Search Results (287)

To effectively overcome changes in aircraft aerodynamic and control characteristics caused by wing surface damage, this paper proposes a fault-tolerant control method based on an extended state observer (ESO) to ensure flight mission requirements under wing surface and control surface failures. First, considering the characteristics and requirements of backstepping control in addressing nonlinear problems, an extended observer is designed to estimate disturbances and uncertainties induced by wing surface failures, and its stability is analyzed by using the Lyapunov method. Next, a backstepping control law for the airflow angle loop is designed based on the extended observer. The serial-chain method is introduced as an allocation algorithm for fault-tolerant flight control in order to compensate for the changes in control efficiency caused by wing surface faults. And stability analysis is conducted by integrating the control characteristics of the aircraft’s airflow angle loop, proving the uniformly bounded stability of the controller. Finally, fault-tolerant control simulations are performed under scenarios of wing damage, elevator damage, and actuator jamming faults. The simulation results demonstrate that the proposed method achieves excellent control performance during wing surface failures. Full article

The identification of gully-type debris flow remains a challenging task due to the irregularity of terrain, which causes significant fluctuations in local curvature and hinders accurate feature extraction using traditional methods. To address this issue, this study proposes a novel identification approach based on point cloud local curvature extrema. The methodology involves collecting image data of debris flow and landslide areas using DJI Matrice 300 RTK (M300RTK), planning control points and flight routes, and generating three-dimensional point cloud data through image matching and point cloud reconstruction techniques. A quadratic surface fitting method was employed to calculate the curvature of each point in the point cloud, while a topological k-neighborhood algorithm was introduced to establish spatial relationships and extract extreme curvature features. These features were subsequently used as inputs to a convolutional neural network (CNN) for landslide identification. Experimental results demonstrated that the CNN architecture used in this method achieved rapid convergence, with the loss value decreasing to 0.0032 (cross-entropy loss) during training, verifying the model’s effectiveness. The introduction of early stopping and learning rate decay strategies effectively prevented overfitting. Receiver-operating characteristic (ROC) curve analysis revealed that the proposed method achieved an area under the ROC curve (AUC) of 0.92, significantly outperforming comparative methods (0.78–0.85). Full article

To address the formation control issue of multiple unmanned aerial vehicles (UAVs), a finite-time control scheme based on terminal sliding mode (TSM) is investigated in this paper. A quadcopter UAV with the vertical takeoff property is considered, with cascaded kinematics composed of rotational and translational loops. To strengthen the application in the low-cost UAV system, the applied torque is synthesized with an auxiliary rotational system, which can avoid utilizing direct attitude measurement. Furthermore, a terminal sliding mode surface is established and employed in the finite-time formation control protocol (FTFCP) as the driven thrust of multiple UAVs over an undirected topology in the translational system. To maintain the safe flight of the UAV clusters in an environment to avoid collision with obstacles or with other UAV neighbors, a pigeon-hierarchy-inspired obstacle avoidance protocol (PHOAP) is proposed. By imitating the interactive hierarchy that exists among the homing pigeon flocks, the collision avoidance scheme is separately enhanced to generate the repulsive potential field for the leader maneuver target and the follower UAV cluster. Subsequently, the collision avoidance laws based on pigeon homing behavior are combined with the finite-time sliding mode formation protocol, and the applied torque is attached as a cascaded structure in the attitude loop to synthesize an obstacle avoidance cooperative control framework. Finally, simulation scenarios of multiple UAVs to reach a desired formation among obstacles is investigated, and the effectiveness of the proposed approach is validated. Full article

To improve the thermal protection effect of an opposing jet, a novel thermal protection technology (i.e., an opposing jet combined with magnetohydrodynamic (MHD) control technology) is proposed in this study. Considering the flight conditions of an ELECTRE vehicle and the unsteady state of the opposing jet, we employed the time-accurate nonequilibrium N-S equations coupled with a low-magnetic-Reynolds-number model to explore the jet characteristics, thermal protection effects, and aerodynamic drag characteristics of this novel technology. Two jet conditions (PR2.53 and PR5.07) and four magnetic field conditions (no-MHD, B₀ = 1 T, 2 T, and 4 T) were employed. The results show that the introduction of a magnetic field can guide the flow of the opposing jet by reconstructing the shock, where the reattachment shock is pushed away from the surface and the shock standoff distance (SSD) increases. Compared with the opposing jet and the MHD control technologies, this novel technology can provide a better thermal protection effect. In particular, it enables a long penetration mode (LPM) jet, which aggravates the aerodynamic heating environment around the vehicle at lower flow rates to provide effective thermal protection for the vehicle. Moreover, this novel technology can achieve effective thermal protection without increasing the aerodynamic drag at an appropriate jet mass flow rate and a magnetic field strength. For example, under the B₀ = 2 T magnetic field, the ratios of peak wall heat flux for the two technologies (the MHD control technology and the PR2.53 jet combined MHD control technology) are 0.908 and 0.820, respectively, whereas the ratios of average drags for the two technologies are 1.235 and 0.993, respectively. Full article

