Search Results (160)

Conventional borescopes are limited by inadequate mechanical flexibility, poor environmental adaptability and reachability, and heavy reliance on operator expertise during aero-engine inspections, making it difficult to meet the demands for efficient and dependable in situ nondestructive evaluation (NDE). This paper presents a novel telescopic continuum robot mechanism with an ultra-high aspect ratio (63.75:1) and three constant-curvature segments, achieving a synergistic design between the robot’s body structure and the long-stroke linear actuator of its central backbone to realize ultra-high-aspect-ratio configurations. This design improves the robot’s ability to access complex and confined internal spaces within aero-engines, thereby reducing inspection blind spots. Furthermore, a configuration-space control strategy integrating kinematic decoupling and driving tendon tension compensation is proposed. This strategy addresses the issues of multi-segment actuation coupling and tendon slack, ensuring the motion control performance for in situ aero-engine blade inspection. The feasibility of the mechanism design was validated through an experimental simulation platform incorporating both turbine blade and compressor blade scenarios. This work offers a new solution for in situ NDE in aero-engines by synergistically integrating an innovative ultra-high-aspect-ratio telescopic mechanism with a dedicated configuration-space controller that addresses multi-segment coupling and tendon slack. Full article

Multi-joint hydraulic robotic arms are core equipment in intelligent mining, yet their performance is often limited by strong dynamic coupling and nonlinear hydraulic effects. Traditional control methods struggle to achieve high-precision trajectory tracking and coordinated motion under high loads and flow-coupling constraints. To address these challenges, this paper establishes a coupled hydraulic–mechanical dynamic model for a multi-joint robotic arm. The mechanical dynamics are derived using the Lagrangian formulation, while the hydraulic dynamics account for flow coupling among cylinders. An improved deviation coupling control (IDCC) strategy is proposed, integrating feedforward–feedback compensation, coupling error regulation, and a flow-limiting correction term. Co-simulation in Simulink (2024b) and Amesim (2020) shows that under flow-saturation conditions, the improved strategy reduces the peak trajectory errors by approximately 47.88%, 28.08%, and 49.89% for Joints 1–3, respectively, and shortens the settling time by 27.93%. Experimental results from a three-joint hydraulic test platform confirm error reductions of 10.20–15.58% and a 31.50% decrease in overall adjustment time. The study demonstrates that the proposed control strategy effectively suppresses multi-joint coupling interferences, enhances trajectory tracking accuracy, and improves the adaptability of hydraulic robotic arms under flow-limited conditions, providing a viable solution for high-precision control in intelligent mining applications. Full article

Dual linear motor-driven systems (DLMDS) are widely used in industrial manufacturing due to their high dynamic stability and robust performance, typically featuring a symmetric Y1–Y2 axis structure. High-precision synchronization control of the motion platform is crucial for overall system performance. However, in practice, such systems are inevitably affected by mechanical installation errors, load disturbances, and nonlinear friction, which lead to the asymmetry of the Y1–Y2, severely degrading the synchronization accuracy between the two symmetric axes. To address these challenges, this paper proposes an EtherCAT-enabled active disturbance rejection control (ADRC) strategy for high-performance gantry synchronization systems. To cope with strong coupling effects, external disturbances, and high-speed operation, a master–slave synchronization architecture is developed based on ADRC and the EtherCAT cyclic synchronous torque (CST) mode. An extended state observer (ESO) is employed to estimate and compensate for lumped disturbances in real time, enabling precise synchronization without relying on an accurate mechanical model. Experimental results under both low-speed and high-speed operating conditions show that the proposed method significantly improves the synchronization stability and robustness compared with conventional cross-coupling control and master–slave control strategies. Specifically, the ADRC-based approach reduces synchronization errors by more than 20% under disturbance-free conditions and suppresses approximately 80% of disturbance-induced errors during high-speed operation. These results confirm the effectiveness and practical applicability of the proposed control strategy for high-precision gantry motion systems. Unlike conventional torque-mode implementations that merely replace the position loop with torque regulation, the proposed method introduces a disturbance-estimation-driven synchronization architecture co-designed with deterministic EtherCAT cyclic timing, which enables distributed real-time compensation beyond classical torque feedforward strategies. Full article

