Autonomous helicopter shipboard landing requires the generation of dynamically feasible trajectories that can accurately match the time-varying motion of a moving deck while maintaining desirable near-deck geometric and kinematic characteristics. This paper proposes a differential-flatness-based trajectory planning method for single-phase direct shipboard landing from a cruising state in the vicinity of the ship. The landing trajectory is planned in the flat-output space of a simplified differentially flat helicopter model and parameterized using the minimum-control-effort trajectory representation, MINCO, together with an unconstrained optimization formulation. To improve the final landing process, a set of geometry-aware final-landing constraints is developed by explicitly accounting for the relative pose, relative motion, and near-deck geometry between the helicopter and the moving deck. These constraints enforce terminal motion-state matching and regulate the landing-gear/deck geometry, relative tangential velocity, and relative tilt angle in the near-deck region. Comprehensive simulation studies, including ablation and parameter-sensitivity analyses, computational comparisons, Monte Carlo evaluations, and closed-loop validation with a full nonlinear UH-60 model, demonstrate that the proposed method can efficiently generate trackable shipboard landing trajectories with improved terminal consistency and pre-touchdown geometric and kinematic regulation. Additional closed-loop sensitivity tests show that the tested trajectories remained executable under moderate high-level gain variations, while accurate terminal horizontal alignment remains dependent on deck-motion prediction accuracy.

Model predictive voltage control (MPVC) relies heavily on model accuracy and robustness of proportional-integral (PI) speed regulation, and though model predictive speed control (MPSC) eliminates the PI regulator, it requires weight-coefficient tuning and high computational complexity. Also, in sensorless drives, the widely used fourth-order extended Kalman filter (EKF) demonstrates limited robustness, though it is computationally demanding. Aimed at aforementioned problems, this article proposes a rectified model predictive speed and voltage control (RMPSVC) integrated with a reduced-order EKF for sensorless surface-mounted permanent magnet synchronous motors (SPMSMs). First, a model predictive speed-voltage control structure is developed, in which speed and voltage are both predicted, thereby eliminating the outer-loop PI regulator, avoiding joint speed-current prediction and weight-coefficient tuning as well. Then, an online model parameter rectification based on objective constraint optimization is incorporated to compensate for errors in stator inductance and rotor flux linkage, ensuring prediction accuracy under parameter variations. Finally, to enable the sensorless control target, a third-order EKF incorporating the torque-balance equation is constructed to guarantee real-time computation while maintaining estimation accuracy. Simulation and experimental results on a self-built platform based on an MPC5566 microcontroller unit (MCU) for a 0.4-kW SPMSM demonstrate that the proposed RMPSVC-EKF strategy achieves high dynamic performance, enhanced parameter robustness, and real-time performance.

The autonomous flight of helicopters has broad application potential. However, the inherent complexity of helicopter dynamics has long limited the development of trajectory generation and tracking techniques. This paper addresses differential-flatness-based method for the trajectory generation and tracking of helicopters, aiming to enhance the capability of the autonomous flight of helicopters. We propose a unified model simplification framework and derive the differential flatness transformation of the simplified model. Exploiting the flatness property, we develop an optimization-based trajectory generation method to produce smooth, dynamically feasible and high-quality trajectories. Additionally, we design a trajectory tracking controller integrating differential flatness with linear dynamic inversion to achieve precise trajectory tracking. Extensive simulations demonstrate the effectiveness of the proposed method.

This paper presents a parametric framework for the aerodynamic optimization of an electric tiltrotor blade. The framework integrates a custom blade element momentum (BEMT) based rotor performance calculation tool with built-in vortex-ring corrections and empirical adjustments, and is validated using high-fidelity computational fluid dynamics (CFD) simulations. Blade geometries are systematically generated using a conceptual modeling tool by varying key design parameters (such as rotor diameter, rotational speed, solidity, twist distribution, and airfoil family). Their effects on hover lift, cruise propulsive performance, and associated trade-offs are then analyzed. The optimized blade design achieves superior performance in both vertical lift and forward flight. Sensitivity analyses reveal that a moderate tip speed reduces induced power losses, aligning solidity with blade loading maximizes efficiency, and a tailored negative twist enhances hover capability with only minor penalties during cruise. The proposed methodology is scalable to various rotorcraft platforms and mission profiles, and can be extended through multidisciplinary optimization for fully integrated aircraft configurations.

