Search Results (20)

During the process of flexible aerial refueling, the flexible structure of the hose drogue assembly is affected by internal and external interference, such as docking maneuvering, deformation of the hose, attitude changes, and body vibrations, causing the hose to swing and the whipping phenomenon, which greatly limits the success rate and safety of aerial refueling operations. Based on a 2.4 m transonic wind tunnel, high-speed wind tunnel test technology of a flexible aerial refueling hose–drogue system was established to carry out experimental research on the coupling characteristics of aerodynamics and multi-body dynamics. Based on the aid of Videogrammetry Model Deformation (VMD), high-speed photography, dynamic balance, and other wind tunnel test technologies, the dynamic characteristics of the hose–drogue system in a high-speed airflow and during the approach of the receiver are obtained. Adopting flexible multi-body dynamics, a dynamic system of the tanker, hose, drogue, and receiver is modeled. The cable/beam model is based on an arbitrary Lagrange–Euler method, and the absolute node coordinate method is used to describe the deformation, movement, and length variation in the hose during both winding and unwinding. The aerodynamic forces of the tanker, receiver, hose, and drogue are modeled, reflecting the coupling influence of movement of the tanker and receiver, the deformation of the hose and drogue, and the aerodynamic forces on each other. The tests show that during the approach of the receiver (distance from 1000 mm to 20 mm), the sinking amount of the drogue increases by 31 mm; due to the offset of the receiver probe, the drogue moves sideways from the symmetric plane of the receiver. Meanwhile, the oscillation magnitude of the drogue increases (from 33 to 48 and from 48 to 80 in spanwise and longitudinal directions, respectively). The simulation results show that the shear force induced by the oscillation of the hose and the propagation velocity of both the longitudinal and shear waves are affected by the hose stiffness and Mach number. The results presented in this work can be of great reference to further increase the safety of aerial refueling. Full article

Aiming at the requirements of rapidity, high precision, and robustness for the longitudinal trajectory control of the receiver aircraft in autonomous aerial refueling, a direct lift control (DLC) strategy that integrates incremental nonlinear dynamic inversion (INDI) and nonlinear extended state observer (NESO) is proposed. First, a control strategy for generating direct lift through the coordinated action of the flaperons and elevators is presented, and a longitudinal dynamics model is established. Secondly, based on the INDI and DLC methods, the rapid tracking and control of altitude are achieved. Finally, an NESO is designed. The observer gains are designed through the pole placement method and the robust optimization method to achieve the estimation of states such as airspeed, angle of attack, pitch rate, and pitch angle, as well as unknown force and moment disturbances. The estimated force and moment disturbances are used to implement the active disturbance rejection control. Simulation results show that the strategy has no altitude tracking error under normal operating conditions, and the altitude tracking error is less than 0.2 m under typical disturbance conditions, indicating high control accuracy. Under disturbance conditions, the estimation errors of true airspeed, angle of attack, pitch angle, and pitch angular velocity are less than 0.3 m/s, 0.12°, 0.1°, and 0.2°/s, respectively, demonstrating the high-precision estimation capability of the observer. The NESO exhibits high accuracy in state estimation, the rudder deflection is smooth, and the anti-disturbance capability is significantly better than traditional methods, providing an engineered solution for the longitudinal control of the receiver aircraft. Full article

Autonomous aerial refueling (AAR) technology is of crucial importance in the aviation field. Accurately predicting the position of the refueling drogue is a core challenge in implementing this technology. An innovative method of a physics-informed neural network (PINN), a fusion of supervised learning and unsupervised learning, integrating physical information with an attention-augmented long short-term memory (AALSTM) neural network is proposed. By constructing a physical model of the refueling drogue, accurate physical constraints are provided for the prediction model. Meanwhile, an AALSTM neural network architecture is designed to predict partial states of the refueling drogue and parameters of the dynamic model. An attention-augmented mechanism is introduced to enhance the ability to capture key information. Simulation experiments verify that introducing an attention-augmented mechanism based on the conventional LSTM can improve prediction accuracy. The PINN significantly outperforms the conventional LSTM method in prediction accuracy, providing strong support for the development of AAR technology. Full article

The application of drones has contributed to automated herbicide spraying in the context of precision agriculture. Although drone technology is mature, the widespread application of agricultural drones and their numerous advantages still demand improvements in battery endurance during flight missions in agricultural operations. This issue has been addressed by optimizing the path planning to minimize the time of the route and, therefore, the energy consumption. In this direction, a novel framework for autonomous drone-based herbicide applications that integrates deep learning-based semantic segmentation and coverage path optimization is proposed. The methodology involves computer vision for path planning optimization. First, semantic segmentation is performed using a DeepLab v3+ convolutional neural network to identify and classify regions containing weeds based on aerial imagery. Then, a coverage path planning strategy is applied to generate efficient spray routes over each weed-infested area, represented as convex polygons, while accounting for the drone’s refueling constraints. The results demonstrate the effectiveness of the proposed approach for optimizing coverage paths in weed-infested sugarcane fields. By integrating semantic segmentation with clustering and path optimization techniques, it was possible to accurately localize weed patches and compute an efficient trajectory for UAV navigation. The GA-based solution to the Traveling Salesman Problem With Refueling (TSPWR) yielded a near-optimal visitation sequence that minimizes the energy demand. The total coverage path ensured complete inspection of the weed-infected areas, thereby enhancing operational efficiency. For the sugar crop considered in this contribution, the time to cover the area was reduced by 66.3% using the proposed approach because only the weed-infested area was considered for herbicide spraying. Validation of the proposed methodology using real-world agricultural datasets shows promising results in the context of precision agriculture to improve the efficiency of herbicide or fertilizer application in terms of herbicide waste reduction, lower operational costs, better crop health, and sustainability. Full article

This technology is highly suitable for detecting and tracking multi-skeletal targets in the field of aerial refueling. The paper presents a target feature responder based on K-means clustering, used for categorizing samples and training models. It employs a decision function to optimize the localization region across various classifier detection and tracking algorithms for targets. Additionally, a state judgment module scores the classifiers based on target state and depth information, which allows for adaptive selection of the classifier. To address the issue of missing target information, the method incorporates a stereo-vision-based mechanism to complete the localization region. This approach effectively handles challenges related to target appearance deformation, significant scale variations, motion blur, and occlusions. Full article

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions. Full article

Autonomous aerial refueling technology can significantly extend the operational endurance of unmanned aerial vehicles (UAVs), enhancing their ability to perform long-duration missions efficiently. In this paper, we address the identification of refueling drogues in the close docking phase of autonomous aerial refueling. We propose a high-precision real-time drogue recognition network called DREP-Net. The backbone of this network employs the DGST module for efficient feature extraction and improved representation of multi-scale information. For occlusion and complex background problems, we designed the RGConv module, which combines the re-parameterization module with the GhostNet idea to improve the detection of an occluded drogue. Meanwhile, we introduced the efficient local attention mechanism into the neck network to enhance the overall attention to the target region. Then, we designed Phead, a lightweight detection head that combines the advantages of decoupling and coupling heads to improve the detection speed. Finally, we compared our network with mainstream algorithms on a real drogue dataset, and the results show that DREP-Net has 2.7% higher mean average precision (mAP) compared to the YOLOv8n model, and the detection speed is improved by 31.4 frames per second. Full article

This work focuses on the use of load sensors to help with spraying tasks using unmanned aerial vehicles (UAVs). The study details the construction of a prototype for load measurement to validate the proof of concept. To simulate the application of agricultural pesticides, the UAV follows a predefined route and an image processing system detects the presence of diseased plants. After detection, the UAV pauses its route momentarily and activates the spraying device. The payload sensor monitors the fertilizer application process, which determines whether the amount of pesticide has been fully applied. If the storage tank is empty or the remaining quantity is insufficient for another operation, the system will command the UAV to return to the base station for refueling. Experimental validations were carried out in an indoor controlled environment to verify the proposal and the functionality of the in-flight payload monitoring system. Additionally, the UAV’s flight controller demonstrated robust performance, maintaining stability despite the challenges posed by liquid-load oscillations and varying payloads during the spraying process. In summary, our main contribution is a real-time payload monitoring system that monitors weight during flight to avoid over- or under-spraying. In addition, this system supports automatic refueling, detecting low levels of pesticides and directing the UAV to return to base when necessary. Full article

The accuracy of relative pose estimation is an important foundation for ensuring the safety and stability of autonomous aerial refueling (AAR) of unmanned aerial vehicles (UAV), and in response to this problem, a relative pose estimation method of UAVs based on the spatial equivalent plane is proposed in this paper. The UAV is equivalent to a spatial polygonal plane, and according to the measurement information of the Global Navigation Satellite System (GNSS) receivers, the equivalent polygonal plane equation is solved through the three-point normal vector and the minimum sum of squares of the distance from the four points to the plane. The equations of the distance between the geometric centers of the two polygonal planes, the angle between planes, and the angle between lines are used to calculate the relative pose information of the UAVs. Finally, the simulation environment and initial parameters are utilized for numerical simulation and results analysis. The simulation results show that without considering the motion model of the UAV, the proposed method can accurately estimate the relative pose information of the UAVs. In addition, in the presence of measurement errors, the relative pose estimation method based on the equivalent triangle plane can identify the position of the measurement point with the error, and the relative pose estimation method based on the equivalent quadrilateral plane has good robustness. The simulation results verify the feasibility and effectiveness of the proposed method. Full article

