Academic literature on the topic 'Fixed-wing drone'

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.

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.

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.

Fixed-wing Vertical Takeoff and Landing (VTOL) drones have been widely researched and applied because they combine the advantages of both rotorcraft and fixed-wing drones. However, the research on the transition mode of this type of drone has mainly focused on completing the process quickly and stably, and the application potential of this mode has not been given much attention. The objective of this paper is to routinize the transition mode of compound VTOL drones, i.e., this mode works continuously for a longer period of time as a third commonly used mode besides multi-rotor and fixed-wing modes, which is referred to as the hybrid mode. For this purpose, we perform detailed dynamics modeling of the drone in this mode and use saturated PID controllers to control the altitude, velocity, and attitude of the drone. In addition, for more stable altitude control in hybrid mode, we identify the relevant parameters for the lift of the fixed-wings and the thrust of the actuators. Simulation and experimental results show that the designed control method can effectively control the compound VTOL drone in hybrid mode. Moreover, it is proven that flight in hybrid mode can reduce the flight energy consumption to some extent.

This project aims to design a hybrid drone with fixed wing for the stable cruising as well as multi-copters for the vertical take-off and landing (VTOL) operations. Drones are utilized for number of applications leading from transportation to the surveillance purposes. Some drones are popular for their stable operation, while other for optimum landing and takeoff. Therefore, numerical analysis was performed on propeller, landing gear as well as on whole structure of the drone. The observed result from CFD analysis show that the velocity distribution had maximum velocity of 40 m/s at the mid span of the drone. In addition, the maximum stress obtained was on the landing gear with approximate value of 185 MPa which is due to the weigh and payload of the drone. The final model was built after the analytical and numerical analysis in order to achieve sustainable and reliable prototype.

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.

Drones may be valuable in polar research because they can minimize researcher activity and overcome logistical, financial, and safety obstacles associated with wildlife research in polar regions. Because polar species may be particularly sensitive to disturbance and some research suggests behavioral responses to drones are species-specific, there is a need for focal species-specific disturbance assessments. We evaluated behavioral responses of nesting Common Eiders (Somateria mollissima (Linnaeus, 1758), n = 19 incubating females) to first, second, or in a few cases third exposure of fixed-wing drone surveys using nest cameras. We found no effect of drone flights (F[1,23] = 0, P = 1.0) or previous exposures (F[1,23] = 0.75, P = 0.397) on the probability of a daily recess event (bird leaves nests). Drone flights did not impact recess length (F[1,25] = 1.34, P = 0.26); however, Common Eiders with prior drone exposure took longer recess events (F[1,25] = 5.27, P = 0.03). We did not observe any overhead vigilance behaviors common in other species while the drone was in the air, which may reflect Common Eiders’ anti-predator strategies of reducing activity at nests in response to aerial predators. Surveying nesting Common Eider colonies with a fixed-wing drone did not result in biologically meaningful behavioral changes, providing a potential tool for research and monitoring this polar nesting species.

In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved.

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value (σ ¯) of −17.62 dBsm for scenario A, and a mean RCS value (σ ¯) of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system.

In this paper we present a deep neural network modelling using Computational Fluid Dynamics (CFD) simulations data in order to optimize control of bioinspired morphing wings of a drone. Drones flight needs to consider variation in aerodynamic conditions that cannot all be optimized using a fixed aerodynamic profile. Nature solves this issue as birds are changing continuously the shape of their wings depending of the aerodynamic current requirements. One important issue for fixed wing drone is the landing as it is unable to control and most of the time consequences are some damages at the nose. An optimized shape of the wing at landing will avoid this situation. Another issue is that wings with a maximum surface are sensitive to stronger head winds; while wings with a small surface allowing the drone to fly faster. A wing with a morphing surface could adapt its aerial surface to optimize aerodynamic performance to specific flight situations. A morphing wing needs to be controlled in an optimized manner taking into account current aerodynamics parameters. Predicting optimized positions of the wing needs to consider (CFD) prior simulation parameters. The scenarios for flight require an important number of CFD simulation to address different conditions and geometric shapes. We compare in this paper neural network architecture suitable to predict wing shape according to current conditions. Deep neural network (DNN) is trained using data resulted out of CFD simulations to estimate flight conditions.