Small Drones Research Articles

New Trends in the Procurement and Use of Unmanned Aerial Vehicles

Based on the presented classification of unmanned aerial vehicles (UAVs), the specifics of their use in modern conflicts are examined. The main trends in the procurement of UAVs are analyzed. It is indicated that the use of UAVs in combat is not limited to the functions of reconnaissance, target designation and strikes, as well as the detection and destruction of terrorist groups, as was the case in Afghanistan and Iraq conflicts. Thе UAVs are increasingly used within the framework of reconnaissance and strike complexes on the battlefield. The RD and production of small and mini-drones, as well as of unmanned aerial vehicles capable of remaining invulnerable to air defense systems, are actively developing. Other modern technologies of unmanned systems are being advanced. Due to the fact that in recent years the trend of massive use of small and mini-UAVs in conflicts has prevailed, supplies to the armed forces of various countries of heavy and a number of medium (tactical) high-altitude and medium-altitude drones are decreasing, while the purchases of small drones, as well as light civilian UAVs with reconfigured software, are increasing.

Networked Control of a Small Drone Resilient to Cyber Attacks

With increasing advances in networked systems and networked control systems in everyday life, the problem of cybersecurity becomes crucial. Moreover, in some applications like small UAVs, the safety and integrity of the system and its surroundings are highly susceptible to cyberattacks. In this context, the current paper proposes a resilient networked control approach. The control structure is split into an inner and an outer loop. The outer position control loop uses measurements from motion cameras connected to a remote computer, while the commands are sent through the network. We consider the resilience problem for two types of cyberattacks: denial of service (DoS), emulated as an increase in the network transmission delay, and man in the middle (MitM), emulated as additive input disturbances. The mitigation for the DoS attack is performed through the help of a reference governor (RG), which uses the delay estimates and the system’s model to predict future safety violations and adapts the reference accordingly. The MitM attack is mitigated by an unknown input disturbance observer (UIDO) together with a RG. Experimental results on a Parrot Mambo drone show that both types of attacks are rejected successfully, ensuring a safe and stable flight.

Droplet Shape Representation Using Fourier Series and Autoencoders

The shape of liquid droplets in air plays an important role in the aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such systems. It is desired to have a minimal representation of droplet shapes using as few parameters as possible to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two-real-number representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error of ∼10−3. Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable.

No Drone’s Sky: Full Spectrum Drone Surveillance and Neutralisation Concept for Enhanced Counter-UAS Framework

Unmanned Aircraft Systems (UAS), commonly known as drones, have witnessed substantial global proliferation in the past decade. Their constructive applications hold the promise of being a useful, yet critical component in creating a more efficient society with the enhancement of safety, efficiency, and facilitating advancements in various domains, ultimately contributing to our modern daily lives. However, the escalating dependence on computer and communication technologies renders, especially small, UAS susceptible to various threats, posing risks to public safety, national security, and individual privacy. Addressing these concerns necessitates the development of innovative technologies designed to detect, track, identify, and eliminate UAS in a manner that upholds safety, security, and privacy. A Counter-Unmanned Aircraft System (C-UAS) is defined as a system or apparatus capable of legally and securely incapacitating, disrupting, or assuming control over an UAS. Recent years have witnessed significant research endeavours aimed at detecting and eliminate drone threats. Detection methodologies encompass acoustic, visual, passive radio frequency, radar, and data fusion techniques, while neutralisation strategies encompass physical capture and jamming approaches. This paper, delving into the realm of small drone surveillance, is the opening segment of a three-part series aims to envision a C-UAS framework; it provides an exhaustive review of existing literature in the domain of UAS surveillance, delineating the challenges associated with countering unauthorised or unsafe drone operations, and evaluating the trajectory of detection to prepare against UAS-induced threats. Therefore, the fundamental objective of this paper is to offer a comprehensive surveillance baseline for a structured vision to a C-UAS framework, thus fostering a research community dedicated to the secure integration of drones into the airspace system.

Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests.

Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.

