Fixed-wing Unmanned Aerial Vehicle Research Articles

Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study

At present, flight vehicle structure maintenance is undergoing a paradigm shift from preventive maintenance depending on calendar time and flight cycle to condition-based maintenance (CBM) using structural condition monitoring data. This shift is particularly relevant as unmanned aerial vehicle (UAV) systems increasingly undertake aerial missions. However, unlike CBM for single components, most UAV systems consist of multiple interconnected components. Optimizing pre-flight maintenance and ensuring traceability of inspections from a system-level perspective remains challenging. This study introduces a multi-layered decision-making policy for UAV reliability and stability multi-component evaluation, enhancing service condition monitoring and maintenance evaluation. The system adopts a knowledge-driven approach to develop a comprehensive maintenance framework for multi-component UAVs. It enables detailed damage assessment, effective management, predictive capabilities, and optimization strategies. By integrating and reasoning across knowledge-based, geometric, and decision-making models, the system supports dynamic maintenance and continuous iterative enhancements. To further reduce system complexity, this paper created a risk grating evaluation system. Within the framework of individual components, decision-making rules are established to optimally determine the components needing preventive maintenance when activated by decisions from risk assessments. Additionally, a new soft failure threshold model to determine the optimal maintenance decision variables is defined. This model incorporates general knowledge of the system and subsystems, further optimizing predictive reliability and economic dependency. Finally, the effectiveness and feasibility of this policy are validated through a fixed-wing UAV maintenance case study. The results demonstrate that the proposed framework holds significant promise for maintenance management in aircraft systems.

Aerodynamics evaluation and flight test of a vertical take-off and landing fixed-wing UAV with joined-wing configuration in transition flight state

The transition states between the cruising flight and the taking-off/landing process of a vertical take-off and landing (VTOL) fixed-wing UAV of joined-wing configuration are subject to significant unsteady aerodynamic interference. In this paper, the aerodynamic characteristics of this VTOL UAV during transition flights are evaluated by CFD with and without crosswind interference, in order to reveal the underlying mechanisms of the transition process. Based on these CFD-derived parameters, a flight test with a specifically-designed joined-wing VTOL UAV is proposed. The obtained results demonstrate that the forward flight speed is a crucial parameter during the takeoff transition phase. In the time interval of 5–15 s, significant disturbances are observed in the forces and moments due to the rotor deceleration and forward propeller acceleration, which result in slipstream and downwash flow effects. When crosswind disturbance is added, significant roll and yaw moments arise due to the vast vertical stabilizer area, which requires coordinated attitude adjustments between the rotor and the fixed-wing rudder surface. The descent transition phase is set a duration of 10 s. Four seconds later, as the rotor downwash flow intensifies, the lift force of the fixed wing is transferred to the rotor. When the combined lift becomes insufficient, the flight altitude decreases. When introducing crosswind disturbance, the entire aircraft undergoes significant additional pitch, yaw, and roll moments, with a maximum wave momentum greater than 200 %. Flight tests are then conducted using simulated parameters. The obtained results show that the take-off and landing transition responses without crosswinds are consistent with the predicted outcomes, which demonstrates the high effectiveness of the CFD simulations in predicting these transitions.

Autonomous UAV Chasing with Monocular Vision: A Learning-Based Approach

In recent years, unmanned aerial vehicles (UAVs) have shown significant potential across diverse applications, drawing attention from both academia and industry. In specific scenarios, UAVs are expected to achieve formation flying without relying on communication or external assistance. In this context, our work focuses on the classic leader-follower formation and presents a learning-based UAV chasing control method that enables a quadrotor UAV to autonomously chase a highly maneuverable fixed-wing UAV. The proposed method utilizes a neural network called Vision Follow Net (VFNet), which integrates monocular visual data with the UAV’s flight state information. Utilizing a multi-head self-attention mechanism, VFNet aggregates data over a time window to predict the waypoints for the chasing flight. The quadrotor’s yaw angle is controlled by calculating the line-of-sight (LOS) angle to the target, ensuring that the target remains within the onboard camera’s field of view during the flight. A simulation flight system is developed and used for neural network training and validation. Experimental results indicate that the quadrotor maintains stable chasing performance through various maneuvers of the fixed-wing UAV and can sustain formation over long durations. Our research explores the use of end-to-end neural networks for UAV formation flying, spanning from perception to control.

A High Performance Nonlinear Longitudinal Controller for Fixed-Wing UAVs Based on Fuzzy-Guaranteed Cost Control

Unmanned aerial vehicles (UAVs) have garnered more attention across various industries in recent years, leading to significant development in their design and application globally. Due to the high coupling between UAV states, model uncertainties, and various disturbances, precise longitudinal control of UAVs remains a significant research challenge. Oriented for the speed and altitude control of an electrical-powered fixed-wing UAV, this paper introduces a new control strategy based on the fuzzy guaranteed cost control (F-GCC) technique, which results in a nonlinear longitudinal state feedback control law with strict stability criterion, effectively addressing the issue of state coupling. Moreover, the strategy also includes a thrust estimation model of the electrical propulsion system to significantly reduce the nonlinearity, simplifying the controller design while effectively preserving tracking control performance. Through numerical validation, the UAV longitudinal nonlinear controller designed using the F-GCC technique offers better transient response and stronger robustness than the traditional linear and ADRC controllers.

