres to help communication entities establish their key pairs while keeping those same trusted authorities centers from knowing about them has been devised. The proposed scheme results achieved higher security of 94 percent than existing schemes.

Study on application of winglets in drones

With the rapid development of drone technology, methods to improve drone performance have emerged endlessly. Reducing air resistance can significantly improve drones fight performance. The winglet is used to control the turbulence and vortices. The widespread application of winglets in the field of military and civil aviation has enabled winglets to be applied to drones to improve flight range and economic efficiency. As different winglets have different impacts on drones flight performance, this article focuses on the influence of different winglets on enhancing lift and reducing resistance of small civilian drones. In detail, three representative winglets are taken as the specific research objects, including the fusion winglet, the double fork scimitar winglets, and wingtip fence. The aerodynamic characteristics and the structural designs are comprehensively introduced. The importance of winglets in improving aircraft performance, energy saving and environmental protection is briefly explained. This article may offer a reference for the design of drones.

DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks.

Drones are increasingly capturing the world's attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported. To solve this issue, this article introduces an innovative deep learning method proposed to effectively distinguish between drones and birds. Evaluating the suggested approach with a carefully assembled image dataset demonstrates exceptional performance, surpassing established detection systems previously proposed in the literature. Since drones can appear extremely small compared to other aerial objects, we developed a robust image-tiling technique with overlaps, which showed improved performance in the presence of very small drones. Moreover, drones are frequently mistaken for birds due to their resemblances in appearance and movement patterns. Among the various models tested, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, the SqueezeNet model exhibited superior performance for medium area ratios, achieving higher average precision (AP) of 0.770. In addition, SqueezeNet's superior AP scores, faster detection times, and more stable precision-recall dynamics make it more suitable for real-time, accurate drone detection than the other existing CNN methods. The proposed approach has the ability to not only detect the presence or absence of drones in a particular area but also to accurately identify and differentiate between drones and birds. The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. Bird Detection Challenge. We have also tested the performance of the proposed model on an unseen dataset, further validating its better performance.

Torrential Hazards’ Mitigation Measures in a Typical Alpine Catchment in Slovenia

Different sediment-related disasters due to torrential hazards, such as flash floods, debris flows, and landslides, can occur in an Alpine torrential catchment. When protecting infrastructure and human lives, different structural and non-structural protection measures can be used to mitigate permanent and future risks. An overview of the mitigation measures constructed near the Krvavec ski resort in northwest Slovenia (Central Europe) is presented. In May 2018, an extreme debris flood occurred in this area, causing significant economic damage. After the May 2018 event, different field investigations (i.e., geological and topographic surveys) and modeling applications (e.g., hydrological modeling, debris flow) have been conducted with the purpose of preparing the required input data for the design of protection measures against such disasters in future—due to climate change, more disasters are expected to happen in this torrential watershed. The mitigation includes the restoration of local streams, the construction of a large slit check dam for sediment retention, the construction of several smaller check dams and the construction of 16 flexible net barriers with an estimated ~8000 m3 retention volume for controlling in-channel erosion in steep torrential streams. Additionally, in order to observe and monitor potential future extreme events, an extensive monitoring system has been established in the investigated area. This monitoring system will cover measurements of flexible net corrosion, the estimation of concrete abrasion at check dams, periodical geodetic surveys using small drones (UAV), hydro-meteorological measurements using rainfall gauges and water level sensors. The recent extreme floods of August 2023 also hit this part of Slovenia, and this combination of technical countermeasures withstood the event and prevented large amounts of coarse debris from being transported to the downstream section and devastating infrastructure, as was the case in May 2018 during a less extreme event. Therefore, such mitigation measures can also be used in other torrential catchments in the Alpine environment.

Drones have boots: Learning from Russia’s war in Ukraine

ABSTRACT Before Russia’s 2022 invasion of Ukraine, security studies scholars were myopic about small drones’ enabling functions and tactical benefits. They were preoccupied with drone impacts on international security and the ethical dimensions of counterterrorism drone strikes. Similarly, literature on the revolution in military affairs has examined emerging drone technologies based on their strategic advantages. “Low-tech” drone innovations have received less attention. The war has highlighted the collective magnitude of these omissions. At first, scholars followed extant predictions by concluding that large drones did not revolutionize warfare, proliferated slowly, and were too costly and complex to operate. Yet, one year into the war, thousands of drones—scouts, loitering grenades, drone bomblets, and suicide drones—are defying the field’s assumptions of their uselessness sans air superiority. Contrary to most theoretical expectations, small drones in Ukraine are changing battlefield dynamics from lower airspace. Scholars must begin to study drone diversity in modern wars.