A C4 Software for Anti Drone System

Mini unmanned aerial vehicles (UAVs), commonly known as small drones, have seen outstanding advancements in recent years and have been used in a variety of fields. However, their potential misuse for illegal activities and the risks they pose to safety and privacy have raised concerns. To address these issues, we propose a Command, Control, Communications, and Computers (C4) software able to manage and control anti-drone systems. Our software solution includes an easy-to-use dashboard that processes and displays video data from surveillance sensors. It incorporates AI-powered functionalities, including object detection, target tracking, and classification of small drones. At the inference stage, the network models for drone detection and classification functionalities have achieved an accuracy exceeding 96 %. We have evaluated the effectiveness of our solution by deploying it in a no-fly zone, where it successfully identified and tracked drones in near real-time. The proposed control system provides unified information with which the entire anti-drone process can be managed starting from the detection of each threat.

Investigations into optimum non‐periodic bursts scheme in radar pulse train designed for fixed‐wing drone detections by multi‐function radar

AbstractDrone surveillance with multi‐function radar (MFR) can benefit a lot from the careful radar pulse train organisation into dedicated sub‐RPTs serving particular needs of tasks. In contemporary scenarios of MFR applications, where navigation, search and rescue, and natural environment safeguarding are to be shared, of particular importance is a case where airborne small drone detection radar performs other tasks concurrently. Resource sharing within the radar unit is based on variables and unpredictable circumstances, leading to strains in resources in MFR. Consequently, such constraints limit MFR functioning with optimal drone sensitivity for its own sake. To ensure a high quality of radar services performed quickly, with a low DC power consumption, the task sharing approach by the common MFR platform, non‐periodic bursts emission of radar pulse energy has gained our major attention. At the same time, it was assumed that bursts feature periodic time intervals between radar pulses, which is a favourable circumstance in the control and implementation of signal processing. Each sub‐RPTs is designed to cope with particular detection tasks. One to four linear polarisations (VV, VH, HV, HH) are activated. The focus of the investigation was on the radar emitting a few thousand pulses per second, but only a few 10 pulses per second were expected to be required to detect small airborne drones. The core of the research study was the optimisation process of the scheme operating with 4 to 16 pulse bursts optimised for drone detection with a modest use of resources at MFR. While performing several activities, the X‐band MFR platform called ENAVI was developed in‐house. The proposed non‐periodic burst scheme optimisation approach was validated with in‐field tests with the ENAVI radar model. The in‐air target was a 3‐metre fixed‐wing drone having low RCS and flying at different altitudes with a speed close to 200 km/h. The radar antenna was a 26 dBi low‐profile dual‐polarisation array making it feasible to detect drones remaining a few degrees off the antenna breadboard direction. A very high likelihood of detection of drones from a few kilometres distance was demonstrated within one second. The authors present the effectiveness of the cell‐averaging technique which proved to correctly detect drones within a 7‐km range, and how MFR radar echoes can be used for basic weather surveillance. One vertical VV polarisation was used for drone detection and two VV and VH polarisations for weather observation. Weather radar capabilities were examined against heavy rain clouds imposing a hazard to the safety of drone flights.

When and What to Conform to: Product Framing Strategies in the U.S. Small Drone Market

When and What to Conform to: Product Framing Strategies in the U.S. Small Drone Market

Modeling Energy Consumption of Small Drones for Swarm Missions

Modeling Energy Consumption of Small Drones for Swarm Missions

Unveiling Territorialities: Small Drones for Ethnographic Research on Environmental Conflicts

Researching environmental conflicts can be a challenging ethnographic endeavor, especially when their causes are influenced (in)directly by illicit stakeholders and activities. In this article we explore the potential of small drones for better grasping the complexity of environmental conflicts from an activist research and an engaged ethnography standpoint. Through a case study in Michoacán (Mexico), we show how we used small drones for ethnographic research in three different stages: before takeoff, during flights, and after landing. We also show how drones helped us unveil illegal and criminal territorialities previously unnoticed in the study area, which were key to understanding the environmental conflict, its root causes, and its present dynamics. Based on our findings, we present a methodological proposal for using small drones for ethnographic research. We also discuss some safety and ethical considerations related to our proposal. We conclude by suggesting some avenues for future research to further explore the ethnographic potential of small drones in environmental conflicts and other research areas.