Deep models have recently emerged as promising tools to solve partial differential equations (PDEs), known as neural PDE solvers. While neural solvers trained from either simulation data or physics-informed loss can solve PDEs reasonably well, they are mainly restricted to a few instances of PDEs, e.g. a certain equation with a limited set of coefficients. This limits their generalization to diverse PDEs, preventing them from being practical surrogate models of numerical solvers. In this paper, we present Unisolver, a novel Transformer model trained on diverse data and conditioned on diverse PDEs, aiming towards a universal neural PDE solver capable of solving a wide scope of PDEs. Instead of purely scaling up data and parameters, Unisolver stems from the theoretical analysis of the PDE-solving process. Inspired by the mathematical structure of PDEs that a PDE solution is fundamentally governed by a series of PDE components such as equation symbols and boundary conditions, we define a complete set of PDE components and flexibly embed them as domain-wise and point-wise deep conditions for Transformer PDE solvers. Integrating physical insights with recent Transformer advances, Unisolver achieves consistent state-of-the-art on three challenging large-scale benchmarks, showing impressive performance and generalizability. Code is available at https://github.com/thuml/Unisolver.

Transformers have empowered many milestones across various fields and have recently been applied to solve partial differential equations (PDEs). However, since PDEs are typically discretized into large-scale meshes with complex geometries, it is challenging for Transformers to capture intricate physical correlations directly from massive individual points. Going beyond superficial and unwieldy meshes, we present Transolver based on a more foundational idea, which is learning intrinsic physical states hidden behind discretized geometries. Specifically, we propose a new Physics-Attention to adaptively split the discretized domain into a series of learnable slices of flexible shapes, where mesh points under similar physical states will be ascribed to the same slice. By calculating attention to physics-aware tokens encoded from slices, Transovler can effectively capture intricate physical correlations under complex geometrics, which also empowers the solver with endogenetic geometry-general modeling capacity and can be efficiently computed in linear complexity. Transolver achieves consistent state-of-the-art with 22% relative gain across six standard benchmarks and also excels in large-scale industrial simulations, including car and airfoil designs. Code is available at https://github.com/thuml/Transolver.

With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions. However, facial expression recognition (FER) from NIR images presents a more challenging problem than traditional FER due to the limitations imposed by the data scale and the difficulty of extracting discriminative features from incomplete visible lighting contents. In this paper, we give the first attempt at deep NIR facial expression recognition and propose a novel method called near-infrared facial expression transformer (NFER-Former). Specifically, to make full use of the abundant label information in the field of VIS, we introduce a Self-Attention Orthogonal Decomposition mechanism that disentangles the expression information and spectrum information from the input image, so that the expression features can be extracted without the interference of spectrum variation. We also propose a Hypergraph-Guided Feature Embedding method that models some key facial behaviors and learns the structure of the complex correlations between them, thereby alleviating the interference of inter-class similarity. Additionally, we construct a large NIR-VIS Facial Expression dataset that includes 360 subjects to better validate the efficiency of NFER-Former. Extensive experiments and ablation studies show that NFER-Former significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

Despite significant progress in perception tasks such as 3D scene mapping and semantic information extraction using SLAM and deep learning, applying these techniques within computationally constrained embedded systems remains a challenge. In this work, we introduce a novel end-to-end framework for efficient and real-time volumetric-semantic mapping. We have developed a lightweight and robust RGB-D segmentation network for extracting semantic information. Through the introduction of three distinct modules-CFIM, DAPPF, and LAD-our network significantly enhances real-time performance while achieving Mean Intersection over Union (MIoU) scores comparable to state-of-the-art (SOTA) models. Our model reduces the parameters by 8 to 26 times compared to similar networks and improves inference speed by 2 to 3 times. Additionally, we improved a multi-class bayesian updating strategy by refining penalty function to reduce the memory size of the semantic map and enhance the mapping speed. Compared with other volumetric-semantic mapping approaches, our work maintains the same level of detail in semantic information representation, while increasing mapping speed by 1.3 to 9.6 times and reducing memory size of the map by up to 2.6 times. Finally, we applied our work to real-world mobile robot exploration scenarios, demonstrating the efficiency of the proposed framework.

Due to the susceptibility of depth and thermal images to environmental interferences, researchers began to combine three modalities for salient object detection (SOD). In this letter, we propose an efficient transformer network (ETFormer) based on multimodal hybrid fusion and representation learning for RGB-D-T SOD. First, unlike most works, we design a backbone to extract three modal information, and propose a multi-modal multi-head attention module (MMAM) for feature fusion, which improves network performance while reducing compute redundancy. Secondly, we reassembled a three-modal dataset called R-D-T ImageNet-1K to pretrain the network to solve the problem that other modalities are still using RGB modality during pretraining. Finally, through extensive experiments, our proposed method can combine the advantages of different modalities and achieve better performance compared to other existing methods.

