Search Results (1,422)

Shoulder disorders, including frozen shoulder resulting from stroke-induced hemiplegia, significantly reduce a patient’s ability to perform activities of daily living, thereby necessitating repeated rehabilitation. Consequently, extensive research has been conducted on rehabilitation robots to assist in upper-limb motor recovery. The shoulder moves according to the scapulohumeral rhythm. Considering the biomechanical characteristics of the shoulder joint, the rehabilitation robot was designed to replicate a similar kinematic environment using actuators and linkages that emulate the structures of the upper arm, shoulder, and clavicle. To ensure precise operation, the kinematic accuracy of the robot was pre-evaluated. Kinematic analyses were conducted using MATLAB, and the results were compared with coordinate data from the mechanical design to evaluate positional accuracy. In addition, the convergence and accuracy of joint-angle estimation for target positions were analyzed. The forward kinematic analysis revealed that the average positional error between the measured and target coordinates ranged from 0.5% to 2.8%, with the Base Motor–Back Motor segment exhibiting the highest error (2.8%). The inverse kinematic analysis demonstrated stable convergence to the target positions through iterative computations using the Gauss–Newton method, confirming that the actual motion could be accurately reproduced within the designed range of motion. Full article

In strong clutter and maneuvering scenarios, radar track initiation faces the dual challenges of a low initiation rate and high false alarm rate. Although the existing deep learning methods show promise, the commonly adopted “feature flattening” input strategy destroys the intrinsic temporal structure and feature relationships of track data, limiting its discriminative performance. To address this issue, this paper proposes a novel radar track initiation method based on Dual-Attention Temporal Convolutional Network (DA-TCN), reformulating track initiation as a binary classification task for very short multi-channel time series that preserve complete temporal structure. The DA-TCN model employs the TCN as its backbone network to extract local dynamic features and innovatively constructs a dual-attention architecture: a channel attention branch dynamically calibrates the importance of each kinematic feature, while a temporal attention branch integrates Bi-GRU and self-attention mechanisms to capture the dependencies at critical time steps. Ultimately, a learnable gated fusion mechanism adaptively weights the dual-branch information for optimal characterization of track characteristics. Experimental results on maneuvering target datasets demonstrate that the proposed method significantly outperforms multiple baseline models across varying clutter densities: Under the highest clutter density, DA-TCN achieves 95.12% true track initiation rate (+1.6% over best baseline) with 9.65% false alarm rate (3.63% reduction), validating its effectiveness for high-precision and highly robust track initiation in complex environments. Full article

This article presents a methodological framework for evaluating hydrodynamic loading on seabed structures in the eastern Mediterranean, originally motivated by the design requirements of special protective structures for a planned high-voltage subsea interconnection between Crete and the Greek mainland. The associated study highlighted the need for a comprehensive evaluation of hydrodynamic loading on seabed structures in the South-East Mediterranean. A methodology is presented for determining representative design kinematics near the seabed, accounting for large-scale oceanic circulation, local wind-induced currents, wind-generated surface waves, and tsunami effects. The method integrates long-term metocean datasets, spectral wave modelling, and reliability-based combinations of critical processes, with adjustments for anticipated climate change impacts. The approach is demonstrated through two case studies involving an electrode protective cage and a submarine electricity transmission cable, both representative of components in subsea power connections. The analysis provides design values of velocities, accelerations, and hydrodynamic forces, with typical checks against sliding, uplift, and vibration. Results highlight the depth-dependent magnitude interplay between ocean circulation and wave-induced particle motions, as well as the importance of biofouling and marine growth. The findings aim to support the safe and sustainable design of offshore energy infrastructure in the eastern Mediterranean and similar marine environments. Full article

To address the challenges in concrete vibration during the construction of concrete-faced rockfill dams, this study proposes a multi-objective-driven lightweight and high-frequency vibrating robotic arm (VRA). The proposed system aims to improve adaptability and performance under harsh site conditions, such as inclined slab surfaces and confined rebar layouts. Based on the geometric structure and task characteristics of the VRA, a multi-objective topology optimization framework was established, integrating compromise programming and average frequency strategies. This method simultaneously achieves mass reduction, stiffness enhancement, and modal frequency improvement to avoid resonance during high-frequency operations. The workspace of the VRA was verified using kinematic modeling and Monte Carlo sampling, and a critical physical posture—where the arm is fully extended horizontally, producing maximum span and joint loads—was identified to extract dynamic load boundaries. Finite element analysis was then conducted under worst-case conditions, and the optimization results were validated by modal analysis and flexibility metrics. The optimized VRA demonstrated substantial improvements in structural performance, reducing overall mass, lowering flexibility, and increasing modal frequencies. The proposed framework provides a transferable approach for designing high-frequency robotic arms in vibration-intensive scenarios, supporting intelligent construction in concrete-faced rockfill dams and similar complex environments. Full article