Development, Testing, and Thermoforming of Thermoplastics Reinforced with Surface-Modified Aramid Fibers for Cover of Electronic Parts in Small Unmanned Aerial Vehicles Using 3D-Printed Molds.

This paper presents the development, characterization, and testing of PP/PE-g-MA composites with 10 and 15 wt% surface-modified aramid fibers, and aluminum-based pigment, as covers for a small drone body for collision protection. The successful fiber surface modification with SiO2 by the sol-gel method using TEOS was confirmed by FTIR, SEM, and EDS analyses. The composites were characterized by FTIR and SEM analyses and surface energy and water contact angle measurements and tested in terms of tensile, flexural, impact, and thermal properties. The materials exhibited hydrophobic character and compact and uniform morphostructures, with increased surface energy with fiber content owed to improved adhesion between modified fibers and the matrix. Compared to the control sample, composites with modified fibers showed an increase by 20% in tensile strength, and 36-52% in the modulus, and an increase by 26-33% in flexural strength and 30-47% in the modulus, with higher values at room temperature. Impact resistance of modified fiber composites showed an increase by 20-40% compared to the control sample, due to improved interaction between SiO2-modified fibers and maleic anhydride, which inhibits crack formation, allowing higher energies' absorption. The composites were vacuum-thermoformed on 3D-printed molds as a two-part cover for the body of a drone, successfully withstanding the flight test.

Building the Lesser Caucasus Monumental Landscape during the Bronze Age: Life and Death on the Javakheti Plateau (Georgia)

Abstract Surveys carried out in the Javakheti Plateau during the last decade have improved our knowledge of the Bronze Age archaeology of this highland zone of the Lesser Caucasus of Georgia. Surveys have been conducted all over the area surrounding the Mt. Chikiani volcano, along the southern terraces of the Tsalka Reservoir, and some of the north-western slopes of Mt. Paravani. All these territories have yielded impressive evidence of megalithic monuments, stone-walled villages, kurgans, basalt and schist quarries, as well as obsidian extraction and production areas. Among these latter is an important circular platform made of basalt boulders, above which a workshop for the manufacture of obsidian bifacial arrowheads and spearheads was discovered. All the aforementioned features have been GPS-recorded and photographed with the use of a non-professional small drone. They show that the entire region is a monumental landscape, which most probably started to be built around the beginning of the Bronze Age, as many of the lithic finds would suggest.

FPGA-Based Acceleration of Polar-Format Algorithm for Video Synthetic-Aperture Radar Imaging

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies.

An iterative two-phase optimization method for heterogeneous multi-drone routing problem considering differentiated demands

Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.

Implementing Small Commercial Drones in Land Forces Operations: Considerations for Optimizing ISR (Intelligence, Surveillance, and Reconnaissance)

Abstract This study explores the use of small commercial drones in tactical-level military actions, with a particular focus on optimizing ISR (Intelligence, Surveillance, and Reconnaissance) capabilities. During a 3-day tactical exercise, we analyzed the performance of 9 commercial drones, including DJI Mini 3, DJI Avata, DJI Mini 4, and DJI Mavic. The study demonstrated that these drones provide essential capabilities for real-time information gathering, facilitating rapid decision-making and improving the efficiency of military operations. Significant challenges were also identified, such as limited battery autonomy and vulnerability to adverse weather conditions. The results highlight the need for specialized training programs and an integrated approach that combines advanced technology with military tactics. Our findings suggest that commercial drones can significantly enhance the efficiency of military operations, but increased attention is required to understand the challenges that may arise due to the technological limitations of small commercial drones.