Tiltrotor aircraft combine the vertical take-off and landing capability of a helicopter with the high cruising speed of a fixed-wing airplane. During flight, the aircraft switches between helicopter and airplane modes. Meanwhile, structural vibration and deformation are affected by changes in the aerodynamic loads, collective pitch, and rotation speed of blades. Therefore, it is crucial to consider dynamic characteristics in different modes. This paper presents a surrogate-based structural design framework of a tiltrotor blade to speed up calculations while maintaining accuracy. The shape generation method, rapid finite element method (FEM) model generation, and simplified boundary conditions are implemented to compute the blade modal frequencies, mass and inertia. A B-spline is used to construct the shape of the blade with the distribution of airfoils, twist angles, chord length, swept angles, and dihedral angles. These geometric features are realized by approximation, rotation, translation, and scaling of the control points. The spanwise distribution of the C-beam area and the width and length of the skins are selected to parameterize and establish the FEM model. The blind kriging model is applied to develop a surrogate model. The precision of this surrogate model is evaluated and compared to the kriging model and backpropagation neural network (BPNN) model based on the blade of an eight-ton-weight aircraft. A blade with a linear spanwise distribution of the C-beam area is designed using these three surrogate models trained by 13 samples. The results reveal that the error of the blind kriging model concerning the first four modal frequencies is less than 0.5% and lower than that of the other two models. A gimbal hub is modeled using a combination of a simply supported and a clamped boundary condition at the root. The two boundary conditions and two working conditions are unified into one working condition since a linear relationship is found. Moreover, the effects of the segmented linearly distributed cross-sectional area of the C-beam and the geometric parameters of the skins are investigated. The tip and root of the C-beam have the most significant influence on the low-order modal frequency. The parameters of the skins have opposite effects on the 1st and 2nd flap frequencies and the 1st torsion frequency. This framework provides a complete design process on the basis of geometric features and design parameters in the structure design, reducing the design variables. The use of the surrogate model and simplified working conditions reduce the consumption of time in the design. Conclusions about the tiltrotor blade have reference significance in the design stage.

Combining Global Navigation Satellite System (GNSS) with visual and inertial sensors can give smooth pose estimation without drifting. The fusion system gradually degrades to Visual-Inertial Odometry (VIO) with the number of satellites decreasing, which guarantees robust global navigation in GNSS unfriendly environments. In this letter, we propose an open-sourced invariant filter-based platform, InGVIO, to tightly fuse monocular/stereo visual-inertial measurements, along with raw data from GNSS. InGVIO gives highly competitive results in terms of computational load compared to current graph-based algorithms, meanwhile possessing the same or even better level of accuracy. Thanks to our proposed marginalization strategies, the baseline for triangulation is large although only a few cloned poses are kept. Moreover, we define the infinitesimal symmetries of the system and exploit the various structures of its symmetry group, being different from the total symmetries of the VIO case, which elegantly gives results for the pattern of degenerate motions and the structure of unobservable subspaces. We prove that the properly-chosen invariant error is still compatible with all possible symmetry group structures of InGVIO and has intrinsic consistency properties. Besides, InGVIO has strictly linear error propagation without linearization error. InGVIO is tested on both open datasets and our proposed fixed-wing datasets with variable levels of difficulty and various numbers of satellites. The latter datasets, to the best of our knowledge, are the first datasets open-sourced to the community on a fixed-wing aircraft with raw GNSS.

Visual-Inertial Odometry (VIO) is an approach to give high-accuracy pose estimation in GNSS(Global Navigation Satellite System)-denied environments. However, visual-inertial navigation slowly drifts on its four unobservable directions, namely the translations and yaw in the world frame. The visual and inertial sensors are unable to provide information about how the world frame is aligned with global geographic coordinates. In this letter, g-MSCKF, a Multi-State Constraint Kalman Filter based approach, is developed to combine visual, inertial and GNSS raw measurements. With GNSS raw measurements, g-MSCKF has the ability to use GNSS information even when the number of satellites is below 4. An alignment filter between the local world frame and the global geographic frame is proposed and serves as the initializer for g-MSCKF. Furthermore, unobservable directions may exist and vary when the number of satellites, the satellite-receiver spatial geometry and the receiver motion follow specific patterns. Inconsistency of the estimator may happen under those variable-unobservable circumstances and a variable observability constrained method is provided to avoid inconsistency. Our algorithm is evaluated on real-world open datasets where the sensors traverse indoors and outdoors. The results show that our solution gives globally smooth trajectories in GNSS-intermittent situations with full exploitation of sensor potentials.

In previous studies, helicopter brownout was investigated using various numerical methods, but the influence of a crosswind on the evolution of the dust cloud was not considered. In the present study, a method for analyzing helicopter brownout is developed that includes the effects of a crosswind on the unsteady flow field and the dynamics of the sand particles. The evolution of the dust cloud is simulated and compared with flight-test data obtained with no crosswind, and the differences in the dust clouds with and without a crosswind are investigated. Also, the influences of crosswind direction and velocity on the behavior of the dust cloud are analyzed. The results show that a crosswind has significant influence on the behavior of the dust cloud. A crosswind from the port side strengthens the dust cloud greatly, whereas a headwind weakens it. Compared with that when the crosswind is from the starboard side, the density of the dust cloud when the crosswind is from the port side is greater because the interaction between the crosswind and the rolled-up vortex on the retreating side is stronger than that on the advancing side. When the crosswind is from the port side, the density of the dust cloud increases initially and then decreases with increasing crosswind velocity.

Purpose The paper aims at developing a novel algorithm to estimate high-order derivatives of rotorcraft angular rates to break the contradiction between bandwidth and filtering performance because high-order derivatives of angular rates are crucial to rotorcraft control. Traditional causal estimation algorithms such as digital differential filtering or various tracking differentiators cannot balance phase-lead angle loss and high-frequency attenuation performance of the estimated differentials under the circumstance of strong vibration from the rotor system and the rather low update rate of angular rates. Design/methodology/approach The algorithm, capable of estimating angular rate derivatives to maximal second order, fuses multiple attitude signal sources through a first-proposed randomized angular motion maneuvering model independent of platform dynamics with observations generated by cascaded tracking differentiators. Findings The maneuvering flight test on 5-kg-level helicopter and the ferry flight test on 230-kg-level helicopter prove such algorithm is feasible to generate higher signal to noise ratio derivative estimation of angular rates than traditional differentiators in regular flight states with enough bandwidth for flight control. Research limitations/implications The decrease of update rate of input attitude signals will weaken the bandwidth performance of the algorithm and higher sampling rate setting is recommended. Practical implications Rotorcraft flight control researchers and engineers would benefit from the estimation method when implementing flight control laws requiring angular rate derivatives. Originality/value A purely kinematic randomized angular motion model for flight vehicle is first established, combining rigid-body Euler kinematics. Such fusion algorithm with observations generated by cascaded tracking differentiators to estimate angular rate derivatives is first proposed, realized and flight tested.

The Unsteady Vortex Lattice Method (UVLM) is a medium-fidelity aerodynamic tool that has been widely used in aeroelasticity and flight dynamics simulations. The most time-consuming step is the evaluation of the induced velocity. Supposing that the number of bound and wake lattices is N and the computational cost is O(N-2), we present an O(N) Dipole Panel Fast Multipole Method (DPFMM) for the rapid evaluation of the induced velocity in UVLM. The multipole expansion coefficients of a quadrilateral dipole panel have been derived in spherical coordinates, whose accuracy is the same as that of the Biot-Savart kernel at the same truncation degree P. Two methods (the loosening method and the shrinking method) are proposed and tested for space partitioning volumetric panels. Compared with FMM for vortex filaments (with three harmonics), DPFMM is approximately two times faster for N is an element of [10(3), 10(6)]. The simulation time of a multirotor (N similar to 10(4)) is reduced from 100 min (with unaccelerated direct solver) to 2 min (with DPFMM). (C) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd.

The aerodynamic performance of a reduced-scale coaxial rigid rotor system in hover and steady forward flights was experimentally investigated to gain insights into the effect of interference between upper and lower rotors and the influences of the advance ratio, shaft tilt angle and lift offset. The rotor system featured by 2 m-diameter, four-bladed upper and lower hingeless rotors and was installed in a coaxial rotor test rig. Experiments were conducted in the Φ3.2 m wind tunnel at China Aerodynamics Research and Development Center (CARDC). The rotor system was tested in hover states at collective pitches ranging from 0° to 13° and it was also tested in forward flights at advance ratios up to 0.6, with specific focus on the shaft tilt angle and lift offset sweeps. To ensure that the coaxial rotor was operating in a similar manner to that of the real flight, the torque difference was trimmed to zero in hover flight, whilst the constant lift coefficient was maintained in forward flight. An isolated single-rotor configuration test was also conducted with the same pitch angle setting in the coaxial rotor. The hover test results demonstrate that the figure of merit (FM) value of the lower rotor is lower than that of the upper rotor, and both are lower than that of the isolated single rotor. Moreover, the coaxial rotor configuration can contribute to better hover efficiency under the same blade loading coefficient (CT/σ). In forward flight, the effective lift-to-drag (L/De) ratio of the coaxial rigid rotor does not monotonously change as the advance ratio increases. Increases in the required power and drag in the case with a high advance ratio of 0.6 leads to the decreasing L/De ratio of the rotor. Meanwhile, the L/De ratio of the rotor is relatively high when the rotor shaft is tilted backward. The increasing lift offset tends to result in reduced required rotor power and an increase in the rotor drag. When the effect of the reduced rotor power is greater than that of the increased rotor drag, the L/De ratio increases as the lift offset increases. The L/De ratio can benefit significantly from lift offset at a high advance ratio, but it is much less influenced by lift offset at a low advance ratio. The forward performance efficiency of the upper rotor is poorer than that of the lower rotor, which is significantly different from the case in the hover flight.