Offshore operational environments are inherently stochastic, with waves, currents, and wind loads exerting a significant influence on vessel attitude and equipment stability. While Stewart platforms enable active motion compensation, conventional control strategies frequently suffer from time delays, actuator lag, and limited disturbance rejection, resulting in inadequate performance under complex sea conditions. To overcome these limitations, this paper presents a wave compensation control strategy for a Stewart platform that integrates deep learning-based prediction with active disturbance rejection control (ADRC). A bidirectional long short-term memory (BiLSTM) network is developed to predict vessel attitude in advance. The predicted attitude is transformed into actuator displacement commands through the inverse kinematics of the Stewart platform. An ADRC-based displacement controller is then designed to achieve fast and robust compensation under wave disturbances. Six-degree-of-freedom (6-DOF) dynamic models of a catamaran and a Stewart platform are established in Simulink and Simscape, and sea states 2, 4, and 6 are simulated using an enhanced Joint North Sea Wave Project (JONSWAP) wave spectrum. The simulation results show that, compared with Proportional–Integral–Derivative (PID) and ADRC methods, the proposed BiLSTM-ADRC strategy reduces the roll root mean squared error (RMSE) by 76.6% and 73.2%, and pitch RMSE by 64.1% and 58.1%, respectively, demonstrating an improved attitude stabilization performance. Full article

Understanding how seat-induced whole-body vibration (WBV) is transmitted to and actively compensated by the human body is essential for accurately assessing discomfort, fatigue, and postural control in vehicle occupants. This study proposes a visual–inertial fusion framework utilizing IMU-RGB-D data to isolate seated human body vibration in dynamic vehicular environments. In real-cabin monitoring systems, measured motion is a superposition of platform vibration, passive transmission through the body, active postural compensation, and camera jitter. Existing WBV and driver monitoring studies typically rely on single modality sensing, such as inertial or visual approaches, without decomposing these components or modelling camera vibration. The framework synchronized three IMUs with RGB-D landmarks. Seat, human body, and camera accelerations are separated, and body vibration velocity is derived from body–seat differential acceleration via band-pass filtering and spectral integration. The 3D landmarks enable rotational-translational Postural Compensation Index metrics, axis-wise energy distributions, and anthropometric consistency checks. The study is held in an in-service urban tram case. Torso vibration is dominated by 40% anteroposterior components, while head postural is predominantly > 50% lateral sway. Near static anthropometric evaluation was also studied, resulting in shoulder width errors that remain within ±10–20 mm. The results show that the framework can distinguish passive ride phases from strongly compensated phases, separate camera jitter from true body motion, and reveal anisotropic postural strategies, providing a structured basis for vibration and posture analysis in in-vehicle monitoring. Full article

Unmanned aerial vehicle (UAV) imagery poses persistent challenges for object detection, including dense small objects, large-scale variation, cluttered backgrounds, and stringent localization requirements, where conventional two-stage detectors often fall short in fine-grained small-object representation, efficient global context modeling, and classification–localization consistency. We specifically target low-altitude UAV-captured imagery with highly flexible viewpoints (near-nadir to oblique) and frequent platform-induced motion blur, which makes dense small-object localization substantially more challenging than in conventional remote-sensing imagery. To address these issues, we propose CASA-RCNN, a context-adaptive and scale-aware two-stage detection framework tailored to UAV scenarios. CASA-RCNN introduces a shallow-level enhancement module, ConvSwinMerge, which strengthens position-sensitive cues and suppresses background interference by combining coordinate attention with channel excitation, thereby improving discriminative high-resolution features for small objects. For deeper semantic features, we incorporate an adaptive sequence modeling module based on MambaBlock to capture long-range dependencies and support context reasoning in crowded or occluded scenes with practical computational overheadon a desktop GPU. In addition, we adopt Varifocal Loss for quality-aware classification to better align confidence scores with localization quality, and we design a ScaleAdaptiveLoss to dynamically reweight regression objectives across object scales, compensating for the reduced gradient contribution of small targets during training. Experiments on the VisDrone2021 validation benchmark show that CASA-RCNN achieves 22.9% mAP, improving Faster R-CNN by 9.0 points; it also reaches 36.6% mAP₅₀ and 25.7% mAP₇₅. Notably, performance on small objects improves to 12.5% mAP_s (from 6.9%), and ablation studies confirm the effectiveness and complementarity of the proposed components. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

