Landing Of Unmanned Aerial Vehicle Research Articles

Unmanned aerial vehicle landing localization technology based on visual dual-channel uniqueness coding

Unmanned aerial vehicle (UAV) mainly uses GPS (Global Positioning System) for positioning, but it is difficult to locate in areas where GPS signal is missing or being disturbed. Therefore, the relative positioning technology based on visual method is also widely used in UAV landing, but due to the influence of cross wind and the motion of the landing plane, the traditional visual landing sign and positioning method is prone to lose the positioning target at the end stage of the landing. Therefore, this article proposes a landing sign based on double-layer unique coding, which can realize continuous positioning in the horizontal direction and accurate positioning of large-scale changes in the vertical direction. Firstly, real-time image acquisition and image processing are carried out through the airborne camera. Based on the flight altitude and encoding extraction effect of the UAV, the red or blue channel component is extracted, and then the image is preprocessed, edge detection, line detection, coding grid extraction, etc. As long as any 5 × 5 small squares of the whole pattern can be extracted, the perspective-n-point algorithm is used to obtain the relative pose estimation between the UAV and the landing sign. Experiments show that the landing sign and recognition algorithm effectively improve the UAV landing reliability.

Modeling and control strategy of small unmanned helicopter rotation based on deep learning

Unmanned Aerial Vehicle (UAV) can serve as a substitute for workers in some hazardous environments, but the presence of ground effects makes UAVs prone to hardware damage during the recycling process. To study the rotation phenomenon of small UAV landing process and propose control strategy, the study completes the rotation modeling of small unmanned helicopter based on deep learning algorithm and proposes the control strategy. The results show that the CNN with ReLU function has the best performance, and the model converges in the 5th iteration with this function, while the model with Sigmoid function converges in the 36th iteration, and the fitting effect of the rotation model constructed by the study is higher than that of the traditional rotation model. The actual trajectory of the research-constructed rotation model starts to coincide with the expected trajectory at the 5th s of the landing process, while the actual trajectory of the Cheeseman-Bennett model starts to coincide with the expected trajectory only at the 26th s of the landing process. Under the control strategy model proposed in the study, the roll angle and pitch angle of UAV are stabilized at 46s, and the fluctuation of yaw angle is also minimal. The rotation model constructed in the study can completely reflect the rotation process of the small UAV, and the designed control system can help the UAV recover stability faster.

Aero-propulsive coupling modeling and dynamic stability analysis of distributed electric propulsion tandem-wing UAV with rapid ascent capability

The distributed electric propulsion tandem-wing unmanned aerial vehicle (UAV) offers higher aerodynamic efficiency than conventional electric vertical takeoff and landing UAV and superior rapid ascent takeoff and landing capabilities compared to regular fixed-wing aircraft. However, for this configuration, the aero-propulsive coupling effects between the propellers and wings are significant. To study the mutual interaction between distributed electric propulsion and aerodynamics on the flight dynamics characteristics and rapid ascent flight capability of UAV, an aero-propulsive coupling model incorporating the effects of propellers slipstream is established. Ground-based blown wing experiment is conducted to validate the modeling accuracy. Subsequently, a theoretical modeling framework based on standard stability derivatives is developed, incorporating the influence of thrust on the dynamics within stability derivatives. The dynamic characteristics of distributed electric propulsion tandem-wing configuration are then studied and aero-propulsive coupling effects on natural modes of motion and flight qualities are analyzed. Finally, flight simulations and actual flight tests are conducted to validate the rapid ascent flight capability of UAV in real-world. The experimental results indicate that UAV can achieve rapid ascent flight with significant pitch angles and high climb rates, showing promising results with more than a 91.9% improvement in takeoff performance compared to conventional rolling takeoff.

Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

Autonomous Landing of Unmanned Aerial Vehicles: Hybrid Metaheuristic Aided Detection and Extended Kalman Filter for Localization

Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms

Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on YOLOX is designed for airport runway localization. To meet the requirements of model accuracy and inference speed for the landing guidance system, regression loss functions, probability prediction loss functions, activation functions, and feature extraction networks are designed. Secondly, a deep-learning-based runway line detection algorithm including feature extraction, classification prediction and segmentation networks is designed. To create an effective detection network, we propose efficient loss function and network evaluation methods Finally, a visual/inertial navigation system is established based on constant deformation for visual localization. The relative positioning results are fused and optimized with Kalman filter algorithms. Simulation and flight experiments demonstrate that the proposed algorithm exhibits significant advantages in terms of localization accuracy, real-time performance, and generalization ability, and can provide accurate positioning information during UAV landing processes.