Mission Design and Validation of a Fixed-Wing Unmanned Aerial Vehicle for Environmental Monitoring

Climate change is becoming a worldwide emergency. In order to prevent catastrophic levels of climate change, three broad categories of action are ongoing: cutting emissions, adapting to climate impacts, and financing required adjustments. Cutting emissions requires stopping the use of fossil fuels in favor of renewable energy sources. Adapting to climate change and financing required adjustments need instruments for the understanding of the source causes and how effective the potential measures are. In this context, the use of Unmanned Aerial Vehicles for environmental monitoring is continuously increasing thanks to their ability to collect a wide range of environmental data, from the quality of air to the health status of vegetation, waters, and lands. This paper describes the research activities that are being performed for the design and development of a 100 kg Max Take Off Mass prototype zero-emission Unmanned Aerial Vehicle, named Daphne, destined for environmental monitoring, surveillance, and inspection missions. The developed prototype will drive the next industrialization of the vehicle. A particular focus is given to the design of the power system, based on the use of Proton Exchange Membrane fuel cells fueled with green hydrogen, the integration of the sensors allowing for multipurpose observations and measurements, and the design and validation of the relative multi-purpose missions via an innovative approach based on Model-Based System Engineering.

Linear Disturbance Observer-Enhanced Continuous-Time Predictive Control for Straight-Line Path-Following Control of Small Unmanned Aerial Vehicles

This paper studies the straight-line path-following problem on the lateral plane for fixed-wing unmanned aerial vehicles (FWUAVs) which are susceptible to uncertainties. Firstly, based on the natural frame’s location on the prescribed reference paths, the command yaw angle (which is the basis for yaw angle control system design) is solved analytically by combining it with the errors of path following, attack angle, sideslip angle, attitude angles, and geometric parameters of the prescribed reference paths. Secondly, by considering complicated dynamic characteristics, a linear extended state observer is designed to estimate uncertainties such as nonlinearities, couplings, and unmodeled dynamics whose estimated values are incorporated into the continuous-time predictive controllers for feedback compensation. Finally, numerical simulations are conducted to demonstrate the advantages of the proposed method, including reduced tracking errors and enhanced robustness in the closed-loop system, as compared to the conventional nonlinear dynamic inversion and sliding mode control approaches.

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO & Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

Hierarchical ADP-ISMC Formation Control of Fixed-Wing UAVs in Local Frames Under Bounded Disturbances

Hierarchical ADP-ISMC Formation Control of Fixed-Wing UAVs in Local Frames Under Bounded Disturbances

Construction of a ballistic model of the motion of uncontrolled cargo during its autonomous high-precision drop from a fixed-wing unmanned aerial vehicle

The object of this study is the process related to the fall of an uncontrolled cargo from a height of 400–600 meters relative to sea level in an air environment under the influence of gravity, air drag, and wind in the presence of an initial air speed of about 20 m/s. The study solves the task to build a ballistic model of the movement of an unguided cargo during autonomous high-precision dropping from an unmanned aerial vehicle of aircraft type. A system of equations was derived that explicitly describes the dependence of the cargo's travel speed and coordinates on time and takes into account the effect of gravity, air drag, and the influence of wind. The scope of application of the equations corresponds to drop heights of up to 400 m relative to the surface of the earth and the initial horizontal speed of the cargo up to 20 m/s. The resulting equations were analyzed using an example of a spherical load weighing 10 kg and the largest cross-sectional area of 7.1·10-2 m2 when it falls from heights of 200, 300, and 400 m relative to the earth's surface. In the absence of wind, the horizontal component of the load's speed at the moment of falling is ≈(13–15) m/s, and the vertical component is ≈(50–60) m/s. At the same time, the horizontal displacement of the load under the conditions of a weak crosswind can reach ≈(150–220) m. With a vertical wind speed profile, the equivalent constant wind speed can be determined, resulting in the same effect on the load as a variable speed wind. An algorithm for determining the point of unloading the cargo has been proposed. The cargo delivery error has been evaluated. The most important parameters are the flight time of the cargo and the drop height. In order to achieve a hit accuracy of ±5 m, an error in determining the time of the fall of the load is not more than ≈0.16 s, and an error in determining the height of the drop is not more than ±8 m

Flatness-based path planning for fixed-wing UAVs in tight formation under synergistic aerodynamic constraints

Flatness-based path planning for fixed-wing UAVs in tight formation under synergistic aerodynamic constraints

Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network

This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13 % of energy and 65.93 % of flight time when the ground source and the ground destination are located 2000 m apart and need to transfer 5000bit/Hz of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.

