Search Results (370)

Reservoir simulation remains a major computational bottleneck for production optimization, history matching, and uncertainty quantification, particularly as geological models become increasingly detailed and recovery processes more complex. Coarse-grid network (CGNet) models have recently emerged as an efficient, physics-grounded proxy to full-physics simulations by solving the flow equations on a coarse network whose parameters are freely calibrated to reproduce fine-scale or observed well responses. In this study, we investigate how different coarse-partitioning strategies affect the accuracy and robustness of CGNet models. Four partitioning approaches are examined: a simple cookie-cutter partition, and three partitions based on cell-wise indicators—absolute permeability, velocity magnitude, and the product of forward and backward time-of-flight. Two test cases are considered: one using a single layer of the SPE10 benchmark dataset and the other using a sector model from the Norne field. Results show that, despite substantial differences in coarse-grid topology, the four CGNet models achieve comparable convergence behavior and predictive accuracy. For the SPE10 case, all models reproduce the fine-scale responses well, and no clear superiority among the partitioning methods. In the Norne case, the time-of-flight–based partition yields the lowest misfit and slightly better well-response predictions, although overall differences remain modest. These findings demonstrate that CGNet models are robust to coarse-grid topology and that incorporating flow-based indicators in partition generation can offer marginal improvements for complex geological systems. The results highlight the potential of CGNet as a cost-effective, physically consistent surrogate for large-scale reservoir applications. Full article

This research presents the first known example of a Thrust-Augmented Rocker Bogie (TARB). As a robust and passive mechanisms, the rocker bogie suspension system has seen widespread application in ground-based robotic planetary exploration rovers. However, with the first demonstration of a multirotor on Mars, there is clearly a need to expand the locomotion capacity for planetary rovers. The TARB builds on the existing flight heritage of the rocker rogie but also innovatively combines the system with a multirotor configuration. The combined homogeneous mobility solution can successfully demonstrate multimodal mobility including in terrestrial, aerial, and hybrid forms of locomotion. The prototype TARB developed for this research was constructed in the form of a CanBot. CanBots provide a means to test space-oriented rover technologies with earth-based analogues. Three prototype multimodal CanBots are described in this work, with each showing improvements in mobility and overall design robustness. Laboratory validation of the final TARB-equipped CanBot showed that it could utilize the rocker-bogie system to engage complicated terrestrial terrains while also maintaining the capacity to fly as an aerial vehicle. The laboratory testing also indicated that the CanBot could climb significantly steeper slopes when employing the TARB in a hybrid mode, successfully climbing slopes of 60 degrees, demonstrating static stability on inclines of up to 90 degrees, and successfully navigating along fully inverted surfaces. Full article

Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article

Unmanned Aerial Vehicles (UAVs) have become essential tools in inspection and emergency response operations due to their high maneuverability and ability to access hard-to-reach areas. However, their limited battery life significantly restricts their use in long-duration missions. This paper presents a tethered marsupial robotic system composed of a UAV and an Unmanned Ground Vehicle (UGV), specifically designed for autonomous, long-duration inspection tasks in Global Navigation Satellite System (GNSS)-denied environments. The system extends the UAV’s operational time by supplying power through a tether connected to high-capacity battery packs carried by the UGV. Our work details the hardware architecture based on off-the-shelf components to ensure replicability and describes our full-stack software framework used by the system, which is composed of open-source components and built upon the Robot Operating System (ROS). The proposed software architecture enables precise localization using a Direct LiDAR Localization (DLL) method and ensures safe path planning and coordinated trajectory tracking for the integrated UGV–tether–UAV system. We validate the system through three sets of field experiments involving (i) three manual flight endurance tests to estimate the operational duration, (ii) three experiments for validating the localization and the trajectory tracking systems, and (iii) three executions of an inspection mission to demonstrate autonomous inspection capabilities. The results of the experiments confirm the robustness and autonomy of the system in GNSS-denied environments. Finally, all experimental data have been made publicly available to support reproducibility and to serve as a common open dataset for benchmarking. Full article