Pitot tube measure-aided air velocity and attitude estimation in GNSS denied environment

This paper addresses the problem of estimating air velocity and gravity direction for small autonomous fixed-wing drones in GNSS11Global Navigation Satellite Systems.-denied environments. The proposed solution uses a minimal sensor suite, relying on Pitot tube measurements and Inertial Measurement Unit (IMU) signals, including only gyrometers and accelerometers. The approach combines the Riccati observer and Equivariant Filter designs, using an over-parametrization technique to design an observer on SO(3)×R3 and subsequently re-project to S2×R3 to estimate the gravity direction. The system’s observability is analyzed, and local exponential stability of the origin of the observer error is demonstrated as long as the aircraft attitude is persistently exciting. The observer was evaluated using simulated and real flight data to showcase the estimator’s performance.

Hierarchical Flight Control System for Tilt-Rotor Drones

This paper presents a hierarchical flight control system for tilt-rotor drones. The offered approach performs high-level mission goals by gradually confirming them into machine-level instructions. The learned data from numerous sensors is spread backside to the greater levels for sensitive decision making. Each vertical take-off and landing drone is linked through regular wireless communication rules for accessible multi-agent facility. The proposed flight control system has been effectively employed on several small tilt-rotor drones and validated in some applications. Solutions from waypoint navigation, a probabilistic chase-evasion competition and vision-based object chasing show the capability of the recommended method for intelligent flying drones.

Drone Technology and it’s Application

Drones were first used by the military, but now they're not just for the army – they're for regular folks too. People started using them in the 2000s, and they're not just for serious stuff like analyzing power system faults. Now, we call them drones, and they're like flying robots. You can use them for all kinds of fun things like taking cool pictures, exploring hard-to- reach places, and even joining drone competitions. They've become a big deal on social media and in business competitions. Lately, smart people are working on making tiny drones that can fly by themselves, especially in indoor spaces. The good news is that the parts that make drones work, like tiny computers, sensors, batteries, and ways for them to talk wirelessly, are getting smaller and cheaper. So, soon, we might see small drones everywhere, doing all sorts of stuff in our daily lives at prices we can all afford. But, as drones become more popular, there are some worries. Imagine if someone used a drone to spy on you in your private space – that's not cool! And when drones fly in busy places like cities and airports, there could be problems. That's why we need rules and checks to make sure things don't go wrong. So, while drones are super cool and fun, we also need to make sure everyone stays safe and feels secure. Keywords--Unmanned Aerial Vehicle, Drone, Drone Features, Technology, Drone usage fields.

Enhancing Security and Privacy in Small Drone Networks Using 6G-IOT Driven Cyber Physical System

Enhancing Security and Privacy in Small Drone Networks Using 6G-IOT Driven Cyber Physical System

To house or oust: Honey bee (Apis mellifera) colonies can evaluate and evict drones of low quality

Across the animal kingdom, males advertise their quality to potential mates. Males of low reproductive quality, such as those that are sick, may be excluded from mating. In eusocial species, there is some evidence that reproductive females gauge the quality of their mates. However, males often spend much more time with non-reproductive females when being raised or when returning from unsuccessful mating flights. Do non-reproductive workers evaluate the quality of male reproductives? Here we address this question using male honey bees (Apis mellifera), called drones, as a model. We generated immune-challenged drones by injecting them with lipopolysaccharide and tested: 1) do workers evict immune-challenged drones from their colony, 2) do cuticular hydrocarbon (CHC) profiles, body size, or mass change when drones are immune-challenged, and 3) are these changes used by workers to exclude low quality males from the colony? We found that an immune challenge causes changes in CHC profiles of drones and reduces their body mass. Workers selectively evict small and immune-challenged drones who, themselves, do not self-evict. This work demonstrates that some eusocial males undergo an additional layer of scrutiny prior to mating mediated by the non-reproductive worker caste.Significance statementMales of some species must advertise their quality to mates but, in the case of eusocial species, must they also advertise their quality to nestmates? By manipulating honey bee male quality, we found that small and immune-challenged drones are evicted from colonies overnight. Workers may not use a drone’s cuticular hydrocarbon profile to make this assessment. This is a new example of social immunity expressed against adult males and an example of worker involvement in reproductive decisions.