Search Results (70)

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

Roof instability in the heading area of fully mechanized excavation roadways, together with insufficient coordinated operation between excavation and support, severely restricts tunneling safety and construction efficiency. A novel track-mounted advanced support equipment structure with an articulated curved roof beam is proposed in this study. Considering actual underground working conditions, including uneven roof contact, eccentric loading and local support failure, a three-degree-of-freedom dynamic model covering vertical, pitch and roll motions is established based on Lagrange’s equations. Dynamic characteristics under varying load amplitudes, excitation frequencies, static load offsets and typical support failure modes are systematically analyzed. The results reveal that only vertical vibration emerges under the full support condition, and the resonance frequency of the system is approximately 10 Hz. The maximum steady-state vertical displacement reaches 0.6406 mm with an RMS of 0.5472 mm under an intact support state. The pitch vibration amplitude caused by the failure of the first support group is three times that of the second group, proving front supports dominate anti-overturning capacity. Side beam failure triggers remarkable roll-coupled vibration, while middle beam failure mainly enlarges vertical displacement. This paper clarifies the vertical–pitch–roll coupling vibration mechanism induced by local support failure. Parameter sensitivity analysis reveals that static load offset has the highest sensitivity, while excitation frequency (within 4–6 Hz) and damping ratio exhibit negligible influence on the steady-state response. The obtained quantitative results can provide a reliable theoretical reference for structural optimization, stability regulation and safety monitoring of track-mounted advanced support facilities. Full article

This paper presents a novel approach to design an attitude motion control strategy of a vehicle to mitigate lateral or longitudinal inertial forces acting on the passenger during cornering, braking, and acceleration maneuvers. The collaboration of active suspension system and active airfoil substantially enhances the attitude motion of a vehicle. By incorporating integral control action for both the desired body attitude roll or pitch angle and zero dynamic tire deflection within the performance index, the optimal controller maintains the ideal roll or pitch angle while preserving the road holding capability. The computer simulations were conducted to evaluate the dynamic performance of the proposed system in comparison with various other suspension systems based on a 4-degree-of-freedom half-car model. Four scenarios for rolling and pitching motions were simulated as follows: the first case examines the rolling response to a one-sided bump input applied to a lateral half-car model during straight-line driving. The second case investigates the rolling performance during a cornering maneuver. The third and fourth cases analyze the pitching responses to braking and acceleration using a longitudinal half-car model. The simulation results demonstrate that the proposed system maintains the ideal body attitude, attenuates the effect of the lateral or longitudinal inertial forces and keeps an ideal road holding capability. As a result, the proposed control system substantially improves ride comfort while enhancing the dynamic safety of the vehicle. Full article

Increasing the efficiency of the railway industry requires the creation of solutions aimed at improving the technical, economic, and operational performance of wagons. It would contribute to reducing the cost of their operation. One of the most damaged elements of wagon bodies is their paneling. Its damage not only affects the loss of cargo during transportation but also threatens the safety of the movement of goods. The article is aimed at the load analysis of the body of an open wagon, whose paneling is sandwiched with corrugated panels. This solution will improve the strength of the side walls of the body of the solved freight wagon. This hypothesis has been accepted based on the dynamic load as well as the strength calculated for the body of the solved freight wagon. The dynamic load of the open wagon body has been studied with a mathematical model of its oscillations during the lateral roll. The solution to this model has shown that the maximal values of accelerations are lower by 5% than those acting on the standard design, and they act on the body of the solved freight wagon. The values of accelerations, which act on the body of the solved freight wagon, were calculated by means of numerical simulations using the finite element method implemented in the SolidWorks Simulation software. The discrepancy between the results of mathematical modeling and computer modeling is 6.5%. The strength of the open wagon body has also been calculated. It has been found that the maximal values of stresses in the paneling were lower by 17% than those acting in a standard body structure and 12% lower than the stresses in the body with corrugated panels. The study has also included an analysis of the modal properties of the body of the solved freight wagon, which demonstrates that the safety of the open wagon in motion is observed. The studies conducted will be useful in developing proposals for the creation of the newest wagon designs, including the improved economic, operational, and technical characteristics. Full article