Aerodynamic modeling and performance analysis of model predictive controller for fixed wing vertical takeoff and landing unmanned aerial vehicle

This research focuses mainly on the aerodynamic modelling and performance analysis of a model predictive controller for a hybrid fixed-wing vertical takeoff and landing of unmanned aerial vehicle. The aerodynamics, which comprises several aerodynamic characteristics including the lift, drag, and thrust coefficients, is modelled using Newton’s second law of motion. The force and moment equations were obtained, and they were then converted into matrix and state equation form, together with the transformation matrices. The essential equations with six degrees of freedom (6 DoF) and 12 state matrices including the control input and manipulating variables were obtained. The proposed model predictive controller (MPC) was designed using the optimal model predictive controller design parameters, such as a sampling time of 0.1, a prediction horizon of 15, a control horizon of 3 and additional controller settings. With a settling period of 3 s, an overshoot of 0.5865, and a steady state inaccuracy of 0.00785, the MATLAB simulation demonstrates that the system variables, including roll, pitch, and yaw, are stabilized. This MPC control is more effective in anticipating and optimizing the UAV than other control strategies. Eventually, the controller’s simulation on MATLAB Simulink demonstrates the controller’s ability to stabilize and control the system in a real-time application. Autonomous vertical takeoff and landing operations depend heavily on the mathematical model and architecture of the flight controller. Among the uses are inspection, monitoring, and rescue.

Extended state observer-based continuous finite time control for a fixed-wing vertical take-off and landing unmanned aerial vehicle

Dual system Vertical Take-Off and Landing (VTOL) fixed-wing Unmanned Aerial Vehicles (UAVs) combine the advantages of both the rotorcraft and fixed-wing UAV, characterizing horizontal and vertical flight capabilities. However, they face challenges such as aerodynamic coupling between propulsion systems, model uncertainty, and external disturbances, significantly deteriorating the tracking accuracy and robustness. This paper proposes an Extended State Observer based Continuous Finite-Time Control (ESO-CFTC) scheme to solve the attitude and trajectory tracking problem of such a VTOL fixed-wing UAV. Initially, the Nonsingular Fast Terminal Sliding Mode (NFTSM) controller guarantees the tracking errors reach a prescribed manifold in finite time with a continuous reaching law, then converge to the origin in finite time while avoiding singularities. To suppress the inherent chattering problem of the sliding mode control, an ESO technique is incorporated into the ESO-CFTC scheme, which is completely decoupled with the basic NFTSM controller. The supremum of the estimation residual set is derivated theoretically. The scheme forces the tracking errors to converge into the neighborhood of the origin in finite time without requiring prior knowledge of lumped disturbance supremum values. Finally, the numerical simulation results in a Matlab/Simulink environment demonstrate that the presented method outperforms existing benchmark in terms of disturbance estimation, tracking accuracy, convergence rate, and robustness.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

APPLICATION OF TOPOLOGY OPTIMIZATION ON A 3D-PRINTED SHELF BRACKET

In this study, the topology optimization approach was adopted to reduce the material used in manufacturing. Specifically, the mass optimization technique was deemed suitable. Mass optimization eliminates the parts that don't affect a bracket’s overall strength while under load, resulting in weight reduction and material savings. Two shelf brackets were designed to test this theory and were subjected to mass optimization. A static structural analysis of this optimized model was carried out to confirm the optimization findings. These designs were then manufactured using the 3D-printing process. The yield points were next determined by performing a uniaxial tensile test on the shelf brackets. The outcome of the tests was subsequently compared with the simulation results, and a cost analysis model was created as an output. Ultimately, a reduction of 70% in mass was achieved with acceptable structural strength. In related optimization studies, the connecting part of an unmanned aerial vehicle's landing gear has been optimized resulting in fuel savings. The theory that topology optimization may be used to make both light and stiff parts at the same time has been proven by the results of this research as well as other studies that have been done on the same topic.

Reducing energy consumption of vertical take-off and landing unmanned aerial vehicle using hybrid technical solutions

Reducing energy consumption of vertical take-off and landing unmanned aerial vehicle using hybrid technical solutions

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.

Low-cost development of a fully composite fixed-wing hybrid VTOL UAV

Fixed-wing hybrid vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV) are popular due to their interoperability in the military and civilian domains, primarily where significant terrain difficulties exist for humans. Additionally, they can operate without requiring any runway infrastructure and have extended air endurance and efficiency. Since the hybrid UAV operates in distinct flight modes, viz., (a) VTOL and (b) fixed-wing cruise, carrying different payloads, the airframe structure requires careful design and manufacturing to realize sufficient strength. This experimental study aimed to identify the best combinations of various composite materials for manufacturing a lightweight, low-altitude long endurance (LALE) hybrid VTOL UAV. Primary materials include carbon fiber, Kevlar, fiber-reinforced plastic (FRP), resins, etc. Different rectangular test specimens of 120 × 5 mm size were made from ten different grades of carbon fiber, FRP, and resins using vacuum bagging. After properly curing these test specimens, we quantified their dynamic mechanical characteristics using various bending load experiments on a universal testing machine (UTM). An analysis of the experimental data facilitated the identification of the best composite combinations that provide maximum strength while reducing overall weight. Thus, we could understand the dynamic interplay between peak stress and test specimen weight. We also manufactured a UAV prototype using the identified combination and instrumented and flight-tested it to substantiate the experimental findings.