Background: Augmented reality (AR) and mixed reality (MR) promise to enhance anatomical understanding, spatial orientation, and workflow in cardiac surgery. Their clinical adoption remains limited and the translational path is incompletely defined. Methods: A PubMed search was conducted by two independent reviewers from database inception through July 2025 and identified peer-reviewed, English-language articles describing peri- or intra-operative AR/MR applications in cardiac surgery. Relevant clinical, preclinical, technical, and review articles were selected for inclusion based on scope and content. Given the narrative approach and heterogeneity across studies, findings were synthesized qualitatively into application domains. Results: Fourteen studies were included. Five domains emerged: (1) preoperative planning and patient-specific modelling—MR enhanced spatial orientation and planning for minimally invasive and valve procedures; (2) intraoperative navigation and visualization—AR improved targeting and interpretation with preclinical overlay errors ≈ 5 mm; (3) physiological/functional guidance—thermographic AR detected ischemia in vivo with strong correlation to invasive thermometry; (4) robotic integration and workflow optimization—AR-guided port placement and stepwise robotic adoption supported the feasibility of totally endoscopic CABG; (5) AR-based early rehabilitation. Conclusions: Early clinical and preclinical evidence supports AR/MR feasibility and utility for visualization and orientation in cardiac surgery. Priorities include deformable, motion-compensated registration, ergonomic integration with robotic platforms, and multicentre trials powered for operative efficiency and patient outcomes. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind–wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%. These findings demonstrate the framework’s capability to ensure safe personnel transfer by effectively isolating complex vessel motions and validate the reliability of the coupled dynamic model for offshore operational forecasting. Full article

This paper addresses stability issues, including voltage fluctuation, a frequency offset, and broadband oscillation resulting from the high penetration of renewable energy in a photovoltaic high-permeability distribution network. This paper proposes an active support control strategy which is energy storage grid-connected based on a virtual synchronous generator (VSG). This strategy endows the energy storage system with virtual inertia and a damping capacity by simulating the rotor motion equation and excitation regulation characteristics of the synchronous generator, and effectively enhances the system’s ability to suppress power disturbances. The small-signal model of the VSG system is established, and the influence mechanism of the virtual inertia and damping coefficient on the system stability is revealed. A delay compensator in series with a current feedback path is proposed. Combined with the damping optimization of the LCL filter, the instability risk caused by high-frequency resonance and a control delay is significantly suppressed. The novelty lies in the specific configuration of the compensator within the grid–current feedback loop and its coordinated design with VSG parameters, which differs from traditional capacitive–current feedback compensation methods. The experimental results obtained from a semi-physical simulation platform demonstrate that the proposed control strategy can effectively suppress voltage fluctuations, suppress broadband oscillations, and improve the dynamic response performance and fault ride-through capability of the system under typical disturbance scenarios such as sudden illumination changes, load switching, and grid faults. It provides a feasible technical path for the stable operation of the distribution network with a high proportion of new energy access. Full article

Optical image stabilization (OIS) is crucial for improving airborne opto-electronic imaging performance under dynamic conditions. This study presents a two-dimensional piezoelectric-driven OIS platform capable of compensating linear image shift errors. A motion platform integrating a bridge amplification mechanism and right-angle guiding beams was developed, and its theoretical model was validated through finite element analysis (FEA). To enhance the platform’s repeatability, the hysteresis of the piezoelectric actuator was described using the Bouc-Wen model, and was optimized using a Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO). Experimental results demonstrated that the platform achieves a workspace of 53.92 μm × 53.76 μm, a motion resolution of 30 nm, a maximum coupling error of 2.28%, and a first-order resonant frequency of 356.69 Hz. A composite controller incorporating HGAPSO attained submicron tracking accuracy, with errors of 0.43 μm and 0.47 μm along the X and Y axes, respectively. Strong agreement among theoretical analysis, FEA, and experimental results confirms the platform’s precision and effectiveness meeting the requirements of the OIS. This work provides valuable guidance for the development of high-frequency OIS systems in highly dynamic operational environments. Full article

