Design, Simulation and New Applications of Unmanned Aerial Vehicles

Over the past few years, there has been an increasing fascination with electric unmanned aerial vehicles (UAVs) because of their capacity to undertake demanding and perilous missions while also delivering advantages in terms of flexibility, safety, and expenses. These UAVs are revolutionizing various public services, encompassing real-time surveillance, search and rescue operations, wildlife assessments, delivery services, wireless connectivity, and precise farming. To enhance their efficiency and duration, UAVs typically employ a hybrid power system. This system integrates diverse energy sources, such as fuel cells, batteries, solar cells, and supercapacitors. The selection of an appropriate hybrid power arrangement and the implementation of an effective energy management system are crucial for the successful functioning of advanced UAVs. This article specifically concentrates on UAV platforms powered by batteries, incorporating innovative technologies, like in-flight recharging via laser beams and tethering. It provides an all-encompassing and evaluative examination of the current cutting-edge power supply configurations, with the objective of identifying deficiencies, presenting perspectives, and offering recommendations for future consideration in this domain. Full article

In this research, the design of a robust curved-line path-following control system for fixed-wing unmanned aerial vehicles (FWUAVs) affected by uncertainties on the latitude plane is studied. This is undertaken to enhance closed-loop system robustness under unknown uncertainties and derive the control surface deflection angle directly used to control FWUAVs, which has rarely been studied in previous works. The system is formed through the mass center position control (MCPC) and yaw angle control (YAC) subsystems. In the MCPC, the desired yaw angle, which is treated as the reference signal for the YAC subsystem, is calculated analytically using path-following errors, current flow angles, and the yaw angle. In the YAC, a disturbance estimator is designed to estimate uncertainties such as nonlinearities, couplings, time variations, model parameter perturbations, and unmodeled dynamics. Predictive functional controllers are designed to target nominal systems in the absence of uncertainties, such that the estimations of the uncertainties can be incorporated through feedback for closed-loop system robustness enhancement. The simulation results show that higher path-following precision and stronger robustness for the FWUAVs based on the proposed approach can be achieved using only rough model parameters compared with the conventional nonlinear dynamic inversion, which requires detailed model information. Full article

The collaborative deployment of multiple UAVs is a crucial issue in UAV-supported disaster emergency communication networks, as utilizing these UAVs as air base stations can greatly assist in restoring communication networks within disaster-stricken areas. In this paper, the problem of rapid deployment of randomly distributed UAVs in disaster scenarios is studied, and a distributed rapid deployment method for UAVs´ emergency communication network is proposed; this method can cover all target deployment points while maintaining connectivity and provide maximum area coverage for the emergency communication network. To reduce the deployment complexity, we decoupled the three-dimensional UAV deployment problem into two dimensions: vertical and horizontal. For this small-area deployment scenario, a small area UAVs deployment improved-Broyden–Fletcher–Goldfarb–Shanno (SAIBFGS) algorithm is proposed via improving the Iterative step size and search direction to solve the high computational complexity of the traditional Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In a large area deployment scenario, aiming at the problem of the premature convergence of the standard genetic algorithm (SGA), the large-area UAVs deployment elitist strategy genetic algorithm (LAESGA) is proposed through the improvement of selection, crossover, and mutation operations. The adaptation function of connectivity and coverage is solved by using SAIBFGS and LAESGA, respectively, in the horizontal dimension to obtain the optimal UAV two-dimensional deployment coordinates. Then, the transmitting power and height of the UAV base station are dynamically adjusted according to the channel characteristics and the discrete coefficients of the ground users to be rescued in different environments, which effectively improves the power consumption efficiency of the UAV base station and increases the usage time of the UAV base station, realizing the energy-saving deployment of the UAV base station. Finally, the effectiveness of the proposed method is verified via data transmission rate simulation results in different environments. Full article

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude. Full article

Carrier-based unmanned aerial vehicles (UAVs) require precise evaluation methods for their landing and arresting safety due to their high autonomy and demanding reliability requirements. In this paper, an efficient and accurate simulation method is presented for studying the arresting hook engaging arresting cable process. The finite element method and multibody dynamics (FEM-MBD) approach is employed. By establishing a rigid–flexible coupling model encompassing the UAV and arresting gear system, the simulation model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components. The model considers the material properties, arresting gear system characteristics, and UAV state during engagement. Verification is conducted by comparing simulation results with experimental data from a referenced arresting hook rebound. Finally, simulations are performed under different touchdown points and roll angles of the UAV to analyze the stress distribution of the hook, center of gravity variations, and the tire touch and rollover cable response. The proposed rigid–flexible coupling arresting dynamics model in this paper enables the effective analysis of the dynamic behavior during the arresting hook engaging arresting cable process. Full article