As robotics technology advances, quadruped robots have become capable of operating in complex environments with varying elevation, including ramps and level changes that are challenging for conventional wheeled platforms. While this terrain adaptability opens new opportunities for inspection, rescue, and exploration tasks, the repetitive impacts, frequent ground-contact transitions, and abrupt postural changes inherent to legged locomotion pose significant challenges for LiDAR odometry. High-frequency gait vibrations and abrupt attitude changes introduce intra-scan motion distortion that conventional single-twist deskewing cannot adequately suppress. In addition, sparse vertical geometric constraints in elevation-varying environments weaken Z-axis observability, allowing vertical drift to corrupt the horizontal pose estimate through Hessian coupling. To address these failure modes within a LiDAR-only framework, we propose a Piecewise-Constant Velocity deskewing scheme that partitions each scan into multiple temporal segments with safety clamping on vertical and attitude components, together with a two-stage ICP that decouples SE(3) optimization into horizontal (x, y, yaw) and vertical (z, roll, pitch) stages and applies observability-aware weighting in the vertical update. The proposed odometry front-end is evaluated on four real-world sequences collected with a Unitree Go2 quadruped robot equipped with a Velodyne VLP-16 LiDAR. Experimental results show consistently lower Absolute Pose Error (APE) than ICP, KISS-ICP, and F-LOAM across all sequences. Vertical drift suppression is most pronounced in the ramp-containing sequences, where baseline methods exhibit substantial Z-axis divergence. Full article

As a core piece of equipment for marine monitoring, search and rescue missions, and other applications, the motion state prediction accuracy of Unmanned Surface Vehicles (USVs) directly determines mission reliability and safety. However, existing methods fail to fully consider the motion characteristic differences in various vessel sizes and variable-speed navigation under complex sea conditions, and struggle to capture the spatiotemporal dynamic features of state variations. This paper proposes a hybrid prediction algorithm based on Random Forest-Long Short-Term Memory (RF-LSTM), which utilizes Random Forest for key feature selection while employing LSTM to excavate temporal correlations. An intelligent routing mechanism based on the dominant frequency energy ratio (P_d) is introduced to achieve adaptive prediction mode switching, enabling comprehensive characterization of state variations. Under the 20 kn high-speed condition of a 7.5 m USV, the proposed algorithm achieves a Circular RMSE for heading prediction that is 1.9 times lower than the Extended Kalman Filter (EKF) and 1.2 times lower than a standalone LSTM, with pitch and roll prediction RMSE reduced to 0.36° and 0.85°, respectively. On a 14.5 m-long USV at 23 kn, it maintains a heading prediction accuracy of 0.10°, verifying favorable scale generalization capability. Furthermore, the algorithm demonstrates strong robustness against Gaussian white noise and synthetic ocean noise. Experimental results indicate that RF-LSTM significantly outperforms traditional methods, effectively breaking through the application limitations of fixed-architecture models, substantially enhancing USV autonomy and adaptability in complex marine environments, and providing robust guarantees for mission reliability and safety. Full article

Fall-from-bed is a critical safety issue in hospitals and long-term care; however, large-scale real-fall data are rare, and collecting the data is ethically constrained. This study examined whether the fall-from-bed risk can be inferred from single static in-bed postures without temporal motion. We developed a physics-based bed–human simulator in MuJoCo and generated labeled episodes by sampling diverse initial configurations, rolling out uncontrolled dynamics for three seconds, and detecting falls by floor contact. Each initial state was represented as a 13-keypoint 2D skeleton in a bed-centric coordinate frame, normalized to fixed bed bounds, and supervised with a continuous risk label derived from time-to-fall using per-frame discounting on a 30 frame-per-second grid. On a pose-balanced simulated test set of 50,000 initial postures, the best-performing multilayer-perceptron-based predictor attained an area under the receiver operating characteristic curve of 0.9755, area under the precision–recall curve of 0.9771, F1-score of 0.9138, and mean squared error of 0.0374 (mean over five random seeds). Pose-stratified initialization improved performance relative to fully random sampling. Consistently high performance was observed across supine/prone/lateral subgroups, which improved with training set size. These results suggest that a static posture contains predictive information about fall risk under matched simulator dynamics, supporting the feasibility of posture-based risk scoring in the controlled settings. Full article

An accurate prediction of the short-term motion attitude of ships is essential for navigation safety and offshore operations. However, conventional time series prediction models have constraints in handling time-varying dynamics and adapting to diverse sea states. Therefore, Ship Motion Attitude Prediction Network (SMAPNet) based on Non-Symmetric Tri-Cube Kernel Trend Filter (NTKTF) is proposed in the present paper. SMAPNet decomposes temporal signals using the Feature Extraction Block (FEB), fuses local and global features through Feature Refinement Block (FRB), and integrates Bidirectional Long Short-Term Memory Network (Bi-LSTM) with a self-attention mechanism, Feature Prediction Block (FPB), for short-term prediction within 1 to 5 s. In this experiment, field-measured data from the ship XIN HONG ZHUAN were employed to construct online prediction scenarios, and a systematic evaluation was conducted from three perspectives: local prediction accuracy, evaluation metric, and error distribution. The findings indicate that SMAPNet exhibits improved adaptability and prediction accuracy in predicting ship motion attitudes under different sea states. Specifically, in the single-step prediction of roll and pitch under sea states 3 and 4, the mean square errors (MSE) of SMAPNet are reduced by 10.45%, 6.96% and 14.60%, 2.77% respectively compared with the superior candidate model. Full article

