The usage areas of rotary or fixed wing unmanned aerial vehicles (UAV) have become very widespread with technological developments. For this reason, UAV designs differ in terms of aerodynamic design, flight performance and endurance depending on the intended use. In this study, maximising of the autonomous flight performance of the unmanned helicopter produced at Erciyes University using an optimisation algorithm is discussed. For this purpose, the input parameters of the dynamic model are chosen as blade length, blade mass density, blade chord width and blade twist angle of the unmanned helicopter and the proportional, integral, derivative gain coefficients of the lateral axis of the autopilot. The output parameters of the dynamic model are selected as settling time, rise time and maximum overshoot, which are autonomous performance parameters. The dynamic model consisting of helicopter and autopilot parameters is integrated into the back-tracking search optimisation (BSO) algorithm as an objective function. In the optimization process, where mean squared error (MSE) is used as the performance criterion, optimum input and output values were determined. Thus, helicopter and autopilot parameters, which are among the factors affecting autonomous performance, are taken into account with equal importance and simultaneously. Simulations show that the obtained values are satisfactory. With this approach based on the optimisation method, complex and time-consuming dynamic model calculations are reduced, time and cost are saved, and practicality is achieved in applications. Therefore, this approach can be an innovative and alternative method to improve UAV designs and increase flight performance compared to classical methods.

Unmanned aerial vehicles (UAVs), which are available in our lives in many areas today, bring with them new expectations and needs along with developing technology. In order to meet these expectations and needs, main subjects such as reducing energy consumption, increasing thrust and endurance, must be taken into account in UAV designs. In this study, Backtracking search optimisation (BSO) algorithm-based adaptive neuro-fuzzy inference system (ANFIS) model is proposed for the first time to improve UAV thrust. For this purpose, first, different batteries and propellers were tested on the thrust measuring device and a data set was obtained. Propeller diameter and pitch, current, voltage and the electronic speed controller (ESC) signal were selected as input, and UAV thrust was selected as output. ANFIS was used to relate input and output parameters that do not have a direct relationship between them. In order to determine the ANFIS parameters at the optimum value, ANFIS was trained with the obtained data set by using BSO algorithm. Then, the objective function based on the optimum ANFIS structure was integrated into BSO algorithm, and the input values that gave the optimum thrust were calculated using BSO algorithm. Simulation results, in which parameters such as engine, battery and propeller affecting the thrust are taken into account equally, emphasise that the proposed method can be used effectively in improving the UAV thrust. This hybrid method, consisting of ANFIS and BSO algorithm, can reduce the cost and time loss in UAV designs and allows many possibilities to be tested.

To study the performance of the main windshield of a commercial aircraft that has been verified to be airworthy by bird-strike tests against unmanned aerial vehicle (UAV) impact at high-speed, a typical light UAV with various possible flight postures and the main windshield of a commercial aircraft are considered. The transient impact responses at critical moments, energy change and contact force of a multi-layer windshield impacted by a UAV with different postures are investigated using a simulation method based on the models verified by the high-speed impact test between the whole UAV and the full-size nose. This study shows that the flight posture of the UAV has a significant effect on the damage to the windshield. When the abdomen of a typical light UAV maintains a posture parallel to the plane of the windshield, the high-speed impact would cause catastrophic damage to the windshield and no longer be airworthy. Simultaneously, the damage to the aircraft windshield caused by UAV collision is far more serious than that caused by bird strikes under similar collision conditions. The mass-concentrated components of the UAV and their high-hardness characteristics are the main factors of affecting multi-layer glass of windshield damage. The degree of damage to the windshield is positively related to the absorbed energy rather than the impact contact force. In this study, the impact simulation results between the windshield and UAV with different flight postures are verified qualitatively by testing, which provides a rational understanding and technical pre-research support for emerging and increasingly frequent potential safety hazards in air transport practice.