A semantic SLAM-based method for navigation and landing of UAVs in indoor environments

Autonomous navigation and landing of Unmanned aerial vehicles (UAVs) are critical functions towards full intelligence in unknown environments. However, current methods heavily rely on positioning devices or geometric maps, leading to two limitations: (1) UAVs fail to work normally indoors when signals are blocked, and (2) Navigation systems are not capable of achieving high-level decision-making missions. In this paper, we propose a novel and complete framework to realize the autonomous landing of UAVs in unknown indoor scenes based on visual SLAM, semantic segmentation, terrain estimation, and a decision-making model. Firstly, our 3D map is built on top of the visual SLAM system with semantic features, and then multiple types of maps (e.g., texture map, octo-map, grid-map, and semantic topology) are created for different function requirements. To achieve high-level scene understanding, we design a data association rule to fuse semantic features extracted by a deep learning model into the mapping process of topology structure. Next, a terrain estimation strategy is performed in the lightweight grid-map, which is modeled only via low-level elevation representation. Through multiple terrain constraints factor, a few optimized landing sites are selected in safe regions via clustering algorithm. Finally, based on the guidance of semantic topology, we construct a decision-making model for UAV landing that effectively integrates terrain safety, environment perception, and path planning. We perform extensive autonomous landing experiments on multiple indoor scenes from the TUM RGB-D dataset, and the full unknown environment maps are created simultaneously. The experiment proves that the proposed framework can select an optimal landing site for the advanced mission of UAVs effectively.

Design of air-ground cooperative landing platform and autonomous landing strategy

This paper studies innovative design for landing platform design and autonomous landing strategies for unmanned aerial vehicles (UAVs). First, a self-leveling landing platform on a mobile unmanned ground vehicle (UGV) is designed, which can be maintained even when the UGV moves on uneven terrain. Then, with the cooperation of the UAV and the UGV, they are controlled to reach the same velocity in the horizontal direction, which helps avoid the UAV’s rollover caused by horizontal velocity difference. Meanwhile, a buffering strategy is designed to reduce the vertical velocity difference, which helps avoid a hard landing. Finally, a combined landing code is designed to improve the accuracy and success rate of UAV landing. Experiments are carried out to demonstrate the effectiveness of the proposed design of a self-leveling platform and autonomous landing strategies.

SUITABLE LANDING SITE SELECTION FOR UNMANNED AERIAL VEHICLES USING AIRBORNE LASER SCANNING POINT CLOUD

Abstract. In the context of autonomous landing for unmanned aerial vehicles (UAVs), selecting a suitable landing site is crucial. This research presents a new method for automatically identifying safe landing sites based on point cloud data acquired through an airborne laser scanning (ALS) system. The proposed approach begins by detecting flat regions using principal component analysis (PCA) and region-growing algorithms. Subsequently, a terrain complexity assessment is conducted through plane fitting using an enhanced progressive sample consensus (PROSAC) algorithm. This assessment assists in identifying the most suitable landing site within a specified landing zone. The method's effectiveness is demonstrated through experiments conducted on two distinct natural terrains from the DALE dataset. The results show that the proposed approach can accurately classify landing zones and identify preferred sites that meet safety criteria. This study's findings underscore the proposed method's effectiveness and feasibility in improving the safety and reliability of autonomous UAV landings.

Real-time safe validation of autonomous landing in populated areas: from virtual environments to Robot-In-The-Loop

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful integration of UAVs in populated environments. Nonetheless, validating autonomous landing in real scenarios is a challenging task with a high risk of injuring people. In this work, we propose a framework for safe real-time and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments and physics-based simulation. The proposed evaluation pipeline includes the use of Unreal graphics engine coupled with AirSim for realistic drone simulation to evaluate landing strategies. Then, Software-/Hardware-In-The-Loop can be used to test beforehand the performance of the algorithms. The final validation stage consists in a Robot-In-The-Loop evaluation strategy where a real drone must perform autonomous landing maneuvers in real-time, with an avatar drone in a virtual environment mimicking its behavior, while the detection algorithms run in the virtual environment (virtual reality to the robot). This method determines the safe landing areas based on computer vision and convolutional neural networks to avoid colliding with people in static and dynamic scenarios. To test the robustness of the algorithms in adversary conditions, different urban-like environments were implemented, including moving agents and different weather conditions. We also propose different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works on this challenging task, and analyze them through several randomized iterations. The proposed approach allowed us to safely validate the autonomous landing strategies, providing an evaluation pipeline, and a benchmark for comparison. An extensive evaluation showed a 99% success rate in static scenarios and 87% in dynamic cases, demonstrating that the use of autonomous landing algorithms considerably prevents accidents involving humans, facilitating the integration of drones in human-populated spaces, which may help to unleash the full potential of drones in urban environments. Besides, this type of development helps to increase the safety of drone operations, which would advance drone flight regulations and allow their use in closer proximity to humans.

