Search Results (498)

In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential. Full article

This paper presents a kinematics-constrained grid-based path planning algorithm that generates real-time, safe, and executable trajectories, thereby enhancing the performance and reliability of autonomous vehicle parking systems. The grid resolution adapts to the minimum turning radius and steering limits, ensuring feasible motion primitives. The cost function integrates path efficiency, direction-switching penalties, and collision risk to ensure smooth and feasible maneuvers. A cubic spline refinement produces curvature-continuous trajectories suitable for vehicle execution. Simulation and experimental results demonstrate that the proposed method achieves collision-free and curvature-bounded paths with significantly reduced computation time and improved maneuver smoothness compared with conventional A* and Hybrid A*. In both structured and dynamic parking environments, the planner consistently maintained safe clearance and stable tracking performance under variations in vehicle geometry and velocity. These results confirm the robustness and real-time feasibility of the proposed approach, effectively unifying kinematic feasibility, safety, and computational efficiency for practical autonomous parking systems. Full article

In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation. Full article

To meet the increasing demands of energy efficiency and real-time performance in autonomous driving systems, this paper presents a lightweight and robust LiDAR SLAM framework designed with power-aware considerations. The proposed system introduces three core innovations. First, it replaces traditional ordered point cloud indexing with a 2D range image projection, significantly reducing memory usage and enabling efficient feature extraction with curvature-based criteria. Second, a multi-stage outlier rejection mechanism is employed to enhance feature robustness by adaptively filtering occluded and noisy points. Third, we propose a dynamically filtered local mapping strategy that adjusts keyframe density in real time, ensuring geometric constraint sufficiency while minimizing redundant computation. These components collectively contribute to a SLAM system that achieves high localization accuracy with reduced computational load and energy consumption. Experimental results on representative autonomous driving datasets demonstrate that our method outperforms existing approaches in both efficiency and robustness, making it well-suited for deployment in low-power and real-time scenarios within intelligent transportation systems. Full article

Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simplified dataset from the raw scan data while preserving the underlying geometry of the damaged blade in high-curvature and damaged regions. At first, outliers are removed from the scan data, and measurement noise is reduced through local least-squares quadric surface/plane fitting on the adaptive support domain of measured points under the measurement uncertainty constraint of inspection data. Then, a directed Hausdorff distance-based region growing scheme is developed to progressively search within the support domain of denoised data points to obtain a down-sampled dataset while preserving the local geometric shape of the surface. Numerical and experimental case studies have been conducted to evaluate the accuracy and computation time of scan-to-CAD rigid registration and CAD-to-scan non-rigid registration processes using the down-sampled dataset of damaged blades. The results have demonstrated that the proposed methodology effectively removes the measurement noise and outliers and provides a down-sampled dataset from the scan data that can significantly reduce the time complexity of the computer-aided inspection and remanufacturing process of the point cloud of damaged blades with a negligible loss of accuracy. Full article

Efficient simplification of 3D models is essential for mobile and other resource-constrained application scenarios. Industrial 3D assemblies, typically composed of numerous components and dense triangular meshes, often pose significant challenges in rendering and transmission due to their large scale and high complexity. The Quadric Error Metrics (QEM) algorithm offers a practical balance between simplification accuracy and computational efficiency. However, its application to large-scale industrial models remain limited by performance bottlenecks, especially when combined with curvature-based optimization techniques that improve fidelity at the cost of increased computation. Therefore, this paper presents a parallel implementation of the QEM algorithm and its curvature-optimized variant using the OpenMP framework. By identifying key bottlenecks in the serial workflow, this research parallelizes critical processes such as curvature estimation, error metric computation, and data structure manipulation. Experiments on large industrial assembly models at a simplification ratio of 0.3, 0.5, and 0.7 demonstrate that the proposed parallel algorithms achieve significant speedups, with a maximum observed speedup of 5.5×, while maintaining geometric quality and topological consistency. The proposed approach significantly improves model processing efficiency, particularly for medium- to large-scale industrial models, and provides a scalable and practical solution for real-time loading and interaction in engineering applications. Full article