Although conflict archaeology is now well established, the archaeological remains of many specific military confrontations are still to be explored. This article reports the results of fieldwork to document the site of the Battle of the Bulge (16 December 1944–25 January 1945). The authors use drone-mounted 1m-resolution LiDAR and very high-resolution simultaneous localisation and mapping (SLAM) methods to reveal more than 940 features within the forested Ardennes landscape, many of which were subsequently visited and confirmed. As well as highlighting the potential of the LiDAR-SLAM method, deployed here (both in this geographic region and in conflict archaeology) for the first time, the survey results emphasise the need for a debate on managing the heritage of a key modern conflict landscape in Europe.

Tight budgets often limit the scope of test campaigns within the development programmes of small uncrewed air vehicles (UAVs). This paper explores a range of combinations of instrumentation suites and protocols for both wind tunnel and flight evaluation, focusing on the key aspect of drawing up the drag curve of the airframe. Through extensive testing of a 5kg maximum take-off mass, fixed wing, twin motor, richly instrumented test platform, we show that automated glides over a range of airspeeds and the slow down manoeuvre are effective ways of determining power-off drag, while estimating thrust from propeller speed, and voltage and current sensing based methods work well for the power-on case. We also seek the most time-efficient and robust mix of the above manoeuvres to yield a given drag curve accuracy level and we find wind condition impacts the manoeuvre makeup of the optimal strategy.

This paper presents backstepping control and backstepping constraint control approaches for a quadrotor unmanned aerial vehicle (UAV) control system. The proposed methods are applied to a Parrot Mambo drone model to control rotational motion along the $x$, $y$, and $z$ axes during hovering and trajectory tracking. In the backstepping control approach, each state of the system controls the previous state and is called “virtual control.” The last state is controlled by the real control input. The idea is to compute, in several steps, a control law that ensures the asymptotic stability of the system. The backstepping constraint control method, based on barrier Lyapunov functions (BLFs), is designed not only to track the desired trajectory but also to guarantee no violation of the position and angle constraints. Symmetric BLFs are introduced in the design of the controller. A nonlinear mathematical model is considered in this study. Based on Lyapunov stability theory, it can be concluded that the proposed controllers can guarantee the stability of the UAV system and the state converges asymptotically to the desired trajectory. To make the control robust, an adaptation law is applied to the backstepping control that estimates the unknown parameters and ensures their convergence to their respective values. Validation of the proposed controllers was performed by simulation on a flying UAV system.

A distributed UAV (unmanned aerial vehicle) flocking control method based on vision geometry is proposed, in which only monocular RGB (red, green, blue) images are used to estimate the relative positions and velocities between drones. It does not rely on special visual markers and external infrastructure, nor does it require inter-UAV communication or prior knowledge of UAV size. This method combines the advantages of deep learning and classical geometry. It adopts a deep optical flow network to estimate dense matching points between two consecutive images, uses segmentation technology to classify these matching points into background and specific UAV, and then maps the classified matching points to Euclidean space based on the depth map information. In 3D matching points, also known as 3D feature point pairs, each of their classifications is used to estimate the rotation matrix, translation vector, velocity of the corresponding UAV, as well as the relative position between drones, based on RANSAC and least squares method. On this basis, a flocking control model is constructed. Experimental results in the Microsoft Airsim simulation environment show that in all evaluation metrics, our method achieves almost the same performance as the UAV flocking algorithm based on ground truth cluster state.

A novel concept—the contact-based landing on a mobile platform—is proposed in this paper. An adaptive backstepping controller is designed to deal with the unknown disturbances in the interactive process, and the contact-based landing mission is implemented under the hybrid force/motion control framework. A rotorcraft aerial vehicle system and a ground mobile platform are designed to conduct flight experiments, evaluating the feasibility of the proposed landing scheme and control strategy. To the best of our knowledge, this is the first time a rotorcraft unmanned aerial vehicle has been implemented to conduct a contact-based landing. To improve system autonomy in future applications, vision-based recognition and localization methods are studied, contributing to the detection of a partially occluded cooperative object or at a close range. The proposed recognition algorithms are tested on a ground platform and evaluated in several simulated scenarios, indicating the algorithm’s effectiveness.