An approach to the implementation of a neural network for real-time cryptographic data protection with symmetric keys oriented on embedded systems is presented. This approach is valuable, especially for onboard communication systems in unmanned aerial vehicles (UAV), because of its suitability for hardware implementation. In this study, we evaluate the possibility of building such a system in hardware implementation at FPGA. Onboard implementation-oriented information technology of real-time neuro-like cryptographic data protection with symmetric keys (masking codes, neural network architecture, and matrix of weighting coefficients) has been developed. Due to the pre-calculation of matrices of weighting coefficients and tables of macro-partial products and the use of tabular-algorithmic implementation of neuro-like elements and dynamic change of keys, it provides increased cryptographic stability and hardware–software implementation on FPGA. The table-algorithmic method of calculating the scalar product has been improved. By bringing the weighting coefficients to the greatest common order, pre-computing the tables of macro-partial products, and using operations of memory read, fixed-point addition, and shift operations instead of floating-point multiplication and addition operations, it provides a reduction in hardware costs for its implementation and calculation time as well. Using a processor core supplemented with specialized hardware modules for calculating the scalar product, a system of neural network cryptographic data protection in real-time has been developed, which, due to the combination of universal and specialized approaches, software, and hardware, ensures the effective implementation of neuro-like algorithms for cryptographic encryption and decryption of data in real-time. The specialized hardware for neural network cryptographic data encryption was developed using VHDL for equipment programming in the Quartus II development environment ver. 13.1 and the appropriate libraries and implemented on the basis of the FPGA EP3C16F484C6 Cyclone III family, and it requires 3053 logic elements and 745 registers. The execution time of exclusively software realization of NN cryptographic data encryption procedure using a NanoPi Duo microcomputer based on the Allwinner Cortex-A7 H2+ SoC was about 20 ms. The hardware–software implementation of the encryption, taking into account the pre-calculations and settings, requires about 1 msec, including hardware encryption on the FPGA of four 2-bit inputs, which is performed in 160 nanoseconds. Full article

In this paper, the characterization of 3D-printed materials that are considered in the design of multirotor unmanned aerial vehicles (UAVs) for specialized purposes was carried out. The multirotor UAV system is briefly described, primarily from the aspect of system dynamics, considering that the airframe parts connect the UAV components, including the propulsion configuration, into a functional assembly. Three additive manufacturing (AM) technologies were discussed, and a brief overview was provided of selective laser sintering (SLS), fused deposition modeling (FDM), and continuous fiber fabrication (CFF). Using hardware and related software, 12 series of specimens were produced, which were experimentally tested utilizing a quasi-static uniaxial tensile test. The results of the experimental tests are provided graphically with stress–strain diagrams. In this work, the focus is on CFF technology and the testing of materials that will be used in the production of mechanically loaded airframe parts of multirotor UAVs. The experimentally obtained values of the maximum stresses were compared for different technologies. For the considered specimens manufactured using FDM and SLS technology, the values are up to 40 MPa, while for the considered CFF materials and range of investigated specimens, it is shown that it can be at least four times higher. By increasing the proportion of fibers, these differences increase. To be able to provide a wider comparison of CFF technology and investigated materials with aluminum alloys, the following three-point flexural and Charpy impact tests were selected that fit within this framework for experimental characterization. Full article

Compared to detection methods employed by Mars rovers and orbiters, the employment of Mars UAVs presents clear advantages. However, the unique atmospheric conditions on Mars pose significant challenges to the design and operation of such UAVs. One of the primary difficulties lies in the impact of the planet’s low air density on the aerodynamic performance of the UAV’s rotor system. In order to determine the aerodynamic characteristics of the rotor system in the Martian atmospheric environment, a rotor system suitable for the Martian environment was designed under the premise of fully considering the special atmospheric environment of Mars, and the aerodynamic characteristics of the rotor system in the compressible and ultra-low Reynolds number environment were numerically simulated by means of a numerical calculation method. Additionally, a bench experiment was conducted in a vacuum chamber simulating the Martian atmospheric environment, and the aerodynamic characteristics of the UAV rotor system in the Martian environment were analyzed by combining theory and experiments. The feasibility of the rotor system applied to the Martian atmospheric environment was verified, and the first generation of Mars unmanned helicopters was developed and validated via hovering experiments, which thereby yielded crucial data support for the design of subsequent Mars UAV models. Full article

This article’s main topic is an assessment of unmanned aircraft system (UAS) noise pollution in several weight categories according to Regulation (EU) 2019/947 and its impact on the urban environment during regular operation. The necessity of solving the given problem is caused by an increasing occurrence of UASs in airspace and the prospect of introducing unmanned aircraft into broader commercial operations. This work aims to provide an overview of noise measurements of two UAS weight categories under natural atmospheric conditions to assess their impact on the surrounding environment. On top of that, modelling and simulations were used to observe and assess the noise emission characteristics. The quantitative results contain an assessment of the given noise restrictions based on the psychoacoustic impact and actual measured values inserted into the urban simulation scenario of the Zilina case study located in northwest Slovakia. It was preceded by a study of noise levels in certain areas to evaluate the variation level after UAS integration into the corresponding airspace. Following a model simulation of the C2 category, it was concluded that there was a marginal rise in the level of noise exposure, which would not exceed the prescribed standards of the Environmental Noise Directive. Full article

With the development of pesticide substitution technology, ozonated water has been gradually applied in agricultural plant protection. This paper describes our development of an ecological plant protection unmanned aerial vehicle (UAV) that can produce and spray ozonated water while flying. Firstly, this paper carries out the design of the ozonated water system, including the selection of the ozone generator and the gas-liquid mixing method. Secondly, the conceptual design method of the ecological plant protection UAV is introduced, including total weight estimation, propulsion system selection, layout and structure design, battery modeling, center of gravity evaluation, and control system. Then, static analysis was computed in ANSYS Workbench on the UAV fuselage. Finally, the field test verified that the hovering time of the UAV could reach the design requirement of 10 min when it was fully loaded. The effective spraying width (with a height of 2 m and a speed of 3 m/s) is 5.25 m. The UAV was used to spray ozonated water with a concentration of 17 ppm continuously once a day; on day 7, the control effect could reach 76.4% and the reduction rate of the larvae population was 59.3%. Therefore, spraying ozonated water with a concentration of 17 ppm every day by using the ecological plant protection UAV can effectively control broccoli diamondback moth larvae and achieve the control effect of traditional pesticides (Chlorantraniliprole SC). Full article

Desert locust is one of the most destructive migratory pest in the world. Current methods of control rely on conventional chemical insecticides during invasion. Some environmentally friendly biopesticides based on Metarhizium acridum and insect growth regulators have also been deployed in preventive control operations. They have been tested in sprayers mounted on commonly used platforms such as vehicles, aircraft, and human. However, despite being used successfully, these tools present many challenges, hence the need to supplement them with suitable alternatives. The successful use of drones to control pests such as fall armyworm, planthoppers, aphids, among others, makes it an attractive technology that has the potential to improve locust management, especially in inaccessible areas. However, key parameters for the safe and optimal use of drones in desert locust control are not documented. This study established the key parameters for spraying desert locusts with a drone. To test the optimum height for spraying Metarhizium acridum on the locusts, the drone was flown at five different heights: 2.5, 5, 7.5, 10, and 12.5 m. At each height, the drone sprayed the ink mixture on spray cards pinned to the ground to approximate the droplet density and compare it to the standard droplet density recommended for desert locust control. To assess the efficacy of M. acridum and the effectiveness of drones in its application, 50 g of spores were mixed in 1 L of diesel and sprayed on caged live locusts of different stages (3rd and 4th instars, as well as the adults); they were monitored for twenty-one days in a controlled room, and their mortality was determined. Variation in droplet density between the tested heights was significant. A height of 10 m agrees with the recommended standard droplet density within the 45 droplets/cm² range. Mortality varied among the locusts’ developmental stages within and between heights. Survival probability varied between heights for 3rd instar, 4th instar, and adults. All the developmental stages of the desert locust were susceptible to Novacrid and the recommended target stage is the 3rd instar. Management of desert locusts by the use of drone technology appears promising when the pesticides are applied at an optimum height and standard operating procedures are followed. Further research could explore the gap in the effects of environmental parameters on flight application efficiency. Full article

This paper proposes a new method that fuses acoustic measurements in the reverberation field and low-accuracy inertial measurement unit (IMU) motion reports for simultaneous localization and mapping (SLAM). Different from existing studies that only use acoustic data for direction-of-arrival (DoA) estimates, the source’s distance from sensors is calculated with the direct-to-reverberant energy ratio (DRR) and applied to eliminate the nonlinear noise from motion reports. A particle filter is applied to estimate the critical distance, which is key for associating the source’s distance with the DRR. A keyframe method is used to eliminate the deviation of the source position estimation toward the robot. The proposed DoA-DRR acoustic SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for drones. The method is the first acoustic SLAM algorithm that has been validated on a real-world drone dataset that contains only acoustic data and IMU measurements. Compared with previous methods, D-D SLAM has acceptable performance in locating the drone and building a source map from a real-world drone dataset. The average location accuracy is 0.48 m, while the source position error converges to less than 0.25 m within 2.8 s. These results prove the effectiveness of D-D SLAM in real-world scenes. Full article

Unmanned Aerial Vehicles (UAVs) are able to provide instantaneous visual cues and a high-level data throughput that could be further leveraged to address complex tasks, such as semantically rich scene understanding. In this work, we built on the use of Large Language Models (LLMs) and Visual Language Models (VLMs), together with a state-of-the-art detection pipeline, to provide thorough zero-shot UAV scene literary text descriptions. The generated texts achieve a GUNNING Fog median grade level in the range of 7–12. Applications of this framework could be found in the filming industry and could enhance user experience in theme parks or in the advertisement sector. We demonstrate a low-cost highly efficient state-of-the-art practical implementation of microdrones in a well-controlled and challenging setting, in addition to proposing the use of standardized readability metrics to assess LLM-enhanced descriptions. Full article

Inventive approaches are constantly being revealed in the field of vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) configuration concepts and designs. To date, a body-associated configuration of UAVs for augmented lift remains unclear among other approached designs. The current paper investigates the mechanism of a high-lift ducted fan mounted in the central body for VTOL UAV designs. We report an unresolved design of a disc-shaped UAV with a single rotor that aims to enhance the cost-effectiveness of fuel consumption with a substantial contribution of body lift to hover thrust. The convex upper surface curvature was applied to generate a significant lift contribution from the body during hover. The computational fluid dynamics (CFD) approach based on unstructured discretization followed by three-dimensional steady Reynolds-averaged Navier–Stokes (RANS) flow was applied in ANSYS CFX to mechanistically investigate the underlying design considerations. The disc-shaped UAV uses the lip curvature on the duct inlet to generate a vertical force that demonstrates a significant contribution of 95% of the rotor thrust during hovering. The UAV’s upper surface generates prolonged flow entrainment free from momentum losses in swirling flows. This phenomenon is followed by reduced power consumption in hovering and vertical flight, making the UAV aerodynamically stable and environmentally safe. Full article

The problem of aircraft entering and exiting water is a complex, nonlinear, strongly disturbed, and multi-coupled multiphase flow problem, which involves the precise capture of the air/water interface and the multi-coupling interaction between aircraft, water, and air. Moreover, due to the large difference in medium properties during the crossing, the load on the body will suddenly change. In this paper, the VOF (volume of fluid) algorithm is used to capture the liquid surface at the air/water interface, and since body movement is involved in this process, the overset grid technology is used to avoid the traditional dynamic grid deformation problem. In the process of this numerical simulation prediction, the effects of different water-entry angles and different water-entry heights on the body load and attitude of the trans-medium aircraft, as well as the cavitation evolution law of the body water entry are analyzed. On this basis, to simulate the authenticity and complexity of the water-entry environment, numerical wave-making technology was introduced to analyze the water-entry load, posture, and cavitation evolution law of the body under different wave environments. The numerical parameters under the condition of wave and no wave are compared, and the difference in water-entry performance under the condition of wave and no wave is analyzed. Full article

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better. Full article

Strapdown celestial imaging sensors provide a compact, lightweight alternative to their gimbaled counterparts. Strapdown imaging systems typically require a wider field of view, and consequently longer exposure intervals, leading to significant motion blur. The motion blur for a constellation of stars results in a constellation of trails on the image plane. We present a method that extracts the path of these star trails, and uses a linearized weighted least squares approach to correct noisy inertial attitude measurements. We demonstrate the validity of this method through its application to synthetically generated images, and subsequently observe its relative performance by using real images. The findings of this study indicate that the motion blur present in strapdown celestial imagery yields an a posteriori mean absolute attitude error of less than 0.13 degrees in the yaw axis, and 0.06 degrees in the pitch and roll axes (3 $\sigma$) for a calibrated wide-angle camera lens. These findings demonstrate the viability of low-cost, wide-angle, strapdown celestial attitude sensors on lightweight UAV hardware. Full article

The energy autonomy of UAVs is an important direction in the field of aerospace. Long-endurance aerial vehicles allow for continuous flight; however, to meet the guidelines, the power supply system has to be able to harvest energy from outside. Solar cells allow the production of electricity during the day when the sun shines on their surface. Depending on the location, time, weather, and other external factors, the energy produced by PV panels will change. In order to calculate as accurately as possible the energy obtained by solar cells, we developed a simulation model that took into account all of the external restrictions and the UAV’s limits during flight. The conducted analysis made it possible to obtain information for the specific input data on whether the UAV is able to fly for 24 h in a specific flight scenario. The UAV powered by solar cells developed by us and the performed aviation missions have shown that the UAV is capable of continuous flight without the need to land. Full article

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability. Full article

Changes in the global climate have induced densified rainfall and caused natural hazards across the world in recent years. Formed by a central mountain range and a corridor of alluvial plains to the west, Taiwan is at risk of flood hazards owing to its low-lying lands as well as the distinct seasonality of rainfall patterns. The rapid discharge of surface runoff and a growing number of impervious surfaces have also increased flood hazards during recent typhoon landfalls. A century ago, ancestors in Taoyuan City constructed a system of water channels composed of thousands of ponds to fulfill the needs of agriculture and aquaculture. During the expansion of urban areas, land reformation replaced a majority of earlier ponds with residential and industrial zones. However, the remaining ponds could potentially serve as on-site water detention facilities under the increasing risk of floods. In this research, we first renewed an outdated pond database by deploying a novel unmanned aerial vehicle (UAV) system with a micro-sonar to map the bathymetry of 80 ponds. Next, a simplified inundation model (SPM) was used to simulate the flood extent caused by different scenarios of rainfall in Bade District of Taoyuan City. Assuming that extremely that heavy rainfalls at 25, 50, 75, and 100 mm occurred in a very short period, the flood area would decrease by 96%, 75%, 52%, and 37%, respectively, when the ponds were preparatorily emptied. Full article

In this paper, a novel geometrical localization scheme based on the Received Signal Strength Indicator (RSSI) is developed for a group of unmanned aerial vehicles (UAVs). Since RSSI-based localization does not require complicated hardware, it is the correct choice for RF target localization. In this promising work, unlike the other techniques given in the literature, transmit power or path loss exponent information is not needed. The procedure depends on the received power difference of each receiver in UAVs. In the developed scheme, four UAVs forming two groups fly in perpendicular planes. Each UAV in the group moves in a circle, keeping its distance from the plane’s center until it gets equal power with the other members of its group. Using this movement rate, lines passing through the source position are calculated. The intersection of these lines gives the position of the RF target. However, in a noisy environment, the lines do not intersect at one point. Therefore, the algorithm given in the manuscript finds a point that has a minimum distance to all lines and is also developed. Simulation results are provided at the end of the manuscript to verify our theoretical claims. Full article

Copter-type UAVs (unmanned aerial vehicles) or drones are expected to become more and more popular for deliveries of small goods in urban areas. One strategy to reduce the risks of drone collisions is to constrain their movements to a drone road system as far as possible. In this paper, for reasons of scalability, we assume that path-planning decisions for drones are not made centrally but rather autonomously by each individual drone, based solely on position/speed/heading information received from other drones through WiFi-based communications. We present a system model for moving drones along a straight road segment or tube, in which the tube is partitioned into lanes. We furthermore present a cost-based algorithm by which drones make lane-switching decisions, and evaluate the performance of differently parameterized versions of this algorithm, highlighting some of the involved tradeoffs. Our algorithm and results can serve as a baseline for more advanced algorithms, for example, including more elaborate sensors. Full article

Achieving energy autonomy in a UAV (unmanned aerial vehicle) is an important direction for aerospace research. Long endurance flights allow for continuous observations, taking of measurements and control of selected parameters. To provide continuous flight, a UAV must be able to harvest energy externally. The most popular method to achieve this is the use of solar cells on the wings and structure of the UAV. Flexible solar cells mounted on the surface of the wings can be damaged and contaminated. To prevent these negative changes, it is necessary to apply a protective coating to the solar cells. One of the more promising methods is lamination. To properly carry out this process, some parameters have to be appropriately adjusted. The appropriate selection of temperature and feed speed in the laminator allows a PV (photovoltaic) panel to be coated with film, minimizing any defects in the structure. Covering PV panels with film reduces the performance of the solar cells. By measuring the current–voltage characteristics, data were obtained showing the change in the performance of solar cells before and after lamination. In the case of testing flexible PV panels, the efficiency decreased from 24.29 to 23.33%. This informed the selection of the appropriate number of solar cells for the UAV, considering the losses caused by the lamination process. Full article