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) photogrammetry for surface modeling and Electric Resistivity Tomography (ERT) for subsurface deposit imaging. This strategy offers a cost-effective, time-efficient, and safer alternative to traditional surveying methods for challenging mountainous terrain. UAV methodology involved data collection using a DJI Mavic 2 Pro (20 MP camera) with 4 K resolution images captured at 221 m altitude and 80 min flight duration. Images were taken with 75% frontal and 70% side overlaps. The Structure from Motion (SfM) processing chain generated high-resolution outputs, including point clouds, Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and orthophotos. To ensure accuracy, five ground control points (GCPs) were established by a Real-Time Kinematic Global Navigation Satellite System (RTK GNSS). An ERT method known as vertical electric sounding (VES) revealed subsurface anomalies like solid rock mass, fractured zones and areas of iron leaching within marble deposits. Three Schlumberger (VES-1, 2, 3) and two parallel Wenner (VES-4, 5) arrays to a depth of 60 m were employed. The resistivity signature acquired by PASI RM1 was analyzed using 1D inversion technique software (ZondP1D). The integrated outputs of photogrammetry and subsurface imaging were used to design an optimized quarry with bench heights of 30 feet and widths of 50 feet, utilizing open-source 3D software (Blender, BIM, and InfraWorks). This integrated approach provides a comprehensive understanding of deposit surface and subsurface characteristics, facilitating optimized and sustainable quarry design and extraction. This research demonstrates the value of an innovative approach in synergistic integration of UAV photogrammetry and ERT, which are often used separately, for enhanced characterization, decision-making and promoting sustainable practices in dimensional stone deposits. Full article

Unmanned aerial vehicles (UAVs) play a key role in the process of contemporary environmental monitoring, enabling more frequent and detailed observations of various environmental parameters. With the rapid growth of scientific publications on this topic, it is important to identify the key trends and directions. This study uses the Top2Vec algorithm for topic modeling algorithm aimed at analyzing abstracts of more than 556 thousand scientific articles published on the arXiv platform from 2010 to 2023. The analysis was conducted in five key domains: air, water, and surface pollution monitoring; causes of pollution; and challenges in the use of UAVs. The research method included data collection and pre-processing, topic modeling, and quantitative analysis of publication activity using indicators of the rate (D1) and acceleration (D2) of change in the number of publications. The study allows concluding that the main challenge for the researchers is the task of processing data obtained in the course of monitoring. The second most important factor is the reduction in restrictions on the UAV flight duration. Among the causes of pollution, agricultural activities will be considered as a priority. Research in monitoring greenhouse gas emissions will be the most topical in air quality monitoring, while erosion and sedimentation—in the area of land surface control. Thermal pollution, microplastics, and chemical pollution are most relevant in the field of water quality control. On the other hand, the interest of the scientific community in topics related to soil pollution, particulate matter, sensor calibration, and volatile organic compounds is decreasing. Full article

This study examines the application of low-cost 1D LiDAR sensors in drone-based stockpile volume estimation, with a focus on indoor environments. Three approaches were experimentally investigated: (i) a multi-drone system equipped with static, downward-facing 1D LiDAR sensors combined with an adaptive formation control algorithm; (ii) a single drone with a static, downward-facing 1D LiDAR following a zigzag trajectory; and (iii) a single drone with an actuated 1D LiDAR in an oscillatory fashion to enhance scanning coverage while following a shorter trajectory. The adaptive formation control algorithm, newly developed in this study, synchronises the drones’ waypoint arrivals and facilitates smooth transitions between dynamic formation shapes. Real-world experiments conducted in a motion-tracking indoor facility confirmed the effectiveness of all three approaches in accurately completing scanning tasks, as per intended waypoints allocation. A trapezoidal prism stockpile was scanned, and the volume estimation accuracy of each approach was compared. The multi-drone system achieved an average volumetric error of 1.3%, similar to the single drone with a static sensor, but with less than half the flight time. Meanwhile, the actuated LiDAR system required shorter paths but experienced a higher volumetric error of 4.4%, primarily due to surface reconstruction outliers and common LiDAR bias when scanning at non-vertical angles. Full article