Background: Respiratory-induced target displacement remains a major challenge for achieving accurate and safe augmented-reality-guided thoracoabdominal percutaneous puncture. Existing approaches often suffer from system latency, dependence on intraoperative imaging, or the absence of intelligent timing assistance; Methods: We developed a mixed-reality (MR) surgical navigation system that incorporates Adaptive Kalman-filter-based respiratory prediction module and visual gating cues. The system was evaluated using a dynamic respiratory motion simulation platform. The Kalman filter performs real-time state estimation and short-term prediction of optically tracked respiratory motion, enabling simultaneous compensation for MR model drift and forecasting of the end-inhalation window to trigger visual guidance; Results: Compared with the uncompensated condition, the proposed system reduced dynamic registration error from (3.15 ± 1.23) mm to (2.11 ± 0.58) mm (p < 0.001). Moreover, the predicted guidance window occurred approximately 142 ms in advance with >92% accuracy, providing preparation time for needle insertion; Conclusions: The integrated MR system effectively suppresses respiratory-induced model drift and offers intelligent timing guidance for puncture execution. Full article

Background: Patients after anterior cruciate ligament reconstruction (ACLR) often exhibit persistent biomechanical deficits, particularly during high-demand tasks like the single-leg drop jump (SLDJ). At approximately six months post-ACLR, patients frequently rely on visual input to compensate for persistent sensorimotor deficits during dynamic tasks, which may lead to altered movement patterns. While visual perturbations have been studied in bilateral jump tasks, their impact on SLDJ biomechanics in ACLR patients remains unexplored. Methods: Patients who were still engaged in rehabilitation and not yet cleared for unrestricted return to sport performed SLDJ under three visual conditions: normal vision, low visual perturbation, and high visual perturbation using stroboscopic glasses. Kinematic and kinetic variables were measured using a 3-dimensional motion analysis system and force platform. Comparisons were made between the ACLR and non-operated limbs, as well as across visual conditions. Results: 24 patients (17 males, 7 females; mean age 25.6 ± 6.3 years, mean height 174 ± 9.0 cm, mean weight 74.7 ± 17.2 kg) were included in the analysis. Knee adduction excursion during landing was significantly affected by visual perturbation (F(2, 46) = 6.55, p = 0.004, η² = 0.019). Post hoc analysis showed that high visual perturbation significantly decreased knee adduction excursion compared to normal vision on the ACLR limb (mean difference 1.499°, SE = 0.388, pBonf = 0.003, Cohen’s d = 0.542). A significant difference was also found between low and high visual perturbation on the ACLR limb (mean difference 1.543°, SE = 0.388, pBonf = 0.002, Cohen’s d = 0.558). No significant changes were observed in the non-operated limb across visual conditions. Conclusions: High visual perturbation significantly altered knee adduction excursion on the ACLR limb, resulting in a shift toward greater knee abduction during landing. No changes were observed in the non-operated limb. These findings support the use of visual perturbation in functional assessment protocols after ACLR to better identify persistent biomechanical deficits that may contribute to reinjury risk. Full article

To counteract accuracy degradation in micrometer-scale precision marking—where the precision marking (PM) system denotes the precision marking platform and the Optical Microscope (OM) system denotes the camera-based visual guidance module—a genetic-algorithm-based framework for motion-system calibration and error control is introduced. A kinematic error model is established to capture multi-source coupled errors in the PM system, and the propagation mechanisms of axis misalignment, pose misregistration, and flatness-induced errors are analyzed. Building on this model, a GA-driven multi-objective calibration scheme and a coordinated optimization model jointly address axis-orthogonality correction, PM-OM extrinsic-pose calibration, and workpiece flatness compensation. Furthermore, a dynamic error-compensation framework leveraging real-time monitoring and adaptive adjustment sustains long-term high-precision marking. In post-calibration tests-after correcting axis orthogonality, aligning the PM-OM extrinsic pose, