Search Results (64)

The electric vertical take-off and landing fixed-wing (FW-VTOL) unmanned aerial vehicle (UAV) combines the advantages of fixed-wing aircraft and multi-rotor aircraft. Based on the cell discharge characteristics and the power system features, this paper proposes a preliminary design and optimization method suitable for electric FW-VTOL UAVs. The purpose of this method is to improve the design accuracy of electric propulsion systems and overall parameters when dealing with the special power and energy requirements of this type of aircraft. The core of this method involves testing the performance data of the cell inside the battery pack, using small-capacity cells as the basic unit for battery sizing, thereby constructing a power battery performance model. Additionally, it establishes optimization design models for propellers and rotors and develops a brushless DC motor performance model based on a first-order motor model and statistical data, ultimately achieving optimized matching of the propulsion system and completing the preliminary design of the entire aircraft. Using a battery discharge model established based on real cell parameters and test data, the impact of the discharge process on battery performance is evaluated at the cell level, reducing the subjectivity of battery performance evaluation compared to the constant power/energy density method used in traditional battery sizing processes. Furthermore, matching the optimization design of power and propulsion systems effectively improves the accuracy of the preliminary design for FW-VTOL UAVs. A design case of a 30 kg electric FW-VTOL UAV is conducted, along with the completion of flight tests. The design parameters obtained using the proposed method show minimal discrepancies with the actual data from the actual aircraft, confirming the effectiveness of the proposed method. Full article

The increasing use of Unmanned Aerial Vehicles (UAVs) demands enhanced flight safety systems. This study presents the development of an affordable and efficient Sense and Avoid (S&A) system for small fixed-wing UAVs, typically under 25 kg and fly at speeds of up to 15 m/s. The system integrates multiple non-cooperative sensors, two ultrasonic sensors, two laser rangefinders, and one LiDAR, along with a Pixhawk 6X flight controller and a Raspberry Pi CM4 companion computer. A collision avoidance algorithm utilizing the Vector Field Histogram method was implemented to process sensor data and generate real-time trajectory corrections. The system was validated through experiments using a ground rover, demonstrating successful obstacle detection and avoidance with real-time trajectory updates at 10 Hz. Full article

This paper presents a methodology for training a Deep Learning model aimed at flight management tasks in a fixed-wing unmanned aerial vehicle (UAV), specifically autopilot control and GPS prediction. In this formulation, sensor data and the most recent GPS signal are first processed by an LSTM to produce an initial coordinate prediction. This preliminary estimate is then merged with additional sensor inputs and passed to an MLP, which replaces the conventional autopilot algorithm by generating the control commands for real-time navigation. The approach is particularly valuable in scenarios where the aircraft must follow a predetermined route—such as surveillance operations—or maintain a remote ground link under varying GPS availability. The study focuses on Class I UAVs; however, the proposed methodology can be adapted to larger classes (II and III) by adjusting sensor configurations and network parameters. To collect training data, a small fixed-wing aircraft was instrumented to record kinematic and control inputs, which then served as inputs to the neural network. Despite the limited sensor suite and the use of an open-source flight controller (SpeedyBee), the flexibility of the proposed approach allows for easy adaptation to more complex UAVs equipped with additional sensors, potentially improving prediction accuracy. The performance of the neural network, implemented in PyTorch, was evaluated by comparing its predicted data with actual flight logs. In addition, the method has been shown to be robust to both short and prolonged GPS outages, as it relies on waypoint-based navigation along previously explored routes, ensuring reliable performance in known operational contexts. Full article

Among the several usages of unmanned aerial vehicles (UAVs), a wireless relay system is one of the most promising applications. Specifically, a small fixed-wing UAV is suitable to establish the system promptly. In the system, an antenna pointing control system directs an onboard antenna to a ground station in order to form and maintain a communication link between the UAV and the ground station. In this paper, we propose a sensor system to detect the direction of the ground station from the UAV by using color marker patterns for the antenna pointing control system. The sensor detects the difference between the antenna pointing direction and the ground station direction. The sensor is characterized by the usage of both the color information of multiple color markers and color marker pattern matching. These enable the detection of distant, low-resolution markers, a high accuracy of marker detection, and robust marker detection against motion blur. In this paper, we describe the detailed algorithm of the sensor, and its performance is evaluated by using the prototype sensor system. Experimental performance evaluation results showed that the proposed method had a minimum detectable drawing size of 10.2 pixels, a motion blur tolerance of 0.0175, and a detection accuracy error of less than 0.12 deg. This performance indicates that the method has a minimum detectable draw size that is half that of the ArUco marker (a common AR marker), is 15.9 times more tolerant of motion blur than the ArUco marker, and has a detection accuracy error twice that of the ArUco marker. The color markers in the proposed method can be placed farther away or be smaller in size than ArUco markers, and they can be detected by the onboard camera even if the aircraft’s attitude changes significantly. The proposed method using color marker patterns has the potential to improve the operational flexibility of radio relay systems utilizing UAVs and is expected to be further developed in the future. Full article