A vertical take-off and landing plane (VTOL plane) is a fixed-wing unmanned aerial vehicle (FWUAV) configuration with the ability to take off and land vertically. It combines the benefits of fixed-wing and multirotor configurations, which gives it a high cruising range and independence from a runway. This configuration requires arms as mountings for the VTOL’s motors. This study discusses the design of a VTOL Plane with various VTOL arm configurations, and a computational fluid dynamics (CFD) simulation was conducted to find out which configuration performs the best aerodynamically. The VTOL arm configurations analyzed were a quad-plane, a twin-tail boom, a tandem wing, and a transverse arm. The interpreted performances were the lift and drag performances, stall conditions, flight efficiency, stability, and maneuverability. The relative wind directions toward the longitudinal axis of the UAV, which are the sideslip angle and the angle of attack, were varied to simulate various flying conditions. The results showed that the twin tail-boom is the most advantageous based on the interpreted performances. Full article

A tilt-rotor quadcopter (TRQ) equipped with four tilt-rotors is more agile than its under-actuated counterpart and can fly at any path while maintaining the desired attitude. To take advantage of this additional control capability and enhance the quadrotor system’s robustness and capability, we designed two sliding mode controls (SMCs): the typical SMC exploits the properties of the rotational dynamics, and the modified SMC avoids undesired chattering. Our simulation studies show that the proposed SMC scheme can follow the planned flight path and keep the desired attitude in the presence of variable deviations and external perturbations. We demonstrate from the Lyapunov stability theorem that the proposed control scheme can guarantee the asymptotic stability of the TRQ in terms of position and attitude following via control allocation. Full article

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model. Full article

A trans-medium aircraft is a new concept aircraft that can both dive in the water and fly in the air. In this paper, a new type of water–air multi-medium span vehicle is designed based on the water entry and exit structure model of a multi-rotor UAV. Based on the designed structural model of the cross-media aircraft, the OpenFOAM open source numerical platform is used to analyze the single-medium aerodynamic characteristics and the multi-medium spanning flow analysis. The rotating flow characteristics of single-medium air rotor and underwater propeller are calculated by sliding mesh. In order to prevent the numerical divergence caused by the deformation of the grid movement, the overset grid method and the multiphase flow technology are used for the numerical simulation of the water entry and exit of the cross-medium aircraft. Through the above analysis, the flow field characteristics of the trans-medium vehicle in different media are verified, and the changes in the body load and attitude at different water entry angles are also obtained during the process of medium crossing. Full article

In this paper, a new adaptive observer is proposed to estimate the actuator fault and disturbance of a quadrotor UAV system with actuator failure and disturbance. Based on this, a nonsingular fast terminal sliding mode controller is designed. Firstly, according to the randomness of faults and disturbances, the UAV system under faults and disturbances is regarded as one of the Markov jump nonlinear systems (MJNSs). Secondly, an adaptive observer is designed to simultaneously observe the system state, fault, and disturbance. In order to improve the precision, the fast adaptive fault estimation (FAFE) algorithm is adopted in the adaptive observer. In addition, a quasi-one-sided Lipschitz condition is used to deal with the nonlinear term, which relaxes the condition and contains more nonlinear information. Finally, a nonsingular fast terminal sliding mode controller is designed for fault-tolerant control of the system. The simulation results show that the faults and disturbances can be observed successfully, and that the system is stochastic stable. Full article

This study aims to identify the factors associated with the adoption of drone delivery in Medellín, Colombia, in the context of the COVID-19 pandemic. For that purpose, it implemented the Diffusion of Innovation (DOI) theory and the Technology Acceptance Model (TAM), which have constructs that complement each other to determine the decision to accept a given technology. A survey was administered to 121 participants in order to validate the model proposed here, which is based on variables that reflect the perceived attributes and risks of this innovation and individuals’ characteristics. The results indicate that the factors Performance Risk, Compatibility, Personal Innovativeness, and Relative Advantage of Environmental Friendliness have the greatest influence on Intention to Use Drone Delivery (mediated by Attitude Towards Drone Delivery). This paper offers relevant information for the academic community and delivery companies because few other studies have investigated this topic. Additionally, the proposed technology adoption model can be a benchmark for other emerging economies in similar social, economic, and technological conditions. Full article

The deployment performance of the unfolded wing determines whether the winged missiles can fly normally after being launched, infecting the attack performance of the winged missiles. The paper proposes a new deployment mechanism with clearance eliminator. Based on the slider-crank principle, the proposed deployment mechanism achieves fast and low-impact deployment of the wings. The proposed clearance eliminator with shape memory alloy (SMA) effectively eliminates the clearance of the sliding pair and improves the support stiffness and stability of the deployed wing. The collision characteristics and the clearance elimination are studied for the deployment mechanism. The influence of the collision force on the motion state of the wing during the deployment is analyzed. The static stiffness of the wing under the clearance state and the deformation is analyzed. The dynamic stiffness under the catapult clearance elimination state is modeled based on the fractal geometry and contact stress theory. The relationship between the locking force and the support stiffness is revealed. The kinetic simulation is used to analyze the motion response during the action of the deployment mechanism. Modal analysis, harmonic response analysis, and random vibration analysis were conducted for the whole wings. A prototype was developed to verify the ejection performance of the wing according to the input load characteristics. The dynamic stiffness of the unfolded wings is tested by the fundamental frequency experiments to verify the performance of the clearance elimination assembly. The experimental results show that the designed deployment mechanism with clearance compensation achieves fast ejection and high stiffness retention of the missile wing. Full article