Reverse pedal operational property in front crosswind flight condition is a potential hazard for accidents involving loss of tail rotor effectiveness (LTE), which is closely related to the main rotor (MR) wake interference on the tail rotor (TR). As understanding of this interaction is vital for the early warning strategy development, the MR wake influence effect on TR thrust and the effect of helicopter yaw stability are examined in this study. For this purpose, the comparison of TR thrust and flow field with wind azimuth and speed in front crosswind environment was performed by experiment and CFD simulation, respectively. Test campaign was performed at a 5.5 m × 4 m wind tunnel in the China Aerodynamics Research and Development Center using a high-position bottom-blade forward-rotating TR and a counterclockwise rotating MR to address the TR thrust under wind speeds of 8–22 m/s with 50°, 60°, and 70° wind azimuths. The influence of MR disc loading was also contrasted. CFD analysis was used to gain insight into the flow physics responsible for the interference effect. It was conducted with unsteady Reynolds-averaged Navier–Stokes simulations, where the MR using the actuator disk approach and the TR blade rotation was modeled via a sliding mesh method. Results indicated that the MR disc vortex has a remarkable interference effect on the TR aerodynamic performance characteristic and that the effect is sensitive to the wind speed, wind direction, and MR disc loading. The observed yaw instability is considered to be related to the lesser inflow introduced by the MR disc vortex due to the change in the relative position of the disc vortex filament and TR with the wind azimuth. The increase in TR thrust at moderate wind speeds is due to the increase in leading edge dynamic pressure caused by the opposite swirl direction of the disc vortex contrasted to the TR. The MR disc loading affects the TR thrust due to the change of disc vortex strength and position.

Unsteady aerodynamic interference between a rotorcraft and a ship occurs during shipboard launch and recovery operations and has a negative impact on the safety. An experiment of a reduced-scale model rotor and a CFD analysis in hover was carried out to investigate the performance and flow field of a rotor approaching a ship. In this paper, the thrust and pitching moment of the rotor hovering above the ground, deck were tested, and the influence of hangar door on the thrust, pitching moment, and flow field was also measured. A CFD method based on RANS and overset technology was used to investigate the flow field of the rotor operating on the model-scale ship. As the rotor approaches the deck, its thrust first decreases induced by a recirculation near the deck, and then increases induced by the effect of deck, and finally obviously decreases caused by the recirculation near hangar door. The deck and hangar door also affects the flow field to yield an intensive nose-down pitching moment. The status of the hangar door has a significant influence on the rotor thrust and pitching moment. The recirculation is weakened with an opened hangar door resulting in recovery of the rotor thrust and decrease of the nosed down pitching moment.

Main rotor actuator failure leads to catastrophic accidents for single main rotor helicopters. This paper focuses on safe landing trajectories after an actuator is locked in place by the remaining actuators, without introducing other control inputs. A general swashplate geometry is described, and new reconfiguration solutions for the control mixer are presented. The safe landing trajectories are obtained by formulating a nonlinear optimal control problem based on a nonlinear helicopter dynamic model and geometry constraints due to actuator failure. Safe landing trajectory results are shown with various initial forward velocities of all actuator failure cases. The safe initial speed boundaries are also explored by employing speed sweeps.

Helicopter autorotation trajectory planning problems have been dealt within computationally expensive optimal control algorithms. This paper presents an efficient helicopter autorotation trajectory planning method, using functional tensor-train- (FT-) based dynamic programming (DP) algorithms. The autorotation trajectory planning method is shown real-time feasible, which involves general helicopter autorotation dynamics at the same time. To validate the dynamic feasibility of the trajectories, a trajectory-tracking controller using active disturbance rejection control (ADRC) is designed to ensure a helicopter model tracks the trajectories. Finally, a helicopter autorotation simulation with a six-degree-of-freedom high-fidelity multibody-based helicopter model is demonstrated for validation.