Design and Implementation of A Low-Cost Parachute Landing System for Fixed-Wing Mini Unmanned Aerial Vehicles

Fixed-wing mini-UAVs (Unmanned Aerial Vehicles) face difficulties due to the need for runways during take-off and landing. While fixed-wing UAVs are capable of using catapults during take-off, various landing systems are required for landing. Therefore, in this study, a parachute system design and production were carried out for the safe landing of fixed-wing mini-UAVs. The produced parachute utilized ultra-lightweight ripstop nylon fabric and suspension lines, while a carbon fiber tube was chosen for the launching system for its lightweight and strength. The parachute deployment system was triggered by a servo motor with low power consumption and high torque. During tests, the parachute was activated at a height of 47 meters during flight. The parachute deployment was completed in 1.42 seconds, and the descent with the parachute lasted 11 seconds. The vertical descent speed of the parachute during landing was measured at 4.27 m/s. The produced parachute landing system was manufactured at 71% lower cost compared to existing parachute landing systems in the literature and on the market. Additionally, the ultra-light ripstop parachute weighed 56 grams, making it 12% lighter than similar systems. Considering the advantages in terms of cost and weight, it is anticipated that parachute landing systems will be increasingly used for fixed-wing UAVs in the future.

An Online Data-Driven Method for Accurate Detection of Thermal Updrafts Using SINDy

Utilizing thermal updrafts shows potential for enabling long-endurance cruising of fixed-wing unmanned aerial vehicles without energy consumption. This article presents a novel online method based on sparse identification of nonlinear dynamics (SINDy) approach to achievement identification of thermal sources in the atmosphere. Initially, the algorithm is incorporated into the upper-level planning system, interacting with the lower-level controller. Then, experiments are conducted through software-in-the-loop simulations (SITL) to validate the implementation of the proposed algorithm. It is found that direct observation of thermal sources through measurements using SINDy is unfeasible during straight and circular flight modes. Nevertheless, simulation analysis of the proposed approach indicates that under unobservable conditions, a portion of the parameters can still be identified. By comparing results obtained using the particle filter algorithm, this method is shown to accurately estimate the parameters with negligible errors under observability conditions. The novelty of this approach lies in its significant improvement of the localization accuracy of the thermal source, without the need for parameter adjustments in the algorithm. Finally, the proposed methods are integrated into commonly used hardware platforms, and their online feasibility is verified through hardware-in-the-loop simulations.

Design and implementation of voice-command controller for fixed-wing unmanned aerial vehicles using automatic speech recognition and natural language processing techniques

This paper explores the development of a voice command controller leveraging the capabilities of an automatic speech recognition (ASR) system and natural language processing (NLP) technique to manage a fixed-wing unmanned aerial vehicle (UAV). The controller is designed to interpret voice commands for controlling fixed-wing UAVs. The implementation of the system involved two key stages: (1) implementation of a voice command controller using integrated ASR and NLP techniques deployed in a simulated plane in the SITL simulator followed by (2) deployment of the controller to an actual Sky Surfer plane fixed-wing aircraft. The results indicate that the algorithm achieved an average confidence rate of 91.86 % in transcribing voice commands to words, with a Word Error Rate (WER) of approximately 0.021. The developed system demonstrated the ability to interpret both low-level and high-level commands for UAV control interfaces. Such an interface offers greater intuitiveness compared to traditional RC controls, potentially requiring less training to operate effectively. Moreover, it reduces human workload, as once commands are issued, the system can execute them without the need for continuous supervision.

3D Modelling and Measuring Dam System of a Pellucid Tufa Lake Using UAV Digital Photogrammetry

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.

Control of a fixed wing unmanned aerial vehicle using a higher-order sliding mode controller and non-linear PID controller

Unmanned aerial vehicles (UAVs) have seen a rise in use during the last few years. Such aircrafts are now a convenient way to complete dangerous, dirty, and tedious tasks. Given that their operation involves a control problem which is non-linear and coupled, it is difficult to analyse. This paper presents the modeling and control of a fixed-wing unmanned aircraft as a contribution to this field. The system’s flight dynamics is derived using Newton’s second law of motion. The system is designed to have a non-linear Proportional Integral Derivative (NPID) controller and a higher-order sliding mode controller (HOSMC). When simulating the system using MATLAB Simulink software, an external disturbance was added to test the robustness of the controllers. Five performance indices which include mean square error (MSE), integral time square error (ITSE), integral absolute error (IAE), integral time absolute error (ITAE), and integral square error (ISE), were used to compare the controllers performance. These indices are used to provide a numerical assessment of the two controllers’ performance. The outcomes demonstrate that the roll, pitch, and yaw states performed better than the super-twisting sliding mode controller. On the airspeed control, the non-linear PID performed better than the super-twisting sliding mode controller.

Fixed-wing UAV obstacle avoidance algorithm based on real terrain

Abstract This paper presents an obstacle avoidance algorithm for UAVs based on real terrain constraints. The common obstacle avoidance algorithm often abstracts the obstacle as a sphere or a circle or studies the UAV’s two-dimensional obstacle avoidance on a raster map. With the development of fixed-wing UAVs, UAVs have longer and longer time in the air, longer and longer range, and the low-altitude environment they face is often difficult to simulate with simplified regular shape. Thus, this paper constructs the UAV flight terrain environment using open elevation data and designs an obstacle avoidance algorithm accordingly. When the UAV is tracking the track point, it can better control the UAV to avoid obstacles and fly to the target track point.

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance