Search Results (19)

This paper explores unconventional configurations of rotary-wing unmanned aerial vehicles (UAVs), focusing on designs that transcend the limitations of traditional ones. Through innovative rotor arrangements, refined airframe structures, and novel flight mechanisms, these advanced designs aim to significantly enhance performance, versatility, and functionality. Rotary-wing UAVs that deviate markedly from conventional models in terms of mechanical topology, aerodynamic principles, and movement modalities are rigorously examined. These unique UAVs are categorized into four distinct groups based on their mechanical configurations and dynamic characteristics: (1) UAVs with tilted or tiltable propellers, (2) UAVs featuring expanded mechanical structures, (3) UAVs with morphing multirotor capabilities, and (4) UAVs incorporating groundbreaking aerodynamic concepts. This classification establishes a structured framework for analyzing the advancements in these innovative designs. Finally, key challenges identified in the review are summarized, and corresponding research outlooks are derived to guide future development in rotary-wing drone technology. Full article

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 ($t_r$) of the drone is shortened by 0.3 s, the maximum peak time ($t_p$) is reduced by 0.35 s, and the adjustment time ($t_s$) 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. Full article

Hover-capable unmanned aerial vehicles (UAVs), including rotary-wing UAVs such as unmanned helicopters, multi-rotor drones, and tilt-rotor UAVs, are widely employed due to their hovering capabilities. In recent years, tilt-rotor aircraft, which offer both vertical takeoff and landing as well as rapid maneuverability, have increasingly become a research focus. This paper first proposes a design concept for a flying-wing configuration tilt-rotor tri-rotor UAV, detailing the selection of airfoils and the calculation of aerodynamic parameters. To address the specific operational requirements and flight characteristics of this UAV, a specialized tilting mechanism was developed, and a flight control system was designed and implemented using classical PID control methods. Finally, a prototype of the tilt-rotor tri-rotor UAV was fabricated and subjected to flight tests. The results from both simulations and flight tests confirmed that the UAV met the design performance criteria and that the control method was effective. Full article

The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects. Full article

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance. Full article

This study presents a computational fluid dynamics (CFD) based approach to determine the optimal positioning for an atmospheric turbulence sensor on a rotary-wing uncrewed aerial vehicle (UAV) with X8 configuration. The vertical ($z_{BF}$) and horizontal ($x_{BF}$) distances of the sensor to the UAV center to reduce the effect of the propeller-induced flow are investigated by CFD simulations based on the $k-ϵ$ turbulence model and the actuator disc theory. To ensure a realistic geometric design of the simulations, the tilt angles of a test UAV in flight were measured by flying the drone along a fixed pattern at different constant ground speeds. Based on those measurement results, a corresponding geometry domain was generated for the CFD simulations. Specific emphasis was given to the mesh construction followed by a sensitivity study on the mesh resolution to find a compromise between acceptable simulation accuracy and available computational resources. The final CFD simulations (twelve in total) were performed for four inflow conditions (2.5 m s⁻¹, 5 m s⁻¹, 7.5 m s⁻¹ and 10 m s⁻¹) and three payload configurations (15 kg, 20 kg and 25 kg) of the UAV. The results depend on the inflows and show that the most efficient way to reduce the influence of the propeller-induced flow is mounting the sensor upwind, pointing along the incoming flow direction at $x_{BF}$ varying between 0.46 and 1.66 D, and under the mean plane of the rotors at $z_{BF}$ between 0.01 and 0.7 D. Finally, results are then applied to the possible real-case scenario of a Foxtech D130 carrying a CSAT3B ultrasonic anemometer, that aims to sample wind with mean flows higher than 5 m s⁻¹. The authors propose $x_{BF}=1.7 \mathrm{m}$ and $z_{BF}=20 \mathrm{c}\mathrm{m}$ below the mean rotor plane as a feasible compromise between propeller-induced flow reduction and safety. These results will be used to improve the design of a novel drone-based atmospheric turbulence measurement system, which aims to combine accurate wind and turbulence measurements by a research-grade ultrasonic anemometer with the high mobility and flexibility of UAVs as sensor carriers. Full article