This research investigates the optimization of airfoil design for fixed-wing drones, aiming to enhance aerodynamic efficiency and reduce drag. The research employs Kulfan CST and Bézier surface parameterization methods combined with global sensitivity analysis (GSA) and machine learning techniques to improve airfoil performance under various operational conditions. Particle swarm optimization (PSO) is utilized to optimize the airfoil design, minimizing drag in cruise and ascent conditions while ensuring lift at takeoff. Computational fluid dynamics (CFD) simulations, primarily using XFOIL, validate the aerodynamic performance of the optimized airfoils. This study also explores the generative design approach using a neural network trained on 10 million airfoil simulations to predict airfoil geometry based on desired performance criteria. The results show important improvements in drag reduction, especially during low-speed cruise and ascent phases, contributing to extended flight endurance and efficiency. These results can be used for small unmanned aerial vehicles (UAVs) in real-world applications to develop better-performance UAVs under mission-specific constraints. Full article

The increasing complexity of aerial acrobatics missions necessitates ever-higher levels of attitude control precision in fixed-wing unmanned aerial vehicles (UAVs). Traditional control methods, such as feedback linearization and small disturbance derivation linear models, falter in maintaining attitude tracking accuracy, due to the presence of unanticipated disturbances—most notably, wave disturbances during low-altitude maritime flights—and model uncertainties introduced by factors like large-angle maneuvers, intricate aerodynamic characteristics, and fuel consumption. Consequently, these limitations impede the successful execution of intricate maneuvers, such as looping, the split-S, the Immelmann turn, and the Pougatcheff cobra maneuver. In response to these challenges, we propose an integral sliding mode control based on disturbance observer (ISMC-DO) system to achieve robust attitude angle tracking amidst model uncertainties and mitigate the effects of wave disturbances. Additionally, quaternion representations are adopted as a supplement to Euler angles, thereby resolving the singularity issues inherent in the latter. By using the Lyapunov function, the ISMC-DO-based control system is shown to be asymptotically stable. Simulation results further validate that ISMC-DO can achieve high-precision attitude tracking control of the UAV under wave disturbance. Full article

The development of unconventional and hybrid unoccupied aerial vehicles (UAVs) has gained significant momentum in recent years, with many designs utilizing small fans or rotary blades for vertical take-off and landing (VTOL). However, these systems often inherit the limitations of traditional helicopter rotors, including susceptibility to aerodynamic inefficiencies and mechanical issues. Additionally, achieving a seamless transition from VTOL to fixed-wing flight mode remains a significant challenge for hybrid UAVs. A novel approach is the reciprocating airfoil (RA) or reciprocating wing (RW) VTOL aircraft, which employs a fixed-wing configuration driven by a reciprocating mechanism to generate lift. The RA wing is uniquely designed to mimic a fixed-wing while leveraging its reciprocating motion for efficient lift production and a smooth transition between VTOL and forward flight. Despite its advantages, the RA wing endures substantial stress due to the high inertial forces involved in its operation. This study presents an optimized structural design of the RA wing through wing topology optimization and finite element analysis (FEA) to enhance its load-bearing capacity and stress performance. A comparative analysis with existing RA wing configurations at maximum operating velocities highlights significant improvements in the safety margin, failure criteria, and overall stress distribution. The key results of this study show an 80.4% reduction in deformation, a 43.8% reduction in stress, and a 78% improvement in safety margin. The results underscore the RA wing’s potential as an effective and structurally stable lift mechanism for RA-driven VTOL aircraft, demonstrating its capability to enhance the performance and reliability of next-generation UAVs. Full article

Unmanned aerial vehicle (UAV) technology has recently experienced increasing development, leading to the creation of a wide variety of autonomous solutions. In this paper, a guidance strategy for straight and orbital paths following fixed-wing small UAVs is presented. The proposed guidance algorithm is based on a reference vector field as desired, with 16 courses for the UAV to follow. A sliding mode approach is implemented to improve the robustness and effectiveness, and the asymptotic convergence of the aircraft to the desired trajectory in the presence of constant wind disturbances is proved according to Lyapunov. The algorithm exploits the banking dynamics and generates reference signals for the inner-loop aileron control. A MATLAB&Simulink^® simulation environment is used to verify the performance and robustness of the compared guidance algorithms. This high-fidelity model considers the six-degrees-of-freedom (DoF) whole-flight dynamics of the UAV and it is based on experimental flight test data to implement the aerodynamic behavior. Full article

Electrothermal de-icing systems are a popular approach to protect unmanned aerial vehicles (UAVs) from the performance degradation caused by in-cloud icing. However, their power and energy requirements must be minimized to make these systems viable for small and medium-sized fixed-wing UAVs. Thermal de-icing systems allow intercycle ice accretions and can result in runback icing. Intercycle and runback ice increase the aircraft’s drag, requiring more engine thrust and energy. This study investigates the aerodynamic influence of intercycle and runback ice on a typical UAV wing. Lift and drag coefficients from a wind tunnel campaign and Ansys FENSAP-ICE simulations are compared. Intercycle ice shapes result in a drag increase of approx. 50% for a realistic cruise angle of attack. While dispersed runback ice increases the drag by 30% compared to the clean wing, a spanwise ice ridge can increase the drag by more than 170%. The results highlight that runback ice can significantly influence the drag coefficient. Therefore, it is important to design the de-icing system and its operation sequence to minimize runback ice. Understanding the need to minimize runback ice helps in designing viable de-icing systems for UAVs. Full article

Small air-launched unmanned aerial vehicles (UAVs) face challenges in range and endurance due to their compact size and lightweight design. To address these issues, this paper introduces a multi-phase wind energy harvesting trajectory planning method designed to optimize the onboard electrical energy consumption during rendezvous and formation flight of air-launched fixed-wing swarms. This method strategically manages gravitational potential energy from air-launch deployments and harvests wind energy that aligns with the UAV’s flight speed. We integrate wind energy harvesting strategies for single vehicles with the spatial–temporal coordination of the swarm system. Considering the wind effects into the trajectory planning allows UAVs to enhance their operational capabilities and extend mission duration without changes on the vehicle design. The trajectory planning method is formalized as an optimal control problem (OCP) that ensures spatial–temporal coordination, inter-vehicle collision avoidance, and incorporates a 3-degree of freedom kinematic model of UAVs, extending wind energy harvesting trajectory optimization from an individual UAV to swarm-level applications. The cost function is formulized to comprehensively evaluate electrical energy consumption, endurance, and range. Simulation results demonstrate significant energy savings in both low- and high-altitude mission scenarios. Efficient wind energy utilization can double the maximum formation rendezvous distance and even allow for rendezvous without electrical power consumption when the phase durations are extended reasonably. The subsequent formation flight phase exhibits a maximum endurance increase of 58%. This reduction in electrical energy consumption directly extends the range and endurance of air-launched swarm, thereby enhancing the mission capabilities of the swarm in subsequent flight. Full article

Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given. To enhance UAV stability and resist atmospheric disturbances at sea, the configuration is optimized using an improved genetic algorithm. Then, three typical multi-UAV formations are analyzed. Finally, the trajectory is designed based on an analysis of its influence on EMI at the MAD. The simulation results show that the low-EMI design can keep MADs away from the EMI sources of UAVs and maintain flight stability. The thread-like formation is the best choice in terms of mutual interference and search width. The results also reveal the close relationship between the low-EMI design and flight trajectory. This research can provide a reference for the conceptual design and trajectory optimization of small, fixed-wing UAVs for magnetic anomaly detection. Full article

A combined control method integrating Linear Active Disturbance Rejection Control (LADRC) and Sliding Mode Control (SMC) is proposed to mitigate model uncertainty and external disturbances in the attitude control of fixed−wing unmanned aerial vehicles (UAVs). First, the mathematical and dynamic models of a small fixed−wing UAV are constructed. Subsequently, a Linear Extended State Observer (LESO) is designed to accurately estimate the model uncertainties and unidentified external disturbances. The LESO is then integrated into the control side to enable the SMC to enhance the control system’s anti−interference performance due to its insensitivity to variations in−system parameters. The system’s stability is proven using the Lyapunov stability theory. Finally, simulations comparing the classical LADRC and the newly developed SMC−LADRC reveal that the latter exhibits strong robustness and anti−interference capabilities in scenarios involving model uncertainty, external disturbances, and internal disturbances, confirming the effectiveness of this control method. Full article

In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems. Full article

With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of three phases: long-distance object detection, object region estimation, and collision risk assessment and collision avoidance. For long-distance object detection, an optical flow-based background subtraction method is utilized to detect an intruder far away from the host. A mask region-based convolutional neural network (Mask R-CNN) model is trained to estimate the region of the intruder in the image. Finally, the collision risk assessment adopts the area expansion rate and bearing angle of the intruder in the images to conduct mid-air collision avoidance based on visual flight rules (VFRs) and conflict areas. The proposed collision avoidance approach is verified by both simulations and experiments. The results show that the system can successfully detect different sizes of fixed-wing intruders, estimate their regions, and assess the risk of collision at least 10 s in advance before the expected collision would happen. Full article

The safety enhancement of small fixed-wing UAVs regarding obstacle detection is addressed using optimization techniques to find the best sensor orientations of different multi-sensor configurations. Four types of sensors for obstacle detection are modeled, namely an ultrasonic sensor, laser rangefinder, LIDAR, and RADAR, using specifications from commercially available models. The simulation environment developed includes collision avoidance with the Potential Fields method. An optimization study is conducted using a genetic algorithm that identifies the best sensor sets and respective orientations relative to the UAV longitudinal axis for the highest obstacle avoidance success rate. The UAV performance is found to be critical for the solutions found, and its speed is considered in the range of 5–15 m/s with a turning rate limited to 45°/s. Forty collision scenarios with both stationary and moving obstacles are randomly generated. Among the combinations of the sensors studied, 12 sensor sets are presented. The ultrasonic sensors prove to be inadequate due to their very limited range, while the laser rangefinders benefit from extended range but have a narrow field of view. In contrast, LIDAR and RADAR emerge as promising options with significant ranges and wide field of views. The best configurations involve a front-facing LIDAR complemented with two laser rangefinders oriented at ±10° or two RADARs oriented at ±28°. Full article