As technology advances, small unmanned aerial vehicles (UAVs) are being engineered for increasingly versatile missions. The Multiple Environment Deployable Aerial Item Delivery (MEDAID) team, composed of 16 senior undergraduate aerospace engineering students, developed the XM-24 Orca as part of a capstone design project. This single-use UAV is designed to deliver medical supplies to soldiers in contested or remote environments. Capable of being ground or air-launched, the Orca incorporates spring-loaded swinging wings to meet a compact 610 mm stowed width requirement, a defining challenge in this project, allowing integration with existing drone platforms. The design effort was driven by key requirements: the ability to carry two 2.3 kg medical aid canisters, achieve a range of at least 370 km, sustain endurance for at least 4 h, and execute a dash speed of 51.4 m/s. This unique combination of mission requirements including airborne launch and wing deployment, extended range, and payload delivery necessitated an innovative design previously undocumented in the literature. The design was developed through rigorous computational analysis, refined through wind tunnel testing, and validated through a series of ground-based and flight tests. This paper documents unique design challenges and innovative solutions that offer guidance for future development efforts. Full article

In this research, two inflatable-winged Unmanned Aerial Vehicles (UAVs) in distinct configurations, a single-fuselage layout with external trailing-edge control surfaces and a twin-fuselage layout with fully movable control surfaces were designed, developed, and flight tested to investigate the flight characteristics of inflatable-winged aircraft. Initially, inflatable wings were designed and fabricated from various materials, followed by rigorous ground testing, including structural characteristics tests, pressure retention and resistance tests, and low-speed wind-tunnel evaluations. Following this, two methods for controlling the inflatable wings were proposed, and their roll control effectiveness was thoroughly investigated. Subsequently, two inflatable-winged UAV prototypes, each employing a different configuration and manipulation method, were designed, assembled, and subjected to basic low-altitude flight tests to assess the feasibility of their aerodynamic layouts and control characteristics. The results demonstrated that a segmented wing design with a multi-boom configuration is particularly well-suited for inflatable wings. Additionally, both proposed control methods were tested and shown to be effective in flight. The findings provide valuable insights into the properties of inflatable wings and offer substantial guidance for the development of inflatable-winged aircraft. Full article

This paper proposes a terminal area energy management (TAEM) guidance system using an online ground track predictor (GTP) for an uncrewed space vehicle (USV). Based on the current geometric range method for each separate phase, we establish a real-time range-to-go calculation method for generating reference commands online. The method ensures continuous range-to-go variation through status flags and an integrated range, thereby avoiding sudden command changes at subphase transitions, which may reduce longitudinal tracking stability. To enhance adaptability in an initial low-energy state, the system tracks the low-energy reference trajectory to provide an additional lift-to-drag margin, thus preventing an overly low terminal velocity. The results of numerical simulations with multiple uncertainties validate the proposed guidance strategy. Moreover, the flight test results confirm its ability to direct the USV to the target position with the desired energy state in real-world conditions. Full article

Artificial intelligence technology is profoundly reshaping the aviation field, driving the accelerated evolution of air confrontation patterns toward intelligence and autonomy. Given that experimental aircraft platforms are key means to verify intelligent air confrontation technologies, this paper—on the basis of systematically sorting out the progress of intelligent technologies in the air confrontation domain at home and abroad—first focuses on analyzing the connotation, technological evolution path, and application prospects of experimental aircraft platforms, and deeply interprets the technological breakthroughs and application practices of typical experimental platforms such as X-37B and X-62A in the field of artificial intelligence integration. Furthermore, through the analysis of three typical air confrontation projects, it reveals the four core advantages of experimental aircraft platforms in intelligent technology research: efficient iterative verification, risk reduction, promotion of capability emergence, and provision of flexible carriers. Finally, this paper focuses on constructing a technical implementation framework for the deep integration of intelligent technologies and flight tests, covering key links such as requirement analysis and environmental test design, construction of intelligent test aircraft platforms and capability generation, ground verification, and test evaluation, and summarizes various key technologies involved in the technical implementation framework. This study can provide theoretical support for the deep integration of artificial intelligence technology and the aviation field, including an engineering path from intelligent algorithm design, verification to iterative optimization, supporting the transformation of air confrontation patterns from “human-in-the-loop” to “autonomous gaming,” thereby enhancing the intelligence level and actual confrontation effectiveness in the aviation field. Full article