The transmission mechanism of artificial flapping-wing drones generally needs low weight and the fewest interconnecting components, making their development challenging. The four-bar Linkage mechanism for flapping actuation has generally been used till now with complex and heavy connecting designs, but our proposed novel perpendicularly organized 3-cylindrical joint mechanism is designed to be unique and lighter weight with smooth functioning performance. The proposed prototype transforms the rotary motion of the motor into a specific angle of flapping movement, where the dimensions and specifications of the design components are proportional to the obtained flapping angle. Power consumption and flapping actuation can be monitored by adjusting the motor’s rotational speed to control the individual wing in this mechanism. The proposed mechanism consists of a crank with three slightly slidable cylindrical joints perpendicularly arranged to each other with a specified distance in a well-organized pattern to produce a flapping movement at the other end. In order to examine the kinematic attributes, a mathematical process approach is formulated, and kinematic simulations are performed using SIMSCAPE multibody MATLAB, PYTHON programming and COMPMECH GIM software. The proposed invention’s real-time test bench prototype model is designed, tested and analyzed for flapping validation. Full article

In recent years, industries such as oil and gas production, waste management, and renewable natural gas/biogas have made a concerted effort to limit and offset anthropogenic sources of methane emissions. However, the state of emissions, what is emitting and at what rate, is highly variable and depends strongly on the micro-scale emissions that have large impacts on the macro-scale aggregates. Bottom-up emissions estimates are better verified using additional independent facility-level measurements, which has led to industry-wide efforts such as the Oil and Gas Methane Partnership (OGMP) push for more accurate measurements. Robust measurement techniques are needed to accurately quantify and mitigate these greenhouse gas emissions. Deployed on both fixed-wing and multi-rotor unmanned aerial vehicles (UAVs), a miniature tunable diode laser absorption spectroscopy (TDLAS) sensor has accurately quantified methane emissions from oil and gas assets all over the world since 2017. To compare bottom-up and top-down measurements, it is essential that both values are accompanied with a defensible estimate of measurement uncertainty. In this study, uncertainty has been determined through controlled release experiments as well as statistically using real field data. Two independent deployment methods for quantifying methane emissions utilizing the in situ TDLAS sensor are introduced: fixed-wing and multi-rotor. The fixed-wing, long-endurance UAV method accurately measured emissions with an absolute percentage difference between emitted and mass flux measurement of less than 16% and an average error of 6%, confirming its suitability for offshore applications. For the quadcopter rotary drone surveys, two flight patterns were performed: perimeter polygons and downwind flux planes. Flying perimeter polygons resulted in an absolute error less than 36% difference and average error of 16.2%, and downwind flux planes less than 32% absolute difference and average difference of 24.8% when flying downwind flux planes. This work demonstrates the applicability of ultra-sensitive miniature spectrometers for industrial methane emission quantification at facility level with many potential applications. Full article

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively. Full article

This paper presents the results of a large scale, drone-based aerial survey in northeastern Jordan. Drones have rapidly become one of the most cost-effective and efficient tools for collecting high-resolution landscape data, fitting between larger-scale, lower-resolution satellite data collection and the significantly more limited traditional terrestrial survey approaches. Drones are particularly effective in areas where anthropogenic features are visible on the surface but are too small to identify with commonly and economically available satellite data. Using imagery from fixed-wing and rotary-wing aircraft, along with photogrammetric processing, we surveyed an extensive archaeological landscape spanning 32 km² at the site of Wadi al-Qattafi in the eastern badia region of Jordan, the largest archaeological drone survey, to date, in Jordan. The resulting data allowed us to map a wide range of anthropogenic features, including hunting traps, domestic structures, and tombs, as well as modern alterations to the landscape including road construction and looting pits. We documented thousands of previously unrecorded and largely unknown prehistoric structures, providing an improved understanding of major shifts in the prehistoric use of this landscape. Full article

In alpine environments, many plants, bacteria, and fungi contain ice nuclei (IN) that control freezing events, providing survival benefits. Once airborne, IN could trigger ice nucleation in cloud droplets, influencing the radiation budget and the hydrological cycle. To estimate the atmospheric relevance of alpine IN, investigations near emission sources are inevitable. In this study, we collected 14 aerosol samples over three days in August 2019 at a single site in the Austrian Alps, close to a forest of silver birches, which are known to release IN from their surface. Samples were taken during and after rainfall, as possible trigger of aerosol emission by an impactor and impinger at the ground level. In addition, we collected aerosol samples above the canopy using a rotary wing drone. Samples were analyzed for ice nucleation activity, and bioaerosols were characterized based on morphology and auto-fluorescence using microscopic techniques. We found high concentrations of IN below the canopy, with a freezing behavior similar to birch extracts. Sampled particles showed auto-fluorescent characteristics and the morphology strongly suggested the presence of cellular material. Moreover, some particles appeared to be coated with an organic film. To our knowledge, this is the first investigation of aerosol emission sources in alpine vegetation with a focus on birches. Full article