This research addresses minimum-fuel pinpoint lunar landing at the South Pole, focusing on trajectory design and near-optimal guidance aimed at driving a spacecraft from a circular low lunar orbit (LLO) to an instantaneous hovering state above the lunar surface. Orbit dynamics is propagated in a high-fidelity ephemeris-based framework, which employs spherical coordinates as the state variables and includes several harmonics of the selenopotential, as well as third-body gravitational perturbations due to the Earth and Sun. Minimum-fuel two-impulse descent transfers are identified using Lambert problem solutions as initial guesses, followed by refinement in the high-fidelity model, for a range of initial LLO inclinations. Then, a feedback Lambert-based impulsive guidance algorithm is designed and tested through a Monte Carlo campaign to assess the effectiveness under non-nominal conditions related to injection and actuation errors. Because the last braking maneuver is relatively large, a finite-thrust, locally flat, near-optimal guidance is introduced and applied. Simplified dynamics is assumed for the purpose of defining a minimum-time optimal control problem along the last thrust arc. This admits a closed-form solution, which is iteratively used until the desired instantaneous hovering condition is reached. The numerical results in non-nominal flight conditions testify to the effectiveness of the guidance approach at hand in terms of propellant consumption and precision at landing. Full article

A short takeoff and vertical landing unmanned aerial vehicle (STOVL UAV) is significantly influenced by factors such as the ship’s surface effect, deck motion, and jet effect during vertical landing on an aircraft carrier. The existing control logic cannot effectively solve the coupling problem of longitudinal attitude and trajectory, so it is hard to guarantee the stability and control accuracy of the UAV at low speed. To address the aforementioned interference and coupling problems, a comprehensive control law based on a radial basis function neural network (RBFNN) and nonlinear dynamic optimal allocation is designed in this paper. Firstly, the integrated landing control law of the STOVL UAV is designed. Considering the model uncertainty and complex landing environment, an RBFNN is used for online observation and compensation to improve the robustness of the system. Subsequently, a dynamic control allocation module based on nonlinear optimization is developed to simultaneously satisfy force and moment commands. The simulation results show that the integrated control method effectively decouples the pitch attitude and longitudinal trajectory at low speeds, resulting in effective convergence control of pitch angle, forward flight speed, and altitude. The integration of the RBFNN, as evaluated by the integral of absolute error (IAE), results in a 93% improvement in control accuracy compared to the integrated landing control law designed in this paper without the RBFNN integration. Full article

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging. Full article

Due to irregular hydrodynamic–aerodynamic coupling, the modeling and simulation of near-surface flight are extremely complex. For the present study, a practical dynamic model and a complete motion simulation method for the solution of such problems were established for engineering applications. A discrete non-linear time-varying dynamics model was employed in order to ensure the universality of the method; thereafter, force models—including gravity, aerodynamic, hydrodynamic, control, and thrust models—were established. It should be noted that a non-linear approach was adopted for the hydrodynamic model, which reflects the influences of waves in real-world situations; in addition, a Proportional–Integral–Derivative (PID) control law was added to realize closed-loop simulation of the motion. Considering a take-off flight as a study case, longitudinal three Degrees of Freedom (DoF) motion was simulated. The velocity, angle of attack, height, and angular velocity were selected as the state vectors in the state–space equations. The results show that, with the equilibrium state as the initial setting for the motion, reasonable time–history curves of the whole take-off phase can be obtained using the proposed approach. Furthermore, it is universally applicable for aircraft operating under hydrodynamic–aerodynamic coupling scenarios, including amphibious aircraft, seaplanes, Wing-in-Ground-Effect (WIGE) aircraft, and Hybrid Aerial–Underwater Vehicles (HAUVs). Full article

This paper describes the development of an ice creation model to be used within the framework of a model-based systems engineering approach to predict the amount of ice growing on aircraft wings during flight. This model supports the preliminary design of the ice protection system, as well as the implementation of a control system, in real-time. When the aircraft meets a high concentration of super-cooled water in the atmosphere and a low temperature, the risk of ice formation on its external surfaces is significant. This causes a decrease in aerodynamic performance, with potential loss of control of the aircraft. To mitigate this effect, ice prevention and protection systems are crucial. The characteristics of the icing phenomena are first defined, then their effects on aircraft behavior during operation are evaluated. This allows us to develop a highly parametric predictive model of the actual icing conditions experienced by the aircraft during a given flight mission. To precisely predict the ice accretion and to design an ice protection system, estimating heat fluxes involving the aircraft’s wing surfaces and the external environment is required. To allow for this, this study also develops a thermal model that is specifically applied to the above-mentioned analysis. This model includes many factors characterizing the atmospheric conditions responsible for ice creation upon the aerodynamic surfaces, and it enables an accurate estimation and quantification of all the parameters necessary to design an appropriate ice protection system. Full article

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ET_c), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ET_a followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (K_c) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ET_a (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m⁻³. Good correlations were found between the K_c’s proposed by FAO and the NDVI evolution in the experimental plot, with an R² of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ET_a values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day⁻¹ and a low bias of 0.6 mm day⁻¹. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine K_c, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings. Full article

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article