To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues. Full article

To study the hose-and-drogue system’s motion under bow waves, this paper established a dynamic model of the hose-and-drogue system based on the multibody dynamics theory and the rigid ball-and-rod model. The wake of a tanker aircraft was taken into account in the simulation. The simulation results conformed to the general laws and verified the model’s accuracy. The equilibrium positions of the hose-and-drogue system were computed by the linear superposition of the bow waves and wake. The motion of the hose-and-drogue system was simulated and analyzed when a receiver aircraft moved at a constant speed or accelerated relative to the tanker aircraft. Since the receiver aircraft would not immediately stop after docking, the pulling force changes on the hose with and without a reel were compared. The present results are essential for improving the success rate of aerial refueling and ensuring the safety and stability of the hose-and-drogue system. Full article

In this paper, a visual navigation method based on binocular vision and a deep learning approach is proposed to solve the navigation problem of the unmanned aerial vehicle autonomous aerial refueling docking process. First, to meet the requirements of high accuracy and high frame rate in aerial refueling tasks, this paper proposes a single-stage lightweight drogue detection model, which greatly increases the inference speed of binocular images by introducing image alignment and depth-separable convolution and improves the feature extraction capability and scale adaptation performance of the model by using an efficient attention mechanism (ECA) and adaptive spatial feature fusion method (ASFF). Second, this paper proposes a novel method for estimating the pose of the drogue by spatial geometric modeling using optical markers, and further improves the accuracy and robustness of the algorithm by using visual reprojection. Moreover, this paper constructs a visual navigation vision simulation and semi-physical simulation experiments for the autonomous aerial refueling task, and the experimental results show the following: (1) the proposed drogue detection model has high accuracy and real-time performance, with a mean average precision (mAP) of 98.23% and a detection speed of 41.11 FPS in the embedded module; (2) the position estimation error of the proposed visual navigation algorithm is less than ±0.1 m, and the attitude estimation error of the pitch and yaw angle is less than ±0.5°; and (3) through comparison experiments with the existing advanced methods, the positioning accuracy of this method is improved by 1.18% compared with the current advanced methods. Full article

In this work, the aeroelastic stability of an aerial refueling system is investigated. The system is formed by a classical hose and drogue, and the novelty of our work is the inclusion of a grid fin configuration to improve its stability. The unsteady aerodynamic forces on the grid fins are determined using the concept of a unit grid fin (UGF). For each UGF, the unsteady aerodynamic forces are computed using the Doublet-Lattice Method, and the forces on the complete grid fins are calculated using interfering factors obtained from wind tunnel measurements for the steady case. The static equilibrium position of the system influences the linearized perturbed unsteady motion of the ensemble. This effect, together with the phase lag angle introduced to account for the unsteady aerodynamic forces in the hose, makes the flutter computation of the complete system a non-typical one. The results show that, by adding the grid fins, the stability of the refueling system is improved, delaying or annulling flutter occurrence. Full article

As technologies advance and applications for uncrewed aircraft increase, the capability to conduct automated air-to-air refueling becomes increasingly important. This paper provides a review of required sensors to enable automated air-to-air refueling for an uncrewed aircraft, as well as a review of published research on the topic. Automated air-to-air refueling of uncrewed aircraft eliminates the need for ground infrastructure for intermediate refueling, as well as the need for on-site personnel. Automated air-to-air refueling potentially supports civilian applications such as weather monitoring, surveillance for wildfires, search and rescue, and emergency response, especially when airfields are not available due to natural disasters. For military applications, to enable the Air Wing of the Future to strike at the ranges required for the mission, both crewed and uncrewed aircraft must be capable of air-to-air refueling. To cover the sensors required to complete automated air-to-air refueling, a brief history of air-to-air refueling is presented, followed by a concept of employment for uncrewed aircraft refueling, and finally, a review of the sensors required to complete the different phases of automated air-to-air refueling. To complete uncrewed aircraft refueling, the uncrewed receiver aircraft must have the sensors required to establish communication, determine relative position, decrease separation to astern position, transition to computer vision, position keep during refueling, and separate from the tanker aircraft upon completion of refueling. This paper provides a review of the twelve sensors that would enable the uncrewed aircraft to complete the seven tasks required for automated air-to-air refueling. Full article

Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern in UAV cluster combat. This paper discusses the collaborative path planning problem of unmanned aerial vehicle formations (UAVFs) and refueling tankers in a URMFTG with threat areas and fuel constraints. The purpose of collaborative path planning is to ensure that the UAVFs (with fuel constraints) can complete the reconnaissance mission for the target group with the assistance of refueling tankers, which is one of the most important constraints in the collaborative path planning. In this paper, a collaborative path-planning model is designed to analyze the relationship between the planning path of the UAVFs and the tankers, and a threat avoidance strategy is designed considering the threat area. This paper proposes a two-stage solution algorithm. It creates a UAVFs path-planning algorithm based on the fast search genetic algorithm (FSGA) and a refueling tanker path-planning algorithm based on the improved non-dominated sorting genetic algorithm II (NSGA-II). Based on simulation experiments, the solution method proposed in this paper can provide a better path-planning scheme for a URMFTG. That is, it decreases the rate of the UAVF’s distance growth from 3.1% to 2.2% for the path planning of UAVFs and provides a better Pareto solution set for the path planning of refueling tankers. Full article