During the past two decades, with the advancement of avionics, control systems, design, product methods, and communication systems, UAVs have gained more interest from both civil and military customers. Hence, more researchers have dedicated their time to developing new applications for drones and improving their performance. In this study, a literature review on drone applications is conducted. It is explained that they can be more effective and beneficial when merged with preexisting systems, and thus making a system of systems. Further, we have classified UAV applications more comprehensively. This classification can be very useful for providing insights when designing new UAVs. Finally, previous works are discussed thoroughly and diagrams are developed to show the research focus in this field. These statistical diagrams can also be used as a gap analysis tool.

The Vulnerable marsh deer Blastocerus dichotomus, the largest native cervid in South America, is declining throughout its range as a result of the conversion of wetlands and overhunting. Estimated densities in open wetlands of several types are 0.1–6.8 individuals per km2. We undertook the first unmanned aerial vehicle (UAV) survey of the marsh deer to estimate the density of this species in a 113.6 km2 area under forestry management in the lower delta of the Paraná River, Argentina. During 6–8 August 2019, at a time of year when canopy cover is minimal, we surveyed marsh deer using Phantom 4 Pro UAVs along 94 transects totalling 127.8 km and 8.6 km2 (8.1% of the study area). The 5,506 photographs obtained were manually checked by us and by a group of 39 trained volunteers, following a standardized protocol. We detected a total of 58 marsh deer, giving an estimated density of 6.90 individuals per km2 (95% CI 5.26–8.54), which extrapolates to 559–908 individuals in our 113.6 km2 study area. As it has generally been assumed that marsh deer prefer open habitats, this relatively high estimate of density within a forestry plantation matrix is unexpected. We discuss the advantages of using UAVs to survey marsh deer and other related ungulates.

Reinforcement learning has previously been applied to the problem of controlling a perched landing manoeuvre for a custom sweep-wing aircraft. Previous work showed that the use of domain randomisation to train with atmospheric disturbances improved the real-world performance of the controllers, leading to increased reward. This paper builds on the previous project, investigating enhancements and modifications to the learning process to further improve performance, and reduce final state error. These changes include modifying the observation by adding information about the airspeed to the standard aircraft state vector, employing further domain randomisation of the simulator, optimising the underlying RL algorithm and network structure, and changing to a continuous action space. Simulated investigations identified hyperparameter optimisation as achieving the most significant increase in reward performance. Several test cases were explored to identify the best combination of enhancements. Flight testing was performed, comparing a baseline model against some of the best performing test cases from simulation. Generally, test cases that performed better than the baseline in simulation also performed better in the real world. However, flight tests also identified limitations with the current numerical model. For some models, the chosen policy performs well in simulation yet stalls prematurely in reality, a problem known as the reality gap.

The recent development of new offshore projects in pre-salt deepwater fields has placed offshore loading operations as the main production outflow alternative, increasing the operational complexity and risks. Numerous dangerous situations are associated with oil offloading, such as the messenger line transfer during the mooring stage. Nowadays, this critical task is realized by launching a thin messenger cable using the pneumatic line throwing apparatus. This is a complex and slow process since the operation usually occurs with the ship opposite to the wind. This work proposes a hybrid flight methodology based on computer vision and sensor fusion techniques for autonomous unmanned aerial vehicles (UAVs). The UAV takes off from an oil rig and precisely reaches a specific point in the shuttle tanker without using expensive positioning devices and augmenting UAV’s orientation (yaw) precision since the compass can suffer from severe interference due to naval metallic structures near the vehicle. The proposed framework was tested in a realistic simulated environment considering several practical operational constraints. The results demonstrated both the robustness and efficiency of the methodology.

