Autonomous Flight Research Articles

Whole-Body Control for Autonomous Landing of Unmanned Helicopter Equipped With Antagonistic Cable-Driven Legged Landing Gear

Whole-Body Control for Autonomous Landing of Unmanned Helicopter Equipped With Antagonistic Cable-Driven Legged Landing Gear

Conformally Decambered Natural Laminar Flow Blades for Vertical-Axis Wind Turbines

A two-element natural laminar flow airfoil—commonly used on medium-altitude, long-endurance, unmanned air vehicles—was conformally decambered for application on a two-bladed, H-rotor, vertical-axis wind turbine. Blade kinematics were used to determine the virtual camber line, which was then used to conformally map the original profile onto the chord line. Both decambered and original blade profiles were evaluated experimentally using large chord-radius ratios (0.6 and 0.75) that exploited dynamic stall to produce the driving torque. Decambered blades showed substantially greater power and torque coefficients than the original blades, up to 60 and 27%, respectively, which represents the first experimental validation of conformal decambering. Relatively large peak power coefficients of 0.28 were attained, despite maximum chord-based Reynolds numbers being less than 2×105. Depending upon the chord-radius ratio, either light or deep dynamic stall occurred in the second upstream quadrant, and the flap flow remained attached virtually throughout. In contrast, on the original profiles, massive separation was observed on the blades, and the flap flow remained separated due to assumed outer surface flow separation. Future research should consider surface pressure and flowfield measurements, significantly higher Reynolds numbers, and variable intracycle flap deflection mechanisms to optimize performance and minimize unsteady loads.

Maximisation the autonomous flight performance of unmanned helicopter using BSO algorithm

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

Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

Autonomous flight performance maximization for slung load carrying rotary wing mini unmanned aerial vehicle

PurposeThis research study aims to minimize autonomous flight cost and maximize autonomous flight performance of a slung load carrying rotary wing mini unmanned aerial vehicle (i.e. UAV) by stochastically optimizing autonomous flight control system (AFCS) parameters. For minimizing autonomous flight cost and maximizing autonomous flight performance, a stochastic design approach is benefitted over certain parameters (i.e. gains of longitudinal PID controller of a hierarchical autopilot system) meanwhile lower and upper constraints exist on these design parameters.Design/methodology/approachA rotary wing mini UAV is produced in drone Laboratory of Iskenderun Technical University. This rotary wing UAV has three blades main rotor, fuselage, landing gear and tail rotor. It is also able to carry slung loads. AFCS variables (i.e. gains of longitudinal PID controller of hierarchical autopilot system) are stochastically optimized to minimize autonomous flight cost capturing rise time, settling time and overshoot during longitudinal flight and to maximize autonomous flight performance. Found outcomes are applied during composing rotary wing mini UAV autonomous flight simulations.FindingsBy using stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads over previously mentioned gains longitudinal PID controller when there are lower and upper constraints on these variables, a high autonomous performance having rotary wing mini UAV is obtained.Research limitations/implicationsApproval of Directorate General of Civil Aviation in Republic of Türkiye is essential for real-time rotary wing mini UAV autonomous flights.Practical implicationsStochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads is properly valuable for recovering autonomous flight performance cost of any rotary wing mini UAV.Originality/valueEstablishing a novel procedure for improving autonomous flight performance cost of a rotary wing mini UAV carrying slung loads and introducing a new process performing stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads meanwhile there exists upper and lower bounds on design variables.

Extended State Observer-Based Command-Filtered Safe Flight Control for Unmanned Helicopter under Time-Varying Path Constraints and Disturbances

Extended State Observer-Based Command-Filtered Safe Flight Control for Unmanned Helicopter under Time-Varying Path Constraints and Disturbances

Innovative Hybrid UAV Design, Development, and Manufacture for Forest Preservation and Acoustic Surveillance

The research described in this paper focuses on the development of an innovative unmanned aerial vehicle (UAV) tailored for a specific mission: detecting the acoustic signature emitted by chainsaws, identifying deforestation, and reporting its location for legality assessment. Various calculations were conducted to determine the optimal solution, resulting in the choice of a fixed-wing UAV. A comparative analysis between tri-rotor and quadcopter systems was performed, leading to the selection of the tri-rotor configuration. The primary objective of this study is to design an innovative hybrid UAV concept with key features including a fixed-wing design and integrated VTOL (vertical takeoff and landing) capability in the experimental model. The aircraft has been constructed using advanced materials such as fiber-reinforced polymer composites, manufactured using both conventional and advanced techniques like continuous fiber additive manufacturing and the use of a polymer matrix. Additionally, the aerodynamic configuration is optimized to achieve a cruise speed of approximately 50 km/h and a flight autonomy exceeding 3 h. The UAV has been equipped with payloads for mounting sensors to collect meteorological data, and crucially, the VTOL system has been optimized to vectorize thrust for improved performance during the transition from hover to cruise flight. This paper details the entire manufacturing and assembly process of the drone, covering both the structural framework and associated electrical installations. A dedicated sound detection system is incorporated into the drone to identify chainsaw noise, with the aim of preventing deforestation.