Understanding the causal impacts among various parameters is essential for designing micro aerial vehicles (MAVs). The simulation of computational fluid dynamics (CFD) provides us with a technique to calculate aerodynamic forces precisely. However, even a single result regularly takes considerable computational time. Machine learning, due to the advance in computer hardware, shows another approach that can speed up the analysis process. In this study, we introduce an artificial neural network (ANN) framework to predict the transient aerodynamic forces and the corresponding energy consumption. Instead of considering the whole transient changes of each parameter as inputs, we utilised the technique of Fourier transform to simplify the ANN structure for minimising the computation cost. Furthermore, two typical activation functions, rectified linear unit (ReLU) and sigmoid, were attempted to build the network. The validity of the method was further examined by comparing it with CFD simulation. The result shows that both functions are able to provide highly accurate estimations that can be implemented for model construction under this framework. Consequently, this novel approach makes it possible to reduce the complexity of analysis, study the flapping wing aerodynamics and enable a more efficient way to optimise parameters. Full article

Simulation plays a critical role in the development of UAV navigation systems. In the context of celestial navigation, the ability to simulate celestial imagery is particularly important, due to the logistical and legal constraints of conducting UAV flight trials after dusk. We present a method for simulating night-sky star field imagery captured from a rigidly mounted ‘strapdown’ UAV camera system, with reference to a single static reference image captured on the ground. Using fast attitude updates and spherical linear interpolation, images are superimposed to produce a finite-exposure image that accurately captures motion blur due to aircraft actuation and aerodynamic turbulence. The simulation images are validated against a real data set, showing similarity in both star trail path and magnitude. The outcomes of this work provide a simulation test environment for the development of celestial navigation algorithms. Full article

The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA. Full article

The vehicular ad hoc network (VANET) is a potential technology for intelligent transportation systems (ITS) that aims to improve safety by allowing vehicles to communicate quickly and reliably. The rates of merging collision and hidden terminal problems, as well as the problems of picking the best match cluster head (CH) in a merged cluster, may emerge when two or more clusters are merged in the design of a clustering and cluster management scheme. In this paper, we propose an enhanced cluster-based multi-access channel protocol (ECMA) for high-throughput and effective access channel transmissions while minimizing access delay and preventing collisions during cluster merging. We devised an aperiodic and acceptable merge cluster head selection (MCHS) algorithm for selecting the optimal merge cluster head (MCH) in centralized clusters where all nodes are one-hop nodes during the merging window. We also applied a weighted Markov chain mathematical model to improve accuracy while lowering ECMA channel data access transmission delay during the cluster merger window. We presented extensive simulation data to demonstrate the superiority of the suggested approach over existing state-of-the-arts. The implementation of a MCHS algorithm and a weight chain Markov model reveal that ECMA is distinct and more efficient by 64.20–69.49% in terms of average network throughput, end-to-end delay, and access transmission probability. Full article

Low-altitude flight in mountainous terrains is a difficult flight task applied in both military and civilian fields. The helicopter has to maintain low altitude to realize search and rescue, reconnaissance, penetration, and strike operations. It contains complex environment perception, multilevel decision making, and multi-objective flight control; thus, flight is currently mainly conducted by human pilots. In this work, a control framework is implemented to realize autonomous flight for unmanned helicopter operations in an unknown mountainous environment. The identification of targets and threats is introduced using a deep neural network. A 3D vector field histogram method is adopted for local terrain avoidance based on airborne Lidar sensors. In particular, we propose an intuitive direct-viewing method to judge and change the visibilities of the helicopter. On this basis, a finite state machine is built for decision making of the autonomous flight. A highly realistic simulation environment is established to verify the proposed control framework. The simulation results demonstrate that the helicopter can autonomously complete flight missions including a fast approach, threat avoidance, cover concealment, and circuitous flight operations similar to human pilots. The proposed control framework provides an effective solution for complex flight tasks and expands the flight control technologies for high-level unmanned helicopter operations. Full article

In the paper, a new cascade control system for an autonomous flight of an unmanned aerial vehicle (UAV) based on Proportional–Integral–Derivative (PID) and finite-time State-Dependent Riccati Equation (SDRE) control is proposed. The PID and SDRE controllers are used in a hybrid control system for precise control and stabilization, which is necessary to increase the drone’s flight stability and maneuver precision. The hybrid PID-SDRE control system proposed for the quadrotor model is quasi-optimal, since the suboptimal control algorithm for the UAV stabilization is used. The combination of the advantages of PID and SDRE control gives a significant improvement in the quality of control while maintaining the simplicity of the control system. Furthermore, the use of the suboptimal control technique provides the UAV attitude tracking in finite time. These remarks are drawn from a series of simulation tests conducted for the drone model. Full article

One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolutional Neural Network (CNN) model that utilizes the Single Shot multi-box Detector (SSD) as the baseline network and extends its small object detection performance with feature enhancement modules including super-resolution, deconvolution and feature fusion. These modules are collectively aimed at improving the feature representation of small objects at the prediction layer. The performance of the proposed model is evaluated using three datasets including two aerial images datasets that mainly consist of small objects. The proposed model is compared with the state-of-the-art small object detectors. Experiment results demonstrate improvements in the mean Absolute Precision (mAP) and Recall values in comparison to the state-of-the-art small object detectors that investigated in this study. Full article

A novel cooperative strategy for distributed unmanned aerial vehicle (UAV) swarms with different functions, namely the mission chain-driven unmanned aerial vehicle swarms cooperation method, is proposed to allow the fast search and timely rescue of injured human targets in a wide-area outdoor environment. First, a UAV-camera unit is exploited to detect the suspected human target combined with improved deep learning technology. Then, the target location information is transferred to a self-organizing network. Then, the special bio-radar-UAV unit was released to recheck the survivals through a respiratory characteristic detection algorithm. Finally, driven by the location and vital sign status of the injured, a nearby emergency-UAV unit will perform corresponding medical emergency missions, such as dropping emergency supplies. Experimental results show that this strategy can identify the human targets autonomously from the outdoor environment effectively, and the target detection, target sensing, and medical emergency mission chain is completed successfully relying on the cooperative working mode, which is meaningful for the future search-rescue mission of outdoor injured human targets. Full article

With the rising popularity of unmanned aerial vehicles (UAVs) and increasing variety of their applications, the task of providing reliable and robust control systems becomes significant. An active fault-tolerant control (FTC) scheme requires an effective fault detection and isolation (FDI) algorithm to provide information about the fault’s occurrence and its location. This work aims to present a prototype of a diagnostic system intended to recognize and identify broken blades of rotary wing UAVs. The solution is based on an analysis of acoustic emission recorded with an onboard microphone array paired with a lightweight yet powerful single-board computer. The standalone hardware of the FDI system was utilized to collect a wide and publicly available dataset of recordings in real-world experiments. The detection algorithm itself is a data-driven approach that makes use of an artificial neural network to classify characteristic features of acoustic signals. Fault signature is based on Mel Frequency Spectrum Coefficients. Furthermore, in the paper an extensive evaluation of the model’s parameters was performed. As a result, a highly accurate fault classifier was developed. The best models allow not only a detection of fault occurrence, but thanks to multichannel data provided with a microphone array, the location of the impaired rotor is reported, as well. Full article

Drones, which were first used in military applications, are now widely used by civilians for various purposes such as for deliveries and as cameras. There has been a lack of research into what drone users expect in terms of drone design and operation from a user perspective. In order to figure out what users want from drones, it is necessary to investigate the perception and design preferences of users with regard to drones. Surveys were conducted to collect data on preferences for various aspects of the design and operation of drone technology. Features relevant to the design and operation of drones were considered. We have identified the underlying factor structures of drone design and operation: outdoor mission type, user interface, military mission type, usefulness, risk, special mission type, and concern. The most important factors that contribute to all the dependent variables are the user interface and usefulness. The fact that drones will be increasingly used in the future is clear; however, the purpose of this study was to find out the areas on which to focus and pay further attention. Full article

This paper reports on the formation and transformation of multiple fixed-wing unmanned aerial vehicles (UAVs) in three-dimensional space. A cooperative guidance law based on the classic missile-type parallel-approach method is designed for the multi-UAV formation control problem. Additionally, formation transformation strategies for multi-UAV autonomous assembly, disbandment, and special circumstances are formed, effective for managing and controlling the formation. When formulating the management strategy for formation establishment, its process is divided into three steps: (i) selecting and allocating target points, (ii) forming loose formations, and (iii) forming short-range formations. The management of disbanding the formation is formulated through reverse thinking: the assembly process is split and recombined in reverse, and a formation disbanding strategy that can achieve a smooth transition from close to lose formation is proposed. Additionally, a strategy is given for adjusting the formation transformation in special cases, and the formation adjustment is completed using the adjacency matrix. Finally, a hardware-in-the-loop simulation and measured flight verification using a simulator show the practicality of the guidance law in meeting the control requirements of UAV formation flight for specific flight tasks. Full article

This paper presents the design of a small size Unmanned Aerial Vehicle (UAV) using the 3DEXPERIENCE software. The process of designing the frame parts involves many methods to ensure the parts can meet the requirements while conforming to safety and industry standards. The design steps start with the selection of materials that can be used for the drone, which are polylactic acid (PLA), acrylonitrile styrene acrylate (ASA), and acrylonitrile butadiene styrene (ABS). The drone frame consists of four main parts, which are the center top cover (50 g), the side top cover (10 g), the middle cover (30 g), and the drone’s arm (80 g). A simulation was carried out to determine the stress, displacement, and weight of the drone’s parts. Additionally, a trade-off study was conducted to finalize the shapes of the parts and the various inputs based on their priorities. The outcome of this new design can be represented in design concepts, which involve the use of the snap hook function to assemble two body parts together, namely the middle cover and the center top cover, without the need of an additional fastener. Full article

Swarms of unmanned vehicles (air and ground) can increase the efficiency and effectiveness of military and law enforcement operations by enhancing situational awareness and allowing the persistent monitoring of multiple hostile targets. The key focus in the development of the enabling technologies for swarm systems is the minimisation of uncertainties in situational awareness information for surveillance operations supported by ‘system of systems’ composed of static and mobile heterogeneous sensors. The identified critical enabling techniques and technologies for adaptive, informative and reconfigurable operations of unmanned swarm systems are robust static sensor network design, mobile sensor tasking (including re-allocation), sensor fusion and information fusion, including behaviour monitoring. The work presented in this paper describes one of the first attempts to integrate all swarm-related technologies into a prototype, demonstrating the benefits of swarms of heterogeneous vehicles for defence applications used for the persistent monitoring of high-value assets, such as military installations and camps. The key enabling swarm system technologies are analysed here, and novel algorithms are presented that can be implemented in available COTS-based unmanned vehicles. The algorithms have been designed and optimised to require small computational power, be flexible, be reconfigurable and be implemented in a large range of commercially available unmanned vehicles (air and ground). Full article

