Land Vehicle Research Articles

Towards Planning Urban Air Mobility (UAM) Landing Trajectories in Emergencies

Ground transportation in dense urban environments has been facing challenges for many years (e.g., congestion and resilience) and the problem of congestion in urban environments will become more significant with the growth of populations and urbanization. In the past few years, the industry and the scientific communities have invested resources towards creating new ideas to improve urban transportation performance, such as the Urban Air Mobility (UAM) concept. Therefore, emergencies are considered a pivotal aspect to be managed appropriately to ensure safe UAM operations. Although normal operations represent a challenge nowadays (e.g., performance and social acceptance), emergencies are even more challenging due to the safety-critical risks. Thereupon, the main goal of this research is to propose the Landing Trajectory Planner for Emergencies in UAM Operations (LTPE), using Parallel Metaheuristics and considering autonomous vehicles’ presence. This trajectory planning method aims to design landing trajectories for multiple Electrical Vertical Take-off and Landing (eVTOL) vehicles in normal conditions and in emergencies. LTPE considers different eVTOL configurations in piloting systems (i.e., piloted vehicles, remotely piloted vehicles, and fully autonomous vehicles) as well as different vehicle priorities. Three landing modes are used for vehicles in emergency conditions: (i) land at the originally designed skyport; (ii) land at the nearest skyport; and (iii) land on the ground) and all metaheuristics include an early stopping feature. The experiments showed that LTPE can propose safe and efficient solutions for several scenarios with a short response time.

Piecewise linear value function approximations in nonlinear dynamic scheduling problems with VTOLs

Modern vertical take-off and landing vehicles (VTOLs) could significantly affect the future of mobility. The broad range of their application fields encompasses urban air mobility, transportation and logistics as well as reconnaissance and observation missions in a military context. This article presents an efficient numerical framework for dynamic scheduling of multiple VTOLs. It combines a scheduling problem with optimal trajectory planning. The whole setting is formulated as a mixed-integer bilevel optimization problem. At the upper level, VTOLs are scheduled, and their starting times are computed. The solution of the lower level problem involves the computation of a value function and yields optimal trajectories for every aerial vehicle. In order to solve the bilevel problem, it is recast into a single-level one. The resulting mixed-integer nonlinear program is then piecewise linearized and solved numerically by a mixed-integer linear solver based on the Branch-and-Bound algorithm. Numerical results prove the feasibility of the approach proposed herein.

Tempering temperature effect on the static and impact properties of 51CrV4 and 55Cr3 spring steels

Abstract This study investigated the impact of tempering temperature on the mechanical properties and microstructure of 51CrV4 and 55Cr3 spring steels, which are commonly utilized in the manufacturing of leaf springs. These springs are integral components of suspension systems in both land and railway vehicles. The steels were hardened through austenitizing at 870 °C for 30 min, followed by oil quenching. Subsequently, they were tempered at 300 °C, 375 °C, 450 °C, and 525 °C for 120 min. Mechanical testing (including hardness and tensile tests) and both micro and macrostructural analyses were carried out to assess the effect of tempering temperature. Additionally, impact tests at various temperatures were performed to evaluate the influence of tempering on ductile-to-brittle transition temperature (DBTT). X-ray diffraction was employed for phase analyses while scanning electron microscopy was utilized to examine fracture surfaces. The quenching/tempering processes resulted in strengths that were two to three times higher and lower hardening capacities below 0.05 in comparison to the normalizing treatment. The steels achieved their maximum ultimate tensile strengths above 1800 N/mm2 at 300 °C, with decreasing values as the tempering temperature increased. The hardness of 51CrV4 steel surpassed that of 55Cr3, attributed to the hardenability and precipitation-strengthening effects provided by the Cr and V content. Furthermore, impact energies increased with increasing testing temperatures (40 °C, 0 °C, 25 °C, and +80 °C), with both steels tempered at 525 °C exhibiting higher and satisfactory impact energies across all testing temperatures, exceeding 15 J and 20 J, respectively. In addition, the tempered steels did not show a well-defined DBTT, while only the DBTT of normalized 51CrV4 steel was clearly determined at 0°C.