Many drone platforms have matured to become nearly optimal flying machines with only modest improvements in efficiency possible. “Chimera” craft combine fixed wing and rotary wing characteristics while being substantially less efficient than both. The increasing presence of chimeras suggests that their mix of vertical takeoff, hover, and more efficient cruise is invaluable to many end users. We discuss the opportunity for flapping wing drones inspired by large insects to perform these mixed missions. Dragonflies particularly are capable of efficiency in all modes of flight. We will explore the fundamental principles of dragonfly flight to allow for a comparison between proposed flapping wing technological solutions and a flapping wing organism. We chart one approach to achieving the next step in drone technology through systems theory and an appreciation of how biomimetics can be applied. New findings in dynamics of flapping, practical actuation technology, wing design, and flight control are presented and connected. We show that a theoretical understanding of flight systems and an appreciation of the detail of biological implementations may be key to achieving an outcome that matches the performance of natural systems. We assert that an optimal flapping wing drone, capable of efficiency in all modes of flight with high performance upon demand, might look somewhat like an abstract dragonfly. Full article

The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency. Full article

The increasing availability and sinking costs of unmanned aerial vehicles (UAVs), commonly known as drones, has resulted in these devices becoming relatively commonplace on archaeological sites. The advantages of being able to rapidly obtain bespoke high-resolution images from the air are conspicuous to anyone familiar with archaeological fieldwork; meanwhile the possibilities of subsequently processing such images together with their metadata to obtain digital elevation models (DEMs) and three-dimensional (3-D) models provide additional bonuses to analysis and interpretation. The recent use of a rotary-wing drone by the Western Harra Survey (WHS), an archaeological project co-directed by the author in the “Black Desert”, or Harra, of north-eastern Jordan, showcases these advantages in the context of a landscape that (a) is subject to negligible transformation processes and (b) is difficult to access, both by vehicle and on foot. By using processed drone imagery to record in detail prehistoric basalt structures visible on the surface and their surroundings, morphological site typologies hypothesised from satellite imagery were confirmed, relative dating within sites ascertained, structural features and damage documented, spatial relationships to natural resources established, offsite features traced, modern threats to heritage catalogued, and practically inaccessible sites investigated. Together, these results, most of which were only obtainable and all of which were obtained more rapidly by using a drone, represent significant insights into this underrepresented region, and provide a case-study for the benefits of these devices in other landscapes of a similar nature. Full article

Terrestrial ecosystems can influence atmospheric processes by contributing a huge variety of biological aerosols (bioaerosols) to the environment. Several types of biological particles, such as pollen grains, fungal spores, and bacteria cells, trigger freezing processes in super-cooled cloud droplets, and as such can contribute to the hydrological cycle. Even though biogenic particles are known as the most active form of ice nucleation particles (INPs), the transport to high tropospheric altitudes, as well as the occurrence in clouds, remains understudied. Thus, transport processes from the land surface into the atmosphere need to be investigated to estimate weather phenomena and climate trends. To help fill this knowledge gap, we developed a drone-based aerosol particles sampling impinger/impactor (DAPSI) system for field studies to investigate sources and near surface transport of biological INPs. DAPSI was designed to attach to commercial rotary-wing drones to collect biological particles within about 100 m of the Earth’s surface. DAPSI provides information on particulate matter concentrations (PM₁₀ & PM_2.5), temperature, relative humidity, and air pressure at about 0.5 Hz, by controlling electrical sensors with an onboard computer (Raspberry Pi 3). Two remote-operated sampling systems (impinging and impacting) were integrated into DAPSI. Laboratory tests of the impinging system showed a 96% sampling efficiency for standardized aerosol particles (2 µm polystyrene latex spheres) and 84% for an aerosol containing biological INPs (Betula pendula). A series of sampling missions (12 flights) were performed using two Phantom 4 quadcopters with DAPSI onboard at a remote sampling site near Gosau, Austria. Fluorescence microscopy of impactor foils showed a significant number of auto-fluorescent particles < 0.5 µm at an excitation of 465–495 nm and an emission of 515–555 nm. A slight increase in ice nucleation activity (onset temperature between −27 °C and −31 °C) of sampled aerosol was measured by applying freezing experiments with a microscopic cooling technique. There are a number of unique opportunities for DAPSI to be used to study the transport of bioaerosols, particularly for investigations of biological INP emissions from natural sources such as birch or pine forests. Full article