Computational Validation and Assessment of Large Amplitude Transverse Gust Physics

Rotary-wing vehicles, in particular smaller, lighter unmanned and urban air vehicles in urban and shipboard settings, will operate in low-speed flight conditions that are dominated by strong transient aerodynamics. Understanding the physics of these transient aerodynamics, specifically large amplitude transverse gusts and the resulting vehicle response, is crucial to the successful development and certification of safe air vehicles that operate in these environments. A high-fidelity computational study, including validation with experiments, explores sharp-edged or step transverse gusts where the gust velocity induces nonlinear behavior caused by flow separation. The behavior of the maximum lift and leading edge vortex behavior with the gust ratio is presented. Gust responses are observed to depart from Küssner’s theory when the leading edge vortex first forms as a distinct feature and breaks away from the wing, resulting in flow nonlinearities. Traditional linear indicial admittance techniques are shown to no longer be valid to predict gust responses when the gust velocity approaches the vehicle flight speed.

Race Against the Machine: A Fully-Annotated, Open-Design Dataset of Autonomous and Piloted High-Speed Flight

Race Against the Machine: A Fully-Annotated, Open-Design Dataset of Autonomous and Piloted High-Speed Flight

A Photosensitivity-Enhanced Plant Growth Algorithm for UAV Path Planning.

With the rise and development of autonomy and intelligence technologies, UAVs will have increasingly significant applications in the future. It is very important to solve the problem of low-altitude penetration of UAVs to protect national territorial security. Based on an S-57 electronic chart file, the land, island, and threat information for an actual combat environment is parsed, extracted, and rasterized to construct a marine combat environment for UAV flight simulation. To address the problem of path planning for low-altitude penetration in complex environments, a photosensitivity-enhanced plant growth algorithm (PEPG) is proposed. Based on the plant growth path planning algorithm (PGPP), the proposed algorithm improves upon the light intensity preprocessing and light intensity calculation methods. Moreover, the kinematic constraints of the UAV, such as the turning angle, are also considered. The planned path that meets the safety flight requirements of the UAV is smoother than that of the original algorithm, and the length is reduced by at least 8.2%. Finally, simulation tests are carried out with three common path planning algorithms, namely, A*, RRT, and GA. The results show that the PEPG algorithm is superior to the other three algorithms in terms of the path length and path quality, and the feasibility and safety of the path are verified via the autonomous tracking flight of a UAV.

Precision Landing of Unmanned Aerial Vehicle under Wind Disturbance Using Derivative Sliding Mode Nonlinear Disturbance Observer-Based Control Method

Unmanned aerial vehicles (UAVs) are extensively employed in civilian and military applications because of their excellent maneuverability. Achieving fully autonomous quadrotor flight and precision landing on a wireless charging station in the presence of wind disturbance has become a crucial research topic. This paper presents a composite control technique for UAV altitude and attitude tracking in harsh environments, i.e., wind disturbance. A composite controller was developed based on nonlinear disturbance observer (NDOB) control theory to allow the UAV to land in the presence of random external wind disturbances and ground effects. The NDOB estimated the unknown wind disturbance, and the estimation was fed into the derivative sliding mode nonlinear disturbance observer-based control (DSMNDOBC), allowing the UAV to perform autonomous precision landing. Two loop designs were applied: the inner loop for stabilization and the outer loop for altitude tracking. The quadrotor model dynamics and the proposed controller, DSMNDOBC, were simulated employing MATLAB/Simulink®, and the results were compared with the one obtained by the proportional derivative (PD) controller and the sliding mode controller (SMC). The simulation results indicated that the DSMNDOBC has superior altitude and attitude control compared to the PD and SMC controllers and better disturbance estimation and attenuation performance.

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Qualification of Avionic Software Based on Machine Learning: Challenges and Key Enabling Domains

Advances in machine learning (ML) open the way to innovating functions in the avionic domain, such as navigation/surveillance assistance (e.g., vision-based navigation, obstacle sensing, virtual sensing), speech-to-text applications, autonomous flight, predictive maintenance, and cockpit assistance. Current standards and practices, which were defined and refined over decades with classical programming in mind, do not, however, support this new development paradigm. This paper provides an overview of the main challenges raised by the use of ML in the demonstration of compliance with regulatory requirements (i.e., software qualification) and an overview of literature relevant to these challenges, with particular focus on the issues of robustness, provability, and explainability of ML results.

Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment

Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view.