Adaptive velocity control for UAV boat landing: A neural network and particle swarm optimization approach

Achieving precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms, such as Autonomous Surface Vehicles (ASVs), is challenging, particularly in GPS-denied environments with dynamic disturbances. Conventional methods often rely on high-level waypoint navigation, extensive manual tuning, and expensive sensors. In this work, we propose an adaptive Proportional-Integral-Derivative (PID) controller optimization using a Neural Network-Particle Swarm Optimization (NN-PSO) algorithm. The algorithm dynamically tunes the PID controller, significantly reducing manual tuning effort, while relying solely on a low-cost camera and altitude sensor. The NN-PSO algorithm allows the UAV to land with an average error of 5 cm on static platforms and 10 cm on moving boats, based on multiple test flights. Our method also increases the maximum landing speed to 80.9% of the UAV’s top flight speed, a considerable improvement over existing systems. Our approach not only optimizes landing precision but also introduces techniques for ensuring soft landings, reducing oscillations, and preventing target misses. These enhancements make the method robust across varying flight altitudes and ASV speeds. Furthermore, this approach is applicable to a variety of GPS-denied scenarios, including rescue missions, package deliveries, and workspace inspections, without requiring costly equipment or extensive parameter tuning. Field experiments confirm the precision and stability of the proposed system, validating its performance in real-world conditions. [Video]

Opening of the Skouw and Wutung Vanimo Cross-Border Border Crossing Land Road Crossing and Its Sustainability for the Pacific Region

This study aims to provide an in-depth analysis and clear data regarding the opening of land roads for land crossings, vehicles, goods and services, and people at the Northern Papua-Papua New Guinea border, specifically at the Skouw National Border Post connected to Vanimo, Papua New Guinea. The opening of this crossing lane has both positive and negative impacts on the crossing area. Therefore, it is crucial to conduct this study from a perspective beyond the Westphalian System, which focuses not only on maintaining national sovereignty but also on the unification of borders or a de-borderless approach. This perspective aims to advance the communities around the border of the two countries and provide significant benefits. The method used in this study is a qualitative research method, which explains the border phenomenon between the two countries with valid data obtained through primary and secondary sources, ensuring clear and accurate data. The results indicate that the opening of crossings for goods, services, and people at the Skouw-Vanimo border will enhance economic activities in both countries, increase socio-cultural interactions among border communities, strengthen bilateral relations, and establish the region as a hub for economic and trade activities with other Pacific countries. Moreover, it will reduce tensions among Pacific countries, promote strategic partnerships due to enhanced connectivity in the Pacific region, and even extend to the Indo-Pacific. This study highlights the potential benefits of a de-borderless approach to border management, emphasizing the need for cooperative development to maximize the positive impacts on the communities involved.

Sliding Mode Control Approach for Vision-Based High-Precision Unmanned Aerial Vehicle Landing System Under Disturbances

Unmanned aerial vehicles (UAVs) face significant challenges when landing on moving targets due to disturbances, such as wind, that affect landing precision. This study develops a system that leverages global navigation satellite system (GNSS) signals and UAV visual data to enable real-time precision landings, and incorporates a sliding mode controller (SMC) to mitigate external disturbances throughout the landing process. To this end, a reference-model-based SMC is proposed, which defines reference values for each state to enhance the steadiness and safety of the velocity control system, thereby improving velocity state tracking and accuracy. The stability of the proposed controller is demonstrated using the Lyapunov method and comparing its performance against other controllers, including backstepping, linear-quadratic regulator (LQR), and proportional–integral–derivative (PID). The experimental results reveal a 75% reduction in maximum velocity tracking error and an 80% reduction in maximum landing error with the proposed controller. Finally, extensive real-flight tests confirm the stability and feasibility of the system.