The main motivation of this research is the semi-analytical exploration of the dynamics of an asteroid that is attacked while approaching a planet (with an inelastic collision of the projectile normally to the surface of the asteroid occurring just before approaching). Namely, the particular case of the spin dynamics of the asteroid that has been struck by a projectile almost perpendicularly to the maximal-inertia principal axis, with further perturbing the dynamics of rotation due to gravitational torques during close approach to the planet, is investigated. The initial surface of the asteroid is assumed to be a rubble pile, but preferably with a quasi-rigid internal structure, with circa constant distances between various parts of the asteroid as a first approximation. As a result of an inelastic collision with the surface of the asteroid, the rubble-pile material should be thrown off the surface into outer space in large amounts; thus, the mass of the asteroid and the moments of inertia along its principal axes should be changed (as well as the regime of angular rotation around its maximal-inertia principal axis). The updated Euler’s equations, stemming from the conservation of angular momentum, have been presented with gravitational torques acting during the approach of the asteroid to the planet (taking into account the impact on the asteroid that occurs just before it enters the zone of close approach). The evolution of the non-linear spin dynamical state is studied, along with kinematical findings for Euler angles via the governing equations, in accordance with two main rotational stages: first, immediately after the impact on the asteroid’s surface; and second, at the regime of asteroid rotation during its close approach to the planet, with perturbations caused by gravitational torques (just after being struck by the projectile). Full article

Accurate heavy-duty vehicle load estimation is crucial for transportation and environmental regulation, yet current methods lack precision in data accuracy and practicality for field implementation. We propose a Self-Supervised Reconstruction Heterogeneous Graph Convolutional Network (SSR-HGCN) for load estimation using On-Board Diagnostics (OBD) data. The method integrates physics-constrained heterogeneous graph construction based on vehicle speed, acceleration, and engine parameters, leveraging graph neural networks’ information propagation mechanisms and self-supervised learning’s adaptability to low-quality data. The method comprises three modules: (1) a physics-constrained heterogeneous graph structure that, guided by the symmetry (invariance) of physical laws, introduces a structural asymmetry by treating kinematic and dynamic features as distinct node types to enhance model interpretability; (2) a self-supervised reconstruction module that learns robust representations from noisy OBD streams without extensive labeling, improving adaptability to data quality variations; and (3) a multi-layer feature extraction architecture combining graph convolutional networks (GCNs) and graph attention networks (GATs) for hierarchical feature aggregation. On a test set of 800 heavy-duty vehicle trips, SSR-HGCN demonstrated superior performance over key baseline models. Compared with the classical time-series model LSTM, it achieved average improvements of 20.76% in RMSE and 41.23% in MAPE. It also outperformed the standard graph model GraphSAGE, reducing RMSE by 21.98% and MAPE by 7.15%, ultimately achieving < 15% error for over 90% of test samples. This method provides an effective technical solution for heavy-duty vehicle load monitoring, with immediate applications in fleet supervision, overloading detection, and regulatory enforcement for environmental compliance. Full article

Insulators on power lines require regular maintenance by operators in high-altitude hazardous environments, and the emergence of aerial manipulators provides an efficient and safe support for this scenario. In this study, a lightweight telescopic aerial manipulator system is developed, which can realize the installation and retrieval of insulator inspection robots on power lines. The aerial manipulator has three degrees of freedom, including two telescopic scissor mechanisms and one pitch rotation mechanism. Multiple types of cameras and sensors are specifically configured in the structure, and the total mass of the structure is 2.2 kg. Next, the kinematic model, dynamic model, and instantaneous contact force model of the designed aerial manipulator are derived. Then, the hybrid position/force control strategy of the aerial manipulator and the visual detection and estimation algorithm are designed to complete the operation or complete the task. Finally, the lifting external load test, grasp and installation operation test, as well as outdoor flight operation test are carried out. The test results not only quantitatively evaluate the effectiveness of the structural design and control design of the system but also verify that the aerial manipulator can complete the accurate automatic grasp and installation operation of the 3.6 kg target device in outdoor flight. Full article