Enhanced robust adaptive flight control for a convertible VTOL UAV

In this paper, we propose less conservative formulations for candidate controllers of robust adaptive mixing control (RAMC) strategies. The RAMCs are employed to solve the trajectory tracking problem throughout the full flight envelope, with guaranteed stability, of a convertible plane (CP) vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Initially, the multi-body nonlinear dynamic model of the CP VTOL UAV is obtained using the Euler–Lagrange formalism, from which a linear parameter-varying (LPV) model is derived to be used in the design of the RAMC candidate controllers. The new formulations of the candidate controllers are proposed considering two approaches: a (i) Parallel Distributed Compensation (PDC); and (ii) a parameter-dependent Lyapunov function. Hardware-In-the-Loop (HIL) experiments are performed in a high-fidelity flight simulator to verify the fulfillment of real-time constraints, ensuring a computationally lightweight control strategy ready for implementation in a low-cost embedded computational system.

Investigation of Empennage Location Effect to the Unmanned Aerial Vehicle (UAV) Structure Characteristic

Over the past few decades, unmanned aerial vehicles, commonly known as UAVs, have been widely used in a number of consumer and military applications, such as surveillance, tracking, monitoring, and aerial photography. Fixed-wing and rotary UAVs are the two primary categories in UAV. Interestingly, the hybridization of fixed-wing and rotary UAV gives better performance in terms of energy consumption and the needs of runaway. Designing new hybrid fixed wing-rotary UAV or hybrid vertical take-off and landing (VTOL) is challenging especially to identify the critical location in the UAV and material selection. Therefore, the objective of this research is to study the effect of empennage location and material selection on the structural strength of a hybrid VTOL UAV. The SolidWorks software was employed to design a 3D model of the UAV with different empennage locations, as well as perform a simulation of the structural strength of fibre glass, carbon fibre and kenaf for the hybrid VTOL UAV. The simulation analysis presents stress (Von Mises). The results show that the fibre glass (4.342 N/m2) at top empennage gives the best performance as compared to other parameters. In conclusion, this study is necessary to give a better picture of structural strength of composite materials and best design location in hybrid VTOL UAV for future research.

GPS-Free Wireless Precise Positioning System for Automatic Flying and Landing Application of Shipborne Unmanned Aerial Vehicle.

This research is dedicated to developing an automatic landing system for shipborne unmanned aerial vehicles (UAVs) based on wireless precise positioning technology. The application scenario is practical for specific challenging and complex environmental conditions, such as the Global Positioning System (GPS) being disabled during wartime. The primary objective is to establish a precise and real-time dynamic wireless positioning system, ensuring that the UAV can autonomously land on the shipborne platform without relying on GPS. This work addresses several key aspects, including the implementation of an ultra-wideband (UWB) circuit module with a specific antenna design and RF front-end chip to enhance wireless signal reception. These modules play a crucial role in achieving accurate positioning, mitigating the limitations caused by GPS inaccuracy, thereby enhancing the overall performance and reception range of the system. Additionally, the study develops a wireless positioning algorithm to validate the effectiveness of automatic landing on the shipborne platform. The platform's wave vibration is considered to provide a realistic landing system for shipborne UAVs. The UWB modules are practically installed on the shipborne platform, and the UAV and the autonomous three-body vessel are tested simultaneously in the outdoor open water space to verify the functionality, precision, and adaptability of the proposed UAV landing system. Results demonstrate that the UAV can autonomously fly from 200 m, approach, and automatically land on the moving shipborne platform without relying on GPS.

Adaptive trajectory tracking control for VTOL UAVs with unknown time‐varying mass using an extended I&I estimator

SummaryThis article presents an adaptive control scheme for vertical takeoff and landing unmanned aerial vehicles subject to time‐varying mass based on the immersion and invariance scheme in an extended form. For the varying mass parameter in the position control, a generalized I&I adaptive law is proposed with higher dimension to estimate the mass and its derivatives. The stability of the resulting estimation error manifold, which is in a cascade form, is guaranteed by introducing smooth saturation functions in the position control law. The attitude control is designed by employing a smooth function with positive integrable time‐varying function to compensate unknown time‐varying moment of inertia to achieve asymptotic attitude tracking. Simulation and experimental results verify the effectiveness of the proposed method under different cases, and the robustness to time‐varying parameter is assured.