Neural Network-Based Descent Control for Landers with Sloshing and Mass Variation: A Cascade and Adaptive PID Strategy

Autonomous control of lunar landers is essential for successful space missions, where precision and efficiency are crucial. This study presents a novel control strategy that leverages proportional, integral, and derivative (PID) controllers to manage the altitude, attitude, and position of a lunar lander, considering time-varying mass and sloshing behavior. Additionally, neural network models are developed, to approximate the lander’s mass properties as they change during descent. The challenge lies in the significant mass variations due to fuel, oxidizer, and pressurant consumption, which affect the lander’s inertia and sloshing behavior and complicate control efforts. We have developed a control-oriented model incorporating these mass dynamics, employing multiple PID controllers to linearize the system and enhance control precision. Altitude is maintained by one PID controller, while two others adjust the thrust vector control (TVC) gimbal angles to manage pitch and roll, with a fourth controller governing yaw via a reaction control system (RCS). A cascade PD controller further manages position by feeding commands to the attitude controllers, ensuring the lander reaches its target location. The lander’s TVC mechanism, equipped with a spherical gimbal, provides thrust in the desired direction, with control angles α and β regulated by the PID controllers. To improve the model’s accuracy, we have introduced time delays caused by fluid dynamics and actuator response, modeled via computational fluid dynamics (CFD). Fluid sloshing effects are also simulated as external forces acting on the lander. The neural networks are trained using data derived from computer-aided design (CAD) simulations of the lander vehicle, specifically the inertia tensor and the center of mass (COM) based on the varying mass levels in the tanks. The trained neural networks (NNs) can then use lander tank levels and orientation to inform and accurately predict the lander’s COM and inertia tensor in real time during the mission. The implications of this research are significant for future lunar missions, offering enhanced safety and efficiency in vehicle descent and landing operations. Our approach allows for real-time estimation of the lander’s state and for precise execution of maneuvers, verified through complex numerical simulations of the descent, hover, and landing phases.

Noise assessment of multirotor configurations during landing proceduresa).

This study numerically investigates the noise impact of multirotor aerial vehicles with different rotor scales during landing procedures. The operational environments of individual rotors are influenced by rotational speed and wake dynamics, leading to variations in landing noise characteristics. Noise impacts are evaluated across various landing operations from both physical and psychoacoustic perspectives using noise source hemispheres and noise maps. The physical noise impact is quantified using sound exposure level (SEL), while the psychoacoustic impact is assessed through a psychoacoustic annoyance (PA) based on sound quality metrics. Performance contours are established to compare noise impacts alongside other factors, such as energy consumption, landing duration, vehicle attitudes, and safety considerations. The combined effect of noise source strength and landing duration determines SEL, while PA is primarily influenced by acoustic loudness, which follows a similar trend to noise source strength. Consequently, physical and psychoacoustic noise impacts exhibit distinct trends based on the landing operations. This study outlines a process for optimizing landing operations that meet predefined performance goals while minimizing noise impacts. Because operational performance varies significantly across different landing procedures and vehicle types, the study emphasizes the importance of incorporating comprehensive performance criteria in the design of landing operations.

Cycle slip detection and repair method towards multi-frequency BDS-3/INS tightly coupled integration in kinematic surveying

Carrier phase integer ambiguities must be determined for BDS-3/inertial navigation system (INS) tightly coupled (TC) integration to achieve centimetre-level positioning accuracy. However, cycle slip breaks the consistency of the integer ambiguities. Conventional multi-frequency cycle slip methods use the pseudorange; thus, requiring improvement when applied to kinematic situations. Furthermore, a concise and nonprior information-dependent model is crucial for real-time processing. In this study, an inertial-aided BDS-3 cycle slip detection and repair (I-CDR) method was developed. First, a BDS-3/INS TC model with I-CDR was created. The ionospheric delays were modelled as part of the TC states; therefore, they could be estimated and eliminated. Investigations were conducted on the effects of carrier phase noise, residual ionosphere delay, and INS-predicted position error on combined cycle slip detection (CCD) accuracy. The optimal CCDs under various frequency available configurations were determined. The effectiveness of I-CDR was demonstrated using land vehicle test data. The false alarm ratio was less than 1.0%, and the missed detection ratio was almost zero even in situations with challenging abundant 1-cycle slips in random epochs. Furthermore, the right determination ratio reached 100%. In addition, BDS-3 signal loss-recovery cases were simulated, and all cycle slips for all satellites could be repaired within 40s. I-CDR exhibits outstanding cycle slip detection and repair performance for dense 1-cycle slip and signal loss-recovery cases, demonstrating its suitability for BDS-3/INS TC integration.

Four-Dimensional Generalized AMS Optimization Considering Critical Engine Inoperative for an eVTOL

In this paper, we present a method to optimize the attainable moment set (AMS) to increase the control authority for electrical vertical take-off and landing vehicles (eVTOLs). As opposed to 3D AMSs for conventional airplanes, the hover control of eVTOLs requires vertical thrust produced by the powered lift system in addition to three moments. The limits of the moments and vertical thrust are coupled due to input saturation, and, as a result, the concept of the traditional AMS is extended to the 4D generalized moment set to account for this coupling effect. Given a required moment set (RMS) derived from system requirements, the optimization is formulated as a 4D convex polytope coverage problem, i.e., the AMS coverage over the RMS, such that the system’s available control authority is maximized to fulfill the prescribed requirements. The optimization accounts for not only nominal flight, but also for one critical engine inoperative situation. To test the method, it is applied to an eVTOL with eight rotors to optimize for the rotors’ orientation with respect to the body axis. The results indicate highly improved coverage of the RMS for both failure-free and one-engine-inoperative situations. Closed-loop simulation tests are performed for both optimal and non-optimal configurations to further validate the results.

Modeling the Impact of Additional Electrolyte on Lithium-Ion Convection Cell Performance

Addressing growing energy demands while balancing sustainability imperatives, environmental stewardship, and economic viability stands as a paramount global challenge. Electrochemical technologies, particularly lithium-ion batteries (LIBs), are pivotal in achieving deep decarbonization by facilitating the deployment of electric vehicles and ensuring the reliable delivery of renewable electricity. However, current LIBs fall short of the rigorous requirements for widespread adoption in these critical sectors, as well as in emerging areas such as long-haul trucking and electric vertical takeoff and landing vehicles (eVTOLs).1,2 Breakthroughs in the science and engineering of LIBs are imperative to produce devices capable of delivering high performance without sacrificing safety and cost.In addition to traditional materials-focused approaches, the development of new battery cell architectures, including the integration of electrolyte flow, presents a promising avenue for advancing performance. Prior mathematical modeling has shown forced electrolyte convection can significantly improve heat and mass transfer within LIBs, enabling operation under conditions that challenge conventional formats (e.g., high-rate charge/discharge).3,4 However, to exclude convoluting secondary effects, these studies simplify balance-of-plant systems which, in turn, may not fully describe practical operation. Specifically, these simulations use large electrolyte tanks which buffer against bulk concentration and temperature changes, aiding in the fundamental analyses. Notably, scaling under these conditions could potentially lead to cost-prohibitive and thus impractical systems.5 In this presentation, we investigate the impact of additional electrolyte volume on the complex interplay between electrochemical performance enhancements, fluid dynamic losses, and economic incentives in a convection cell. Through physics-based electrochemical modeling, we demonstrate that electrolyte convection alone can alleviate mass transport limitations even under extreme discharge rates, obviating the need for added electrolyte. In contrast, we find that extra electrolyte is necessary as a heat sink to mitigate cell temperature rise. We extend these results to practical operating conditions, where we conduct a preliminary system-level energy analysis to evaluate the energetic penalties associated with scaling this new architecture. Our findings suggest there exists a narrow added electrolyte range that effectively reduces thermal transport limitations without significantly increasing the system weight and volume. We also construct an accompanying technoeconomic framework to estimate the associated cost tradeoffs, illustrating the disparity between energy-optimized and cost-optimized designs. Ultimately, this work represents an initial exploration into practical design and operation considerations for convection batteries, laying the groundwork for future application-specific investigations.

Solid-State Architecture Batteries for Enhanced Rechargeability and Safety (SABERS) Beyond Li-Ion: Technology to Enable Next-Generation Sustainable Electric Aviation