The recruitment of peripheral membrane proteins is tightly regulated by membrane lipid composition and local electrostatic microenvironments. Our experimental observations revealed that Vp54, a viral matrix protein, exhibited preferential binding to lipid bilayers enriched in anionic lipids such as phosphatidylglycerol (PG) and phosphatidylserine (PS), compared to neutral phosphatidylcholine/phosphatidylethanolamine liposomes, and this occurred in a curvature-dependent manner. To elucidate the molecular basis of this selective interaction, we performed a series of computational analyses including helical wheel projection, electrostatic potential calculations, electric field lines simulations, and electrostatic force analysis. Our results showed that the membrane-proximal region of Vp54 adopted an amphipathic α-helical structure with a positively charged interface. In membranes containing PG or PS, electrostatic potentials at the interface were significantly more negative, enhancing attraction with Vp54. Field line and force analyses further confirmed that both the presence and spatial clustering of anionic lipids intensify membrane–Vp54 electrostatic interactions. These computational findings align with experimental binding data, jointly demonstrating that membrane lipid composition and organization critically modulate Vp54 recruitment. Together, our findings highlight the importance of electrostatic complementarity and membrane heterogeneity in peripheral protein targeting and provide a framework applicable to broader classes of membrane-binding proteins. Full article

In avian biology, the radius of curvature, or R, has hardly ever been used to study the mechanics of birds’ egg shape formation. However, it is essential for introducing important details about the form, function, and performance of an object, which is useful in biomedicine, manufacturing, and precision design. In order to determine a possible biological mechanism and the location of load application that creates the distinctive asymmetric egg shape in nature, the goal of this study was to develop a formula for computing R at any point over an egg contour. We derived a relatively simple means of computing R and identified the location that muscular compression is carried out to give the egg its characteristic form. This location (x/L), the angle (α) of compression and the relative magnitude of the load proportional to R can help identify a specific section of the oviduct and the squeezing muscle involved. Novel equations for computing R, x/L and α were proposed, based on standard geometric parameters. Our findings demonstrate how the theoretical knowledge of physical, mechanical and mathematical processes can contribute to the solution of biological problems and resonates with the fields of egg-inspired engineering. Full article

This paper provides advances in the study of principal $\mathrm{SO}\left(3\right)$-bundles over smooth projective real curves, with applications to robot manipulation orientation. The work introduces a novel specific classification of these bundles, establishing a bijection between isomorphism classes and specific direct sums of cyclic groups. The explicit computation of the cohomology ring $H^{*}(P,\mathbb{Z})$ for a principal $\mathrm{SO}\left(3\right)$-bundle P over a real curve X, revealing its complete structure and torsion subgroups, is a major contribution of the paper. This paper further demonstrates that the equivariant cohomology $H_{\mathrm{SO}\left(3\right)}^{*}(P,\mathbb{Z})$ is isomorphic to $H^{*}(X,\mathbb{Z})\otimes H^{*}(B\mathrm{SO}\left(3\right),\mathbb{Z})$, with implications for connections and curvature. These results are then applied to robotics, showing that for manipulators with revolute joints, a principal $\mathrm{SO}\left(3\right)$-bundle encoding end-effector orientation whose second Stiefel–Whitney class characterizes the obstruction to continuous orientation control exists. For robots with spherical wrists, the configuration space factors as a product, allowing for the decomposition of connections with control implications. Finally, a mechanical connection is constructed that minimizes kinetic energy, with its curvature identifying configurations where small perturbations cause large orientation changes. Full article

A physics-based vehicle–track coupled dynamic model embedding a hydraulic electromechanical regenerative damper (HERD) is developed to quantify electrical power recovery and wear depth in high-speed service. The HERD subsystem resolves compressible hydraulics, hydraulic rectification, line losses, a hydraulic motor with a permanent-magnet generator, an accumulator, and a controllable; co-simulation links SIMPACK with MATLAB/Simulink. Wheel–rail contact is computed with Hertz theory and FASTSIM, and wear depth is advanced with the Archard law using a pressure–velocity coefficient map. Both HERD power regeneration and wear depth predictions have been validated against independent measurements of regenerated power and wear degradation in previous studies. Parametric studies over speed, curve radius, mileage and braking show that increasing speed raises input and output power while recovery efficiency remains 49–50%, with instantaneous electrical peaks up to 425 W and weak sensitivity to curvature and mileage. Under braking from 350 to 150 km/h, force transients are bounded and do not change the lateral wear pattern. Installing HERD lowers peak wear in the wheel tread region; combining HERD with flexible wheelsets further reduces wear depth and slows down degradation relative to rigid wheelsets and matches measured wear more closely. The HERD electrical load provides a physically grounded tuning parameter that sets hydraulic back pressure and effective damping, which improves model accuracy and supports calibration and updating of digital twins for maintenance planning. Full article

Site selection evaluation is a crucial step in the research of hydrogen storage in saline aquifers. Geometric characteristics of the reservoir are one of the key factors determining the site selection evaluation. However, for the anticlinal saline aquifers with effective trap capacity, the coupled effects of reservoir curvature, thickness, and well configuration on hydrogen storage efficiency remain unclear. Thus, based on the Ordos Basin, various 3D computational models with different curvatures, thicknesses, and well configurations are designed to conduct the simulation analysis. The results show that (1) the greater the curvature, the stronger the trap effect. Hydrogen recovery rises first and then declines, reaching a peak of 79.58% at 170° and dropping to 55.17% at 90°. (2) Increasing thickness suppresses lateral hydrogen migration. The maximum gas saturations in the caprock are 0.12, 0.08, and 0.05 for thicknesses of 100%, 200%, and 300%, respectively, indicating that greater thickness reduces gas diffusion into the caprock. (3) The coupling effect between curvature and thickness affects the recovery rate. Thin reservoirs are suitable for small curvatures, while thick reservoirs are more suitable for high curvatures. (4) Top hydrogen injection significantly reduces the sensitivity of the recovery rate to curvature and thickness. When the curvature is between 180° and 100°, lowering recovery differences across thicknesses are lowered from 16.20% under bottom injection to 2.51% under top injection. These results provide support for the site selection and design of hydrogen storage in saline aquifers. Full article

Lane detection is an essential task in the field of computer vision and autonomous driving. This involves identifying and locating road markings on the road surface. This capability not only helps drivers keep the vehicle in the correct lane, but also provides critical data for advanced driver assistance systems and autonomous vehicles. Traditional lane detection models work mainly on the 2D image plane and achieve remarkable results. However, these models often assume a flat-world scenario, which does not correspond to real-world conditions, where roads have elevation variations and road markings may be curved. Our approach solves this challenge by focusing on 3D lane detection without relying on the inverse perspective mapping technique. Instead, we introduce a new framework using the MambaVision-S-1K backbone, which combines Mamba-based processing with Transformer capabilities to capture both local detail and global contexts from monocular images. This hybrid approach allows accurate modeling of lane geometry in three dimensions, even in the presence of elevation variations. By replacing the traditional convolutional neural network backbone with MambaVision, our proposed model significantly improves the capability of 3D lane detection systems. Our method achieved state-of-the-art performance on the ONCE-3DLanes dataset, thus demonstrating its superiority in accurately capturing lane curvature and elevation variations. These results highlight the potential of integrating advanced backbones based on Vision Transformers in the field of autonomous driving for more robust and reliable lane detection. The code will be available online. Full article

In this paper, we investigate massive charged scalar perturbations in four-dimensional charged Lifshitz–AdS black holes with scalar hair within the framework of Einstein–Maxwell–Dilaton (EMD) gravity. Using the improved asymptotic iteration method (AIM), we compute the quasinormal modes (QNMs) and explore their dependence on key parameters, including the Lifshitz dynamical exponent z, the scalar field mass and charge, and the black hole charge, under various spatial curvature settings ($k=0,\pm 1$). Our results reveal rich and sensitive behavior in both the real and imaginary parts of the QNMs. In particular, the decay rates can exhibit monotonic or non-monotonic dependence on the black hole charge, depending on the values of z, $m_s$, and $q_s$. These findings highlight the significant role of field and geometric parameters in governing the dynamical stability of Lifshitz black holes and offer insights into the perturbative properties of non-AdS holographic systems. Full article

We investigate left-invariant generalized cross-curvature solitons on simply connected three-dimensional Lorentzian Lie groups. Working with the assumption that the contravariant tensor $P_{ij}$ (defined from the Ricci tensor and scalar curvature) is invertible, we derive the algebraic soliton equations for left-invariant metrics and classify all left-invariant generalized cross-curvature solitons (for the generalized equation ${\mathcal{L}}_{X}g+\lambda g=2h+2\rho Rg$) on the standard 3D Lorentzian Lie algebra types (unimodular Types Ia, Ib, II, and III and non-unimodular Types IV.1, IV.2, and IV.3). For each Lie algebra type, we state the necessary and sufficient algebraic conditions on the structure constants, provide explicit formulas for the soliton vector fields X (when they exist), and compute the soliton parameter $\lambda$ in terms of the structure constants and the parameter $\rho$. Our results include several existence families, explicit nonexistence results (notably for Type Ib and Type IV.3), and consequences linking the existence of left-invariant solitons with local conformal flatness in certain cases. The classification yields new explicit homogeneous generalized cross-curvature solitons in the Lorentzian setting and clarifies how the parameter $\rho$ modifies the algebraic constraints. Examples and brief geometric remarks are provided. Full article

To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery. Full article