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

Cable-driven legged landing gear for unmanned helicopter: Prototype design, optimization and performance assessment

Cable-driven legged landing gear for unmanned helicopter: Prototype design, optimization and performance assessment

Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing

Uncrewed aerial vehicles are integral to a smart city framework, but the dynamic environments above and within urban settings are dangerous for autonomous flight. Wind gusts caused by the uneven landscape jeopardize safe and effective aircraft operation. Birds rapidly reject gusts by changing their wing shape, but current gust alleviation methods for aircraft still use discrete control surfaces. Additionally, modern gust alleviation controllers challenge small uncrewed aerial vehicle power constraints by relying on extensive sensing networks and computationally expensive modeling. Here we show end-to-end deep reinforcement learning forgoing state inference to efficiently alleviate gusts on a smart material camber-morphing wing. In a series of wind tunnel gust experiments at the University of Michigan, trained controllers reduced gust impact by 84% from on-board pressure signals. Notably, gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six pressure tap signals. By efficiently rejecting environmental perturbations, reduced-sensor fly-by-feel controllers open the door to small uncrewed aerial vehicle missions in cities.

Design methodology and simulation analysis of hybrid fuel cell and battery systems for powering unmanned aircraft systems

Hybrid fuel cell and battery systems are currently among the most promising options for increasing the flight autonomy of unmanned aircraft systems. The ratio between the size of the battery and the fuel cell is a delicate issue in power system design. Both devices are necessary; the battery, with a quick response, to respond to demand peaks, and the fuel cell, with a slow dynamic response, but greater energy density, to increase its autonomy. One of the most important tasks in designing the hybrid system is selecting the appropriate size of each component so that it can respond to peak demands, extend its autonomy as far as possible and reduce its weight.The purpose of this research was to establish a design procedure for hybrid power systems for unmanned aerial systems, which means selecting the best size for each component. The hybrid system's modeling and the dynamic simulation of its response to demand curves are the foundation of this procedure. The simulation results include the consumption of the fuel cell as a function of time, the battery’s charge status and an estimate of its autonomy for each demand curve. The analysis of the simulation results guides the choice of component size, adapting each one to achieve minimum fuel consumption for the characteristic demand curves for which the unmanned aircraft system is designed. After modeling the components of the hybrid system and describing the simulation procedure, this contribution proposes an example of a design for an unmanned aircraft system with a maximum takeoff weight of 20 kg. In this case, the most appropriate size for the fuel cell was 2000 W and a 3000 mAh 10S Lithium-Polymer battery. Fitted with a 12-liter hydrogen tank, stored at a maximum pressure of 300 bar, an estimated autonomy of 1.5 h can be expected.

Uncrewed aerial vehicle with onboard winch system for rapid, cost-effective, and safe oceanographic profiling in hazardous and inaccessible areas

Interactions between coastal waters and marine-terminating glaciers in the Polar Regions play a significant role in global sea level rise fueled by a rapidly warming Arctic.The risk of glacier calving, and the abundance of ice, can make it impossible for surface vessels to access the waters near glacier termini. Alternative methods using manned aircraft are expensive. As a result, oceanographic measurements are limited near glacier termini.We present an uncrewed aerial vehicle (UAV) with an on-board winch system that allows oceanographic profiling in remote, hazardous areas using a commercial conductivity, temperature, and depth (CTD) sensor payload. The UAV is optimized for easy handling and deployment and is capable of high-speed and efficient cruise flight. An autopilot system provides pilot assistance and autonomous flight capabilities. The total weight of the UAV including payload is 6.5 kg with an endurance of 24 min.Testing of the system was conducted in South Greenland during winter conditions in March 2023 with successful profiles collected near a glacier terminus (<5 m) and in small openings in ice mélange (2.2 m). The system proved capable, reliable, and efficient. Further development of the system will allow other sensors for an even more flexible measurement suite.

Evaluation of the use of polylactic acid in 3d printing for the construction of an unmanned aerial vehicle in the department of cordoba

The use of polylactic acid (PLA) in the technology of fused deposition modeling (FDM), better known as 3D printing, to create all kinds of parts, from prototypes and simple pieces to highly technical end products, is a significant contender due to its wide range of properties compared to other petrochemical-based composite materials. The aim of this study was to evaluate the use of polylactic acid (PLA) for the construction of unmanned aerial vehicles (UAVs) through 3D printing. Power and force calculations for the UAV were performed, and a structural analysis using SolidWorks software was conducted to calculate maximum and minimum stresses and determine optimal manufacturing parameters. Additionally, an aerodynamic analysis using ANSYS software was carried out to determine the drag coefficient and the forces acting on the UAV. It was found that the force exerted by the motors on the arms is 13,63 N, and the power is 536 N. In the structural analysis, it was determined that the minimum stresses are located in the chassis arms, while the maximum stresses are found where the chassis is attached, with a value of 6,604 MPa and a safety factor of 3,23. In the aerodynamic analysis, a drag force of 1 N and a flight power of 108,44 N were calculated. The UAV achieved a flight height of 48 m and a flight autonomy of 17 minutes, concluding that 3D printing technology is a viable and cost-effective alternative.