All-electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The primary barrier to overcome is developing an energy storage system that meets rigorous aerospace safety and performance criteria. The performance metrics for eVTOL vehicles are at least two times greater than those of electric ground vehicles. Furthermore, inherently non-flammable batteries are essential for the safe operation of commercial electric aero vehicles. The SABERS concept proposes a battery that meets the critical performance criteria by developing a solid-state architecture battery utilizing a high-capacity sulfur-selenium cathode and lithium metal anode. The combination of sulfur and selenium offers a balanced energy-to-power density ratio, which can be tailored to the specific application by altering the stoichiometric ratios of sulfur to selenium. This hybrid cathode will be developed using NASA-patented holey graphene technology as a highly conductive, ultra-lightweight electrode scaffold. A solid-state electrolyte will be used as a safe, non-flammable replacement for the highly flammable liquid organic electrolytes currently in SOA lithium-ion batteries. This solid-state lithium-sulfur/selenium cell will be designed in a serial stacking configuration to enable the dense packaging of the battery cells. The serial stacking configuration is termed a bipolar stack, which has the advantages of reducing overall cell weight, simplifying the interfaced connections for the cell, and minimizing the cooling requirements. Lastly, the optimization of battery components will occur through a robust and rigorous combination of various computational modeling techniques covering multiple length scales. The expected result will be a fully solid-state battery with operational temperatures up to 150 °C, providing the required energy density, discharge rates, and inherent safety to meet the strict aerospace mission performance criteria. The SABERS team employed novel materials development, computational modeling, and design-of-experiments (DOE) optimization studies in their research. This presentation will show the results of these studies and demonstrate a feasible path for solid-state cells with a specific energy greater than 400 Wh/kg to enable electric aircraft. Figure 1

(Invited) Assessment of Extreme High-Power Demand for Evtol Li-Ion Batteries

Designing battery systems for electric vertical take-off and landing (eVTOL) platforms is challenging because they require high-power demands during crucial flight maneuvers. The eVTOL application needs exceptionally high discharge rates from the on-board lithium-ion batteries (LiBs) during several phases of its mission. However, LiBs have not been explored enough under high-power discharge scenarios, such as that of an eVTOL, because the current application spectrum in electric vehicles (EVs) and electronic devices does not require such high power.In a recent study, we simulated the initial take-off step of electric vertical takeoff and landing (eVTOL) vehicles powered by a lithium-ion battery subjected to an intense 15C discharge pulse at the beginning of the discharge cycle followed by a subsequent low-rate discharge. we conducted extensive electrochemical testing to assess the long-term stability of a lithium-ion battery under these high-strain conditions. The main finding is that despite the performance recovery observed at low rates, the re-application of high rates leads to drastic cell failure.These results highlight the eVTOL battery longevity challenge and emphasize the need for tailored battery chemistry designs for eVTOL applications to address both anode plating and cathode instability.

Design approach for tilt propellers of UAM/eVTOLs for cruise and hover considering aerodynamic and aeroacoustic characteristics via a multi-fidelity model

The design of quiet and efficient propellers/rotors is driven by the rapid development of Vertical Takeoff and Landing (VTOL) vehicles in urban areas. However, this task is challenging due to the expensive computational cost of propeller aerodynamic and aeroacoustic optimization based on accurate high-fidelity (HF) models. In contrast, many low-fidelity (LF) methods are less costly but also less accurate for complex propeller blade geometries, such as those involving sweep. This work aims to use a multi-fidelity (MF) surrogate model (SM) that combines a small HF data set for accuracy and a larger LF data set to speed up modeling. The MF approach inherits the accuracy of the HF method while retaining the computational efficiency of the LF model. The HF and LF aerodynamic data sets for training the MF model are obtained from a RANS solver and a meshless Large Eddy Simulation (LES) method, respectively. The obtained surface pressure of the blade is then used to calculate the noise signals radiating from the propeller using a Farassat's Formulation 1A (F1A) code. For practical design, we developed an optimization framework that combines these aerodynamic and aeroacoustic solvers, the MF model, design of experiment (DoE), and a multi-objective optimizer. The framework aims to optimize the blade aerodynamic shape in terms of chord, twist, and sweep distributions along the span from a baseline propeller, with objectives of efficiency and noise reduction under thrust constraints for both cruise and hover operating conditions. The obtained optimal designs, in the form of three-dimensional Pareto fronts, increased the aerodynamic efficiency by approximately 2% for cruise and 5% for hover and decreased the overall sound pressure level (OASPL) for hover by about 4 dB from the baseline propeller. The MF surrogate model, which utilizes both HF and LF data, doubled the optimization efficiency compared to the surrogate model based solely on HF data. Furthermore, the model based only on LF data fails to capture the three-dimensional flow effects induced by propeller sweep, which is crucial for reducing propeller noise. The aerodynamic benefits include reduced flow separation near the blade root during hover and more ideal loading distribution during cruise, while the aeroacoustic improvements are demonstrated through the “dephase” effect on noise signals emitted from different parts of the swept blade.