As floating offshore wind turbines (FOWTs) scale towards 10 MW+ capacities, suppressing wave-induced rotational resonance becomes critical for system survivability. This study introduces an optimized, highly symmetrical four-float semi-submersible platform, explicitly tailored to support the DTU 10 MW wind turbine and paired with an orthogonal four-point mooring system. Using three-dimensional linear potential flow theory via ANSYS AQWA, comprehensive frequency- and time-domain hydrodynamic evaluations were conducted. To address the inherent limitations of inviscid potential flow assumptions, an empirical added-damping method was implemented. Quantitative results demonstrate a drastic reduction in motion responses: the peak Response Amplitude Operator (RAO) for heave decreased by 68.6% (from 1.945 m/m to 0.610 m/m). Most notably, the peak RAOs for the critical rotational degrees of freedom—pitch and roll—were reduced by over 92% (from 2.080 °/m and 2.216 °/m to ~0.168 °/m, respectively). Ultimately, compared to traditional asymmetric three-float concepts, this novel symmetric omnidirectional layout provides a more uniform restoring stiffness. The resulting suppression of pitch and roll resonance results in a profound reduction in tower-base bending moments and gyroscopic loads, thereby significantly enhancing the dynamic stability, safety margins, and fatigue life of the 10 MW FOWT under extreme survival sea states. Full article

This paper studies the dynamic behavior of a catamaran hovercraft wind farm service vessel (CHWFSV) during the berthing coupling process with a wind turbine tower, aiming to enhance its safety and reliability in engineering applications. By constructing an arc-shaped elastic fender and employing computational fluid dynamics (CFD), it investigates the motion response under transverse waves considering the effects of thrust, air-cushion flow and the elasticity coefficient of the fender. A finite element analysis (FEA) model of the arc-shaped fender, accounting for elastic stress and strain, is developed to study its coupled mechanical behavior under different thrust conditions. The research in this paper is based on numerical CFD simulation with experimental validation. The motion modeling under transverse waves is further verified through uncertainty analysis. The series of research results indicate the following: vessel rolling resonance occurs at λ/L = 1.667 (λ/L denotes the dimensionless wavelength-to-length ratio); increasing air-cushion flow extends the roll period and reduces roll amplitude at λ/L = 0.667, while applying thrust at λ/L = 1.667~3 lowers roll but reduces pitch and heave stability; relatively good berthing performance is achieved when F_CM/∆ = 0.054 and the elastic coefficient is 1.25 × 10⁷ Pa/m (Δ represents the vessel weight). Full article

Obstacle-avoidance risk threshold control and global discrete keypoint re-entry are critical factors influencing the smooth dynamic obstacle avoidance of unmanned vessels. For underactuated USVs, which operate in planar motion with three degrees of freedom (surge, sway, and yaw) but only two independent control inputs (surge velocity and yaw rate), this paper designs a layered obstacle-avoidance strategy featuring adaptive global path re-entry points, combined with short- and long-term obstacle trajectory prediction and risk perception. This method employs an Interactive Multiple Model (IMM) integrating Constant Velocity (CV), Constant Acceleration (CA), and Constant Turn Rate and Acceleration (CTRA) models to perform long-term spatiotemporal trajectory prediction for dynamic obstacles, constructing a spatiotemporal risk cost map. Long-term dynamic obstacle-avoidance trajectory planning is achieved through optimized adaptive global trajectory re-entry points and an improved A* algorithm. This long-term avoidance trajectory replaces the global path from the avoidance start to the re-entry point, providing a smooth, continuous long-term avoidance prediction. To ensure real-time collision avoidance effectiveness, an improved Dynamic Window Approach (DWA) algorithm uses the long-term avoidance trajectory as a foundation. It integrates the IMM’s short-term spatiotemporal obstacle trajectory prediction, sampling in the velocity and steering angle space to generate short-term avoidance control commands. Finally, the long-term and short-term obstacle-avoidance planning are executed in a receding-horizon manner, where the local DWA planner updates control inputs over a short rolling window without solving a full constrained optimization problem. This establishes a hierarchical avoidance strategy: long-term prediction enables smooth avoidance, while short-term prediction enables real-time avoidance, ensuring the continuity and timeliness of dynamic obstacle avoidance. Simulation results demonstrate that compared with traditional A* planning, the proposed risk-aware A* reduces cumulative collision risk by 62% and increases the minimum obstacle clearance distance by over 32.1%, while maintaining acceptable path length growth. This approach effectively reduces collision risks during navigation, enhances path smoothness, and improves navigation safety. Full article

Terminal flight phases, particularly landing, are among the most critical, due to low altitude, low speed and the possible presence of crosswinds. Tools capable of accurately modeling and simulating these phases are essential for identifying potential issues and assessing airplane safety integrity. This work focuses on the development of a nonlinear flight simulator devised to handle terminal maneuvers, including ground effect and wind. Such a simulator incorporates the six-degree-of-freedom rigid body equations of motion coupled with a landing gear model and with a basic control that emulates the action of the pilot, while the aircraft aerodynamic characteristics are estimated through a dedicated semi-empirical procedure. The proposed simulator is employed to assess the effect of crosswind and approach speed on different performance indicators, considering a general aviation airplane (Ryan Navion). These indicators include ground roll distance, wing-tip clearance and lateral forces exerted on the landing gear. The results demonstrate that landings are achievable even beyond the demonstrated crosswind limits without encountering wing-tip strikes or rollover and that higher approach speeds could be advisable in strong crosswind conditions. Full article

Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled data, acquiring large-scale fault data in real-world scenarios remains challenging. To address this issue, this paper proposes a fault diagnosis method combining finite element simulation and deep domain adaptation transfer learning. First, a finite element model of rolling bearings under normal, outer race, inner race, and rolling element fault conditions is developed, and ANSYS/LS-DYNA simulates motion to generate labeled synthetic fault data. The model’s reliability is validated through time-domain, frequency-domain, and time-frequency analyses. A lightweight 1D convolutional neural network (1D CNN) is then designed for fault diagnosis. When trained solely on simulated data, the model achieves only 61.4% accuracy on real data due to domain discrepancies. To bridge this gap, a transfer learning approach integrating generative adversarial networks (GANs) and multi-kernel maximum mean discrepancy (MK-MMD) is proposed: GANs synthesize data resembling real distributions, while MK-MMD minimizes domain shifts between simulated and actual data. This improves the model’s accuracy to 93.8% on real fault datasets. Performance evaluation under variable working conditions and bearing types demonstrates the method’s robustness, providing a practical solution for fault diagnosis in industrial applications with limited data. Full article

Aiming to address the problems of the violent fluctuation of winch traction rope and tire forces and the high safety risk caused by coupling ship motions (rolling, pitching, and heaving), wind loads, and deck space limitations in carrier-based aircraft, this paper focuses on a multi-winch traction system on a small deck. A fully coupled dynamic model of an aircraft landing gear–tire–rope–winch system is constructed, ADAMS2020 and MATLAB/Simulink (MATLAB R2021b) co-simulations are used to develop the three-winch and five-winch traction system models, and a Fiala tire model and a telescopic landing gear model are adopted to build a precise mechanical model of the aircraft. The PID control strategy is proposed, based on the Bessel curve, to control the driving trajectory of the aircraft, and the quantitative influence of ship motion, winch number, and preset trajectory on traction dynamic characteristics is systematically studied. Compared to without trajectory control, the peak force of the winch rope before the start-up phase of the three-winch system is reduced by 54.9%, and the five-winch system is reduced by 57.6%. The fluctuation amplitude of the lateral force of the rear wheel is greater than that of the front wheel, up to a maximum of 215% of the front wheel. The correlation coefficient between the theoretical model and the simulation results is 0.91~0.97, and the error is less than 12%. The PID control strategy based on the Bessel trajectory can significantly improve the steadiness and security of the carrier-based aircraft winch traction system on a small deck. The study delivers the requisite theory and engineering means for the optimized design of carrier-based aircraft traction systems. Full article

The advantages of hybrid models for time series forecasting have received significant attention, and several studies focus on and test their application in the seakeeping of ship motions. A hybrid model integrating an LSTM encoder and Transformer decoder (LT) is introduced to overcome the limitation of individual LSTM and Transformer: initially, the seakeeping response of the KCS ship was simulated by ANSYS-AQWA considering the sea state 3 and 4 simultaneously and established a dataset; secondly, three standalone baseline models (LSTM, Transformer, and TCN), and two hybrid models, LT and LT, with PSO-optimized hyperparameters (P-LT) were constructed and trained to forecast the seakeeping performance of ships with multiple steps of 30, 60 and 90; finally, the comparison between solo and hybrid models was made by different steps on RMSE, MAE and NRMSE evaluations to prove the advancement of LT and P-LT models. The P-LT hybrid model achieved consistent accuracy improvements compared with the best-performing individual models across different ship motions. Notably, RMSE reductions were observed at all prediction horizons (30, 60, and 90 steps), with maximum improvements reaching 13.54% for rolling, 11.83% for pitching, and 12.87% for heaving motions. This study provides both theoretical and practical support to ship motion prediction and demonstrates the potential of the proposed study as an effective engineering product for enhancing safety in ship operation. Full article