To achieve professional roe deer population management and to mitigate wildlife-related agricultural damage, a wildlife population estimation trial was conducted in Hungary using an ultralight aircraft with dual sensors (thermal and DSLR camera) to assess the method’s applicability, using the roe deer as a model species. The test took place in early spring, at an altitude of 400 m above ground level and a flight speed of 150 km/h. The survey targeted a total count of a 1040 hectare area using adjacent 200 m-wide strips. This strip-based design also allowed for a methodological comparison between total count and strip sample count approaches. Object-based image classification was applied, and species-level validation was performed. During the survey, a total of 213 roe deer were localised. The average group size was 9.17 ± 1.7 ($\overline{\mathrm{x}}$ ± SE), with two prominent outliers (28 and 34 individuals). Compared to the density value of 0.205 individuals/ha established through the full-area census, the simulated estimations (50% and 25%) showed considerable under- and overestimation, primarily due to the aggregative behaviour of roe deer. Based on the test, aerial population estimation using dual-sensor technology proved to be effective in agricultural habitats; however, the accuracy of the results is strongly influenced by the sampling design applied. Full article

The complex jet-ground interactions of Short Take-off and Vertical Landing (STOVL) aircraft are critical to flight safety and performance, yet studying them with traditional full-scale wind tunnel tests is prohibitively expensive and time-consuming, hindering design optimization. This study addresses this challenge by developing and numerically verifying a “pressure ratio–momentum–geometry” multi-dimensional similarity framework, enabling accurate and efficient scaled-model analysis. Systematic simulations of an F-35B-like configuration demonstrate the framework’s high fidelity. For a representative curved nozzle configuration (e.g., the F-35B three-bearing swivel duct nozzle, 3BSD), across scale factors ranging from 1:1 to 1:15, the plume deflection angle remains stable at 12° ± 1°. Concurrently, axial force (F) and mass flow rate (Q) strictly follow the square scaling relationship ($F\propto 1/n^2, Q\propto 1/n^2)$, with deviations from theory remaining below 0.15% and 0.58%, respectively, even at the 1:15 scale, confirming high-fidelity momentum similarity, particularly in the near-field flow direction. Second, a 1:13.25 scale aircraft model, constructed using Froude similarity principles, exhibits critical parameter agreement (intake total pressure and total temperature) of the prototype-including vertical axial force, lift fan mass flow, and intake total temperature—all less than 1.5%, while the critical intake total pressure error is only 2.2%. Fountain flow structures and ground temperature distributions show high consistency with the full-scale aircraft, validating the reliability of the proposed “pressure ratio–momentum–geometry” multi-dimensional similarity criterion. The framework developed herein has the potential to reduce wind tunnel testing costs and shorten development cycles, offering an efficient experimental strategy for STOVL aircraft research and development. Full article

Solar-electric propulsion offers a practical way to lengthen the endurance of small fixed-wing unmanned aerial vehicles while removing the noise, emissions, and upkeep that come with combustion engines. This work describes and tests a lightweight platform that couples a flexible thin-film photovoltaic array, a high-efficiency power-tracking controller, and a lithium–polymer battery to an electric brushless drivetrain. A ground-based flight emulator reproducing steady cruise allows continuous logging of the electrical flows between panel, battery, and motor. The results show that the solar subsystem can sustain most of the cruise demand, so the battery is called on only sparingly and is even able to recharge when sunlight is higher than a specific threshold. This balance translates into a clear endurance gain without upsetting the aircraft’s weight or handling. Full article

Real-time kinematic (RTK) unmanned aerial vehicles (UAVs) have become more popular in recent years, mostly because they can reduce the number of ground control points (GCPs) that have to be measured in the field and are required for aerial triangulation. Additionally, thanks to RTK technology, every image has its exterior orientation parameters measured with centimeter accuracy; thus, the block is more stable and there is a lower risk of some geometric distortions appearing within the block, especially in its central part. In this article, the influence of RTK observations on image orientation is analyzed based on a planned UAV test field in Józefosław, near Warsaw, Poland. As part of the experiment, UAV flights with DJI Phantom 4 RTK and DJI Phantom 4 Pro V2.0 were conducted, and 38 GCPs were located in the area. The results show that RTK observations from UAVs can significantly improve the accuracy of aerial triangulation, as inclusion of oblique images also does. For Phantom 4 RTK images, a single GCP was generally sufficient to achieve satisfactory accuracy, whereas six GCPs were required for the Phantom 4 Pro V2.0. Full article

Unmanned Aerial Vehicles (UAVs) have become increasingly valuable for accident scene reconstruction and forensic surveying due to their flexibility and ability to capture high-resolution imagery. This study investigates the impact of flight altitude, camera angle, and image overlap on the spatial accuracy of 3D models generated from UAV imagery. A total of 27 flight configurations were conducted using a DJI Phantom 4 Pro V2, combining three altitudes (30 m, 45 m, 60 m), three camera angles (90°, 75°, 60°), and three overlap levels (60%, 70%, 80%). The resulting 3D models were assessed by comparing measured linear distances between ground control points with known reference distances. The Root Mean Square Error (RMSE) was used to quantify model accuracy. The results indicated that lower flight altitudes, nadir or moderately oblique camera angles, and higher image overlaps consistently yielded the most accurate reconstructions. A Wilcoxon rank-sum test confirmed that the differences in accuracy across parameter settings were statistically significant. These findings highlight the critical role of flight configuration in achieving centimeter-level accuracy, as evidenced by RMSE values ranging from 1.7 to 7.6 cm, and provide practical recommendations for optimizing UAV missions in forensic and engineering applications. Full article

Contemporary multi-UAV formations face dual challenges of obstacle avoidance and rapid formation recovery. To enable UAV swarms to efficiently restore their predefined configurations post-obstacle navigation, a formation recovery strategy grounded in Nash equilibrium game theory is proposed in this paper. By integrating the IVY optimization algorithm, a collaborative control model that systematically balances individual UAV interests with swarm-level objectives through carefully designed optimization criteria is established. Comparative experimental results demonstrate that, compared to traditional formation obstacle-avoidance algorithms, Improved Particle Swarm Optimization (IPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA), our method exhibits superior performance across multiple key metrics, including average path length, formation accuracy rate, recovery time, and total time consumption. Real-flight tests on a multi-UAV platform confirm IVYA-Nash surpasses improved APF in formation accuracy and aerodynamic disturbance resistance, proving robustness in dynamic multi-agent scenarios. The work provides an efficient and reliable solution for coordinated control of UAV formations in complex environments. Full article

The volumetric Hirst method is considered a golden standard in aerobiology for determining particle number concentrations of bioaerosols. Using Hirst-type pollen and spore traps on mobile platforms (i.e., aircraft, cars, motorbikes, bicycles or carried by pedestrians) is anticipated to significantly enhance the spatial and temporal granularity of data for bioaerosol monitoring. Mobile sampling promises to enhance our understanding of bioaerosol dynamics, ecological interactions and the impact of human activities on airborne biological particles. In this article, we present the design and test of an airborne Hirst-type volumetric sampler. We followed a structured approach and incorporated the fundamental principles of the original design, while optimizing for size, weight, power and cost. Our portable Hirst-type volumetric sampler (FlyHirst) was attached to an ultralight aircraft, together with complementing instrumentation, and was tested for collection of atmospheric concentrations of pollen, fungal spores and hyphae. By linking the temporal resolution of the samples with the spatial position of the aircraft, using flight time, we calculated the spatial resolution of our measurements in 3D. In six summer flights over Denmark, our study revealed that the diversity of the recorded spores corresponded to the seasonal expectance. Urtica pollen was recorded up to 1300 m above ground (a.g.l.), and fungal spores up to 2100 m a.g.l. We suggest that, based on this proof-of-concept, FlyHirst can be applied on other mobile platforms or as a personal sampler. Full article