Coupling of Advanced Guidance and Robust Control for the Descent and Precise Landing of Reusable Launchers

This paper investigates the coupling of successive convex optimization guidance with robust structured H∞ control for the descent and precise landing of Reusable Launch Vehicles (RLVs). More particularly, this Guidance and Control (G&C) system is foreseen to be integrated into a nonlinear six-degree-of-freedom RLV controlled dynamics simulator which covers the aerodynamic and powered descent phase until vertical landing of a first-stage rocket equipped with a thrust vector control system and steerable planar fins. A cost function strategy analysis is performed to find out the most efficient one to be implemented in closed-loop with the robust control system and the vehicle flight mechanics involved. In addition, the controller synthesis via structured H∞ is thoroughly described. The latter are built at different points of the descent trajectory using Proportional-Integral-Derivative (PID)-like structures with feedback on the attitude angles, rates, and lateral body velocities. The architecture is verified through linear analyses as well as nonlinear cases with the aforementioned simulator, and the G&C approach is validated by comparing the performance and robustness with a baseline system in nominal conditions as well as in the presence of perturbations. The overall results show that the proposed G&C system represents a relevant candidate for realistic descent flight and precise landing phase for reusable launchers.

Distributed minimum error entropy with fiducial points Kalman filter for state tracking

With the growing size of the system, this distributed Kalman filter (DKF) is widely used in multi-sensor networks. However, it is difficult for DKF to accurately estimate state values in non-Gaussian noise environments. In this paper, a regression equation is first constructed to contain all sensor node information. Then, by bringing the minimum error entropy with fiducial points (MEEF) standard into the process of information fusion, a robust algorithm named centralized MEEF KF (CMEEF-KF) is presented, which is robust to non-Gaussian noise and unusual data. Furthermore, to overcome the communication burden of CMEEF-KF in sensor networks, the distributed MEEF-KF (DMEEF-KF) is developed, which construct a framework of consensus average method for node information fusion. Specifically, each sensor only exchanges the key information with its neighborhoods. In addition, in order to make the algorithm able to cope with the nonlinear state estimation problem, the distributed MEEF extended Kalman filter is also proposed. Eventually, the effectiveness of the suggested algorithms is demonstrated by land vehicle navigation and power system tracking state estimation using a 10-node sensor network.

Joint Optimization of the 3D Model and 6D Pose for Monocular Pose Estimation

The autonomous landing of unmanned aerial vehicles (UAVs) relies on a precise relative 6D pose between platforms. Existing model-based monocular pose estimation methods need an accurate 3D model of the target. They cannot handle the absence of an accurate 3D model. This paper adopts the multi-view geometry constraints within the monocular image sequence to solve the problem. And a novel approach to monocular pose estimation is introduced, which jointly optimizes the target’s 3D model and the relative 6D pose. We propose to represent the target’s 3D model using a set of sparse 3D landmarks. The 2D landmarks are detected in the input image by a trained neural network. Based on the 2D–3D correspondences, the initial pose estimation is obtained by solving the PnP problem. To achieve joint optimization, this paper builds the objective function based on the minimization of the reprojection error. And the correction values of the 3D landmarks and the 6D pose are parameters to be solved in the optimization problem. By solving the optimization problem, the joint optimization of the target’s 3D model and the 6D pose is realized. In addition, a sliding window combined with a keyframe extraction strategy is adopted to speed up the algorithm processing. Experimental results on synthetic and real image sequences show that the proposed method achieves real-time and online high-precision monocular pose estimation with the absence of an accurate 3D model via the joint optimization of the target’s 3D model and pose.