As an important component of autonomous intelligent systems, the research on autonomous positioning algorithms used by UAVs is of great significance. In order to resolve the problem whereby the GNSS signal is interrupted, and the visual sensor lacks sufficient feature points in complex scenes, which leads to difficulties in autonomous positioning, this paper proposes a new robust adaptive positioning algorithm that ensures the robustness and accuracy of autonomous navigation and positioning in UAVs. On the basis of the combined navigation model of vision/inertial navigation and satellite/inertial navigation, based on ESKF, a multi-source fusion model based on a federated Kalman filter is here established. Furthermore, a robust adaptive localization algorithm is proposed, which uses robust equivalent weights to estimate the sub-filters, and then uses the sub-filter state covariance to adaptively assign information sharing coefficients. After simulation experiments and dataset verification, the results show that the robust adaptive algorithm can effectively limit the impact of gross errors in observations and mathematical model deviations and can automatically update the information sharing coefficient online according to the sub-filter equivalent state covariance. Compared with the classical federated Kalman algorithm and the adaptive federated Kalman algorithm, our algorithm can meet the real-time requirements of navigation, and the accuracy of position, velocity, and attitude measurement is improved by 2–3 times. The robust adaptive localization algorithm proposed in this paper can effectively improve the reliability and accuracy of autonomous navigation systems in complex scenes. Moreover, the algorithm is general—it is not intended for a specific scene or a specific sensor combination– and is applicable to individual scenes with varied sensor combinations. Full article

Coaxial rotor systems are appealing for multirotor drones, as they increase thrust without increasing the vehicle’s footprint. However, the thrust of a coaxial rotor system is reduced compared to having the rotors in line. It is of interest to increase the efficiency of coaxial systems, both to extend mission time and to enable new mission capabilities. While some parameters of a coaxial system have been explored, such as the rotor-to-rotor distance, the influence of rotor pitch is less understood. This work investigates how adjusting the pitch of the lower rotor relative to that of the upper one impacts the overall efficiency of the system. A methodology based on blade element momentum theory is extended to coaxial rotor systems, and in addition blade-resolved simulations using computational fluid dynamics are performed. A coaxial rotor system for a medium-sized drone with a rotor diameter of 71.12 cm is used for the study. Experiments are performed using a thrust stand to validate the methods. The results show that there exists a peak in total rotor efficiency (thrust-to-power ratio), and that the efficiency can be increased by 2% to 5% by increasing the pitch of the lower rotor. The work contributes to furthering our understanding of coaxial rotor systems, and the results can potentially lead to more efficient drones with increased mission time. Full article

The rapid air-to-ground search of injured people in the outdoor environment has been a hot spot and a great challenge for public safety and emergency rescue medicine. Its crucial difficulties lie in the fact that small-scale human targets possess a low target-background contrast to the complex outdoor environment background and the human attribute of the target is hard to verify. Therefore, an automatic recognition method based on UAV bimodal information is proposed in this paper. First, suspected targets were accurately detected and separated from the background based on multispectral feature information only. Immediately after, the bio-radar module would be released and would try to detect their corresponding physiological information for accurate re-identification of the human target property. Both the suspected human target detection experiments and human target property re-identification experiments show that our proposed method could effectively realize accurate identification of ground injured in outdoor environments, which is meaningful for the research of rapid search and rescue of injured people in the outdoor environment. Full article

Ice accretion on commercial aircraft operating at high Reynolds numbers has been extensively studied in the literature, but a direct transformation of these results to an Unmanned Aerial Vehicle (UAV) operating at low Reynolds numbers is not straightforward. Changes in Reynolds number have a significant impact on the ice accretion physics. Previously, only a few researchers worked in this area, but it is now gaining more attention due to the increasing applications of UAVs in the modern world. As a result, an attempt is made to review existing scientific knowledge and identify the knowledge gaps in this field of research. Ice accretion can deteriorate the aerodynamic performance, structural integrity, and aircraft stability, necessitating optimal ice mitigation techniques. This paper provides a comprehensive review of ice accretion on fixed-wing UAVs. It includes various methodologies for studying and comprehending the physics of ice accretion on UAVs. The impact of various environmental and geometric factors on ice accretion physics is reviewed, and knowledge gaps are identified. The pros and cons of various ice detection and mitigation techniques developed for UAVs are also discussed. Full article

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones. Full article

When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users’ locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’ location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition. Full article

The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) type sensors. The data acquired from the sensors are processed using digital filters. In the literature, the results of research conducted on the effectiveness of Kalman and complementary filters are known. A significant problem in evaluating the performance of the studied filters was the lack of an arbitrarily determined UAV position. The authors of this paper undertook the task of determining the best filter for a real object. The main objective of this research was to improve the stability of the physical quadrotor. For this purpose, we developed a research method using a laboratory station for testing quadrotor drones. Moreover, using the MATLAB environment, they determined the optimal parameters for the real filter applied using the PX4 software, which is new and has not been considered before in the available scientific literature. It should be mentioned that the authors of this work focused on the analysis of filters most commonly used for flight stabilization, without modifying the structure of these filters. By not modifying the filter structure, it is possible to optimize the existing flight controllers. The main contribution of this study lies in finding the most optimal filter, among those available in flight controllers, for angular position estimation. The special emphasis of our work was to develop a procedure for selecting the filter coefficients for a real object. The algorithm was designed so that other researchers could use it, provided they collected arbitrary data for their objects. Selected results of the research are presented in graphical form. The proposed procedure for improving the embedded filter can be used by other researchers on their subjects. Full article