The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has enabled personalized sports coaching, yet a significant gap remains: translating low-level sensor data into high-level, contextualized feedback. Large Language Models (LLMs) excel at reasoning and instruction but lack a native understanding of physical kinematics. This paper introduces BoxingPro, a novel framework that bridges this semantic gap by fusing wearable sensor data with LLMs for automated boxing coaching. Our core contribution is a dedicated translation methodology that converts multi-modal time-series data (IMU) and visual data (video) into structured linguistic prompts, enabling off-the-shelf LLMs to perform sophisticated biomechanical reasoning without extensive retraining. Our evaluation with professional boxers showed that the generated feedback achieved an average expert rating of over 4.0/5.0 on key criteria like biomechanical correctness and actionability. This work establishes a new paradigm for integrating sensor-based systems with LLMs, with potential applications extending far beyond boxing to any domain requiring physical skill assessment. Full article

The optimization of the micro-motion rotary mechanism aims to obtain the maximum rotation angle in a certain space and increase the compensation range of the micro-motion mechanism. Aiming to address the disadvantages of a small movement stroke, low positioning accuracy, and limited research on the sub-arc-second level of precision micro-drive mechanism, a micro-drive mechanism was designed in this study and structural optimization was performed to obtain the maximum output angle. Additionally, the performance of the optimized mechanism was investigated. First, based on the principle of a flexure hinge guide and conversion, a micro-drive rotary mechanism that could transform the linear motion of piezoelectric ceramics into rotating motion accurately without parasitic motion and non-motion direction force was designed. Second, its structural optimization was achieved using the particle swarm optimization algorithm. Third, analyses of the drive performance and kinematics of the system were conducted. Finally, a performance test platform for the micro-drive rotary mechanism was built, its positioning performance and dynamic characteristics were verified experimentally, and the maximum rotary displacements and positioning error of the system were calculated. This research has certain reference value for studies of ultra-precision positioning. Full article

The discrete-serpentine heterogeneous multi-arm space robot (DSHMASR) has more advantages than single discrete space robots or single serpentine space robots in complex tasks of on-orbit servicing. However, the mechanical structure complexity of the DSHMASR poses challenges for modeling and motion planning. In this paper, a coupled kinematic model and a coordinated trajectory planning method for the DSHMASR were proposed to address these issues. Firstly, an uncontrolled satellite and the DSHMASR were modeled based on the momentum conservation law. The generalized Jacobian matrix J_g of the space robotic system was derived. Secondly, the manipulation capability of the DSHMASR was analyzed based on the null-space of J_g. Furthermore, the cooperative capturing-monitoring trajectory planning method for DSHMASR was presented through the manipulability optimization. The expected trajectory of each arm’s tip can be obtained by pose deviations and velocity deviations between the tip and the target point. Additionally, the optimized joint velocities of each arm were calculated by combining differential kinematics and manipulability optimization. Therefore, the manipulability of DSHMASR in the direction of the capture operation was enhanced simultaneously as it approached the target satellite. Finally, the proposed algorithm was demonstrated by establishing the Adams–Simulink co-simulation model. Comparisons with traditional approaches further confirm the outperformance of the proposed method in terms of manipulation capability. Full article

By emulating the morphological structures of organisms, bionic robots achieve enhanced locomotion efficiency, stability, and environmental adaptability. Inspired by insect morphology and biological locomotion mechanisms, a wheel-legged transformation device for a hexapedal robot is proposed in this work. First, an iris-type wheel-legged transformation mechanism is designed. Subsequently, the operational principle of the iris–link composite mechanism is analyzed, and kinematic modeling of the transformation process is conducted. Finally, joint angle rotation, positional variation, and their effects under different gait states are examined through simulation of three typical gait patterns. Experimental results demonstrate that the proposed design significantly improves the motion stability of the bionic hexapedal robot. Furthermore, the adoption of a hollow leg structure reduces weight while enhancing locomotion flexibility, thereby strengthening the robot’s overall capability to respond to external disturbances. In summary, this study offers a valuable reference for the future development of wheel-legged transformable bionic robots. Full article

Background: Ultrasound is a non-invasive imaging technique that provides real-time evaluation of anatomical structures. While versatile in examining various organs, it can be physically demanding for physicians due to the need for challenging positions, causing musculoskeletal pain and potentially work-related diseases over time. The study aimed to assess the ergonomics of abdominal and breast ultrasound, identify the most challenging anatomical area, determine which part of the examination causes the greatest strain, and evaluate the overall ergonomic impact of the entire procedure. Methods: This single-center study involved 4 radiologists and focused on breast and abdominal ultrasonography. Kinematic data were recorded using the Noraxon Ultium Motion inertial system to track body movements during the ultrasound procedures. Five critical segments were identified while examining the liver, right kidney, left kidney, right breast, and left breast. Ergonomic assessment was performed using the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) methods, evaluating postural risks and physical strain during each segment and the whole procedure. Results: Both RULA and REBA assessments yielded median total scores of 6.0–7.0 and 6.0–7.5, respectively, reflecting consistently medium to high musculoskeletal loading. Examinations of the left breast and left kidney were associated with the most demanding postures. These elevated scores demonstrate that abdominal and breast ultrasonography imposes substantial ergonomic strain, potentially increasing the risk of work-related musculoskeletal disorders. Conclusions: The high ergonomic risk scores indicate an urgent need to modify scanning techniques and workstation design to reduce musculoskeletal strain in sonographers. Implementing ergonomic improvements is essential to prevent occupational injuries and promote long-term health. Full article

By extending the four-dimensional semi-Riemann geometry to higher-dimensional Finsler/Hamilton geometry, the canonical quantization of the fundamental metric tensor of general relativity, i.e., an approach that tackles a geometric quantity, is derived. With this quantization, the smooth continuous Finsler structure is transformed into a quantized Hamilton structure through the kinematics of a free-falling quantum particle with a positive mass, along with the introduction of the relativistic generalized uncertainty principle (RGUP) that generalizes quantum mechanics by integrating gravity. This transformation ensures the preservation of the positive one-homogeneity of both Finsler and Hamilton structures, while the RGUP dictates modifications in the noncommutative relations due to integrating consequences of relativistic gravitational fields in quantum mechanics. The anisotropic conformal transformation of the resulting metric tensor and its inverse in higher-dimensional spaces has been determined, particularly highlighting their translations to the four-dimensional fundamental metric tensor and its inverse. It is essential to recognize the complexity involved in computing the fundamental inverse metric tensor during a conformal transformation, as it is influenced by variables like spatial coordinates and directional orientation, making it a challenging task, especially in tensorial terms. We conclude that the derivations in this study are not limited to the structure in tangent and cotangent bundles, which might include both spacetime and momentum space, but are also applicable to higher-dimensional contexts. The theoretical framework of quantization of general relativity based on quantizing its metric tensor is primarily grounded in the four-dimensional metric tensor and its inverse in pseudo-Riemannian geometry. 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

The topic of the present study is the aerodynamic performance of a Natural Laminar Flow (NLF) wing for UAVs at low speed. The basis is a thoroughly tested NLF airfoil in the wind tunnel of NASA which is well-customized for light aircrafts. The aim of this work is the numerical verification that a typical wing design (tapered with moderate aspect ratio and wash-out), being constructed out of aerodynamically highly efficient NLF airfoils during cruise, can deliver high aerodynamic loading under minimal freestream turbulence as well as realistic atmospheric conditions of intermediate turbulence. Thus, high mission flexibility is achieved, e.g., short take off/landing capabilities on the deck of ship where moderate air turbulence is prevalent. Special attention is paid to the effect of the Wing Tip Vortex (WTV) under minimal inflow turbulence regimes. The flight conditions are take off or landing at moderate Reynolds number, i.e., one to two millions. The numerical simulation is based on an open source CFD code and parallel processing on a High Performance Computing (HPC) platform. The aim is the identification of both mean flow and turbulent structures around the wing and subsequently the formation of the wing tip vortex. Due to the purely three-dimensional character of the flow, the turbulence is resolved with advanced modeling, i.e., the Improved Delayed Detached Eddy Simulation (IDDES) which is well-customized to switch modes between Delayed Detached Eddy Simulation (DDES) and Wall-Modeled Large Eddy Simulation (WMLES), thus increasing the accuracy in the shear layer regions, the tip vortex and the wake, while at the same time keeping the computational cost at reasonable levels. IDDES also has the capability to resolve the transition of the boundary layer from laminar to turbulent, at least with engineering accuracy; thus, it serves as a high-fidelity turbulence model in this work. The study comprises an initial benchmarking of the code against wind tunnel measurements of the airfoil and verifies the adequacy of mesh density that is used for the simulation around the wing. Subsequently, the wing is positioned at near-stall conditions so that the aerodynamic loading, the kinematics of the flow and the turbulence regime in the wing vicinity, the wake and far downstream can be estimated. In terms of the kinematics of the WTV, a thorough examination is attempted which comprises its inception, i.e., the detachment of the boundary layer on the cut-off wing tip, the roll-up of the shear layer to form the wake and the motion of the wake downstream. Moreover, the effect of inflow turbulence of moderate intensity is investigated that verifies the bibliography with regard to the performance degradation of static airfoils in a turbulent atmospheric regime. Full article