While the opioid epidemic engulfing the United States and the globe is well-documented, the potential use of powerful fentanyl derivatives as a weapon of terror is increasingly a concern. Carfentanyl, a powerful and deadly fentanyl derivative, is seeing a surge in popularity as an illegal street drug, and there is increasing congressional interest surrounding the classification of opioid derivatives under the Chemical Weapons Convention (CWC) given their potential to cause harm. The combination of the potency of opioid derivatives along with the ease of accessibility poses a potential risk of the use of these deadly agents as chemical weapons, particularly by terrorist organizations. Disaster Medicine specialists in recent years have established a sub-specialty in Counter-Terrorism Medicine (CTM) to address and research the unique terrorism-related issues relating to mitigation, preparedness, and response measures to asymmetric, multi-modality terrorist attacks.

The design and performance quantification of four Vertical Take-Off and Landing (VTOL) architectures for a canard-type aircraft configuration are presented. The aero-structural sizing of the canard configuration and the sizing procedure for the proposed VTOL configurations are described and discussed. The proposed VTOL architectures are based on a range of rotor distances to the centre of gravity, quad- and tri-rotor configurations, retractable front rotors and tilt rear rotors. The aerodynamic performance, total installed power and VTOL system mass were modelled and experimentally validated. The results show that a fully exposed VTOL system penalises the Lift-over-Drag (L/D) ratio significantly relative to a clean configuration. The VTOL system mass can be reduced by up to 32% by using a tilt tri-rotor configuration when compared with an equidistant quad-rotor+pusher configuration. The fraction of installed power usable for forward flight can be increased by up to 80% with a tilt configuration. For the proposed mission, the range can be significantly increased if a tri-rotor tilt configuration is adopted in place of an equidistant quad-rotor+pusher configuration.

Backstepping represents a promising control law for fixed-wing Unmanned Aerial Vehicles (UAVs). Its non-linearity and its adaptation capabilities guarantee adequate control performance over the whole flight envelope, even when the aircraft model is affected by parametric uncertainties. In the literature, several works apply backstepping controllers to various aspects of fixed-wing UAV flight. Unfortunately, many of them have not been implemented in a real-time controller, and only few attempt simultaneous longitudinal and lateral–directional aircraft control. In this paper, an existing backstepping approach able to control longitudinal and lateral–directional motions is adapted for the definition of a control strategy suitable for small UAV autopilots. Rapidly changing inner-loop variables are controlled with non-adaptive backstepping, while slower outer loop navigation variables are Proportional–Integral–Derivative (PID) controlled. The controller is evaluated through numerical simulations for two very diverse fixed-wing aircraft performing complex manoeuvres. The controller behaviour with model parametric uncertainties or in presence of noise is also tested. The performance results of a real-time implementation on a microcontroller are evaluated through hardware-in-the-loop simulation.

This study deploys RTK-GNSS in 2012, TLS in 2015 and UAV in 2018 to monitor the changes of Urumqi Glacier No. 1 (UG1), eastern Tien Shan, and analyzes the feasibility of three technologies in monitoring the mountain glaciers. DEM differencing shows that UG1 has experienced a pronounced thinning and mass loss for the period of 2012–18. The glacier surface elevation change of −0.83 ± 0.57 m w.e. a−1 has been recorded for 2012–15, whereas the changes of glacier tongue surface elevation in 2015–18 and 2012–18 were −2.03 ± 0.95 and −1.34 ± 0.88 m w.e. a−1, respectively. The glacier area shrunk by 0.07 ± 0.07 × 10−3 km2 and the terminus retreat rate was 6.28 ± 0.83 m a−1 during 2012–18. The good agreement between the glaciological and geodetic specific mass-balances is promising, showing the combination of the three technologies is suitable to monitor glacier mass change. We recommend application of the three technologies to assess each other in different locations of the glacier, e.g. RTK-GNSS base stations, ground control points, glacier tongue and terminus, in order to avoid the inherent limitations of each technology and to provide reliable data for the future studies of mountain glacier changes in western China.

The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled late-season weed escapes. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using unmanned aerial vehicle–based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes before crop harvest: common waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately before ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 to 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assisting with management decision making.