Transition flight of a concept of lifting-wing quadcopter

Abstract This paper presents the concept of a lifting-wing quadcopter unmanned aerial vehicle (UAV), a vertical take-off and landing vehicle (VTOL) with a rear wing, a canard at its front and four propellers. The aerodynamic surfaces are designed so that their mounting angle can be adjusted and fixed before flight, so its performance in transition flight can be studied for a combination of wing and canard mounting angles. A dynamic model using rigid-body equations of motion is presented, which is used to compute the transition flight trajectory from hover to cruise in horizontal flight. The trim conditions were computed for a range of fixed wing and canard mounting angles to study the effects of these variables on transition trajectory parameters such as required power, body pitch angle and propeller rotation speeds as a function of flight speed. Furthermore, a transition flight control algorithm is presented, which has a cascaded PID controller and a reference scheduler to switch between the proper reference states, controls and control allocation matrix. Finally, the transition control algorithm of the conceptual UAV is numerically simulated. Results show that this configuration can perform a fast and smooth transition from hover to cruise flight using the proposed flight control algorithm, substantially reducing required propulsive power in cruise of up to 64%. The application of the control algorithm made notable a transition manoeuver that consists of negatively inclining the aircraft at a negative pitch angle, initially at high intensity, and as the final cruising speed approaches, the inclination is attenuated until the equilibrium pitch angle is reached. Simultaneously with the negative inclination of the pitch angle, there is a slight drop in altitude, which is quickly resumed as the trajectory develops until the final cruising speed. Lastly, this aircraft configuration can be widely used in applications where performance gains in operations currently carried out by multicopters, which cover large distances and need long flight time, would bring great operational advantages.

Effects of Core and Surface Materials on the Flexural Behavior of Lightweight Composites Sandwich Beams

Sandwich composite elements are used in many sectors thanks to their low weight/strength ratios, high bending strength, good thermal insulation properties, and low costs. It is widely used in the machinery and construction industry, especially in land, sea, and air vehicles. The main objective of this research is to design and produce lightweight, durable, insulated, and low-cost, sustainable building elements that will meet emergency shelter needs after disasters. For housing purposes, 24 sandwich beams were prepared, eight designs with different surface coatings and core materials, and three in each design group. The effects of surface coating and core material on behavior were investigated with four-point bending experiments. Load-displacement relationships were determined from the experiments, and the beams' load-carrying capacities and failure patterns under the effects of bending and shearing were determined. In addition, theoretical methods determined maximum load values and compared them with the results of the experiments. As a result of the experiments, it was concluded that the best-performing design under bending effects was sandwich beams with plywood surface and XPS core.

YOLO-RWY: A Novel Runway Detection Model for Vision-Based Autonomous Landing of Fixed-Wing Unmanned Aerial Vehicles

In scenarios where global navigation satellite systems (GNSSs) and radio navigation systems are denied, vision-based autonomous landing (VAL) for fixed-wing unmanned aerial vehicles (UAVs) becomes essential. Accurate and real-time runway detection in VAL is vital for providing precise positional and orientational guidance. However, existing research faces significant challenges, including insufficient accuracy, inadequate real-time performance, poor robustness, and high susceptibility to disturbances. To address these challenges, this paper introduces a novel single-stage, anchor-free, and decoupled vision-based runway detection framework, referred to as YOLO-RWY. First, an enhanced data augmentation (EDA) module is incorporated to perform various augmentations, enriching image diversity, and introducing perturbations that improve generalization and safety. Second, a large separable kernel attention (LSKA) module is integrated into the backbone structure to provide a lightweight attention mechanism with a broad receptive field, enhancing feature representation. Third, the neck structure is reorganized as a bidirectional feature pyramid network (BiFPN) module with skip connections and attention allocation, enabling efficient multi-scale and across-stage feature fusion. Finally, the regression loss and task-aligned learning (TAL) assigner are optimized using efficient intersection over union (EIoU) to improve localization evaluation, resulting in faster and more accurate convergence. Comprehensive experiments demonstrate that YOLO-RWY achieves AP50:95 scores of 0.760, 0.611, and 0.413 on synthetic, real nominal, and real edge test sets of the landing approach runway detection (LARD) dataset, respectively. Deployment experiments on an edge device show that YOLO-RWY achieves an inference speed of 154.4 FPS under FP32 quantization with an image size of 640. The results indicate that the proposed YOLO-RWY model possesses strong generalization and real-time capabilities, enabling accurate runway detection in complex and challenging visual environments, and providing support for the onboard VAL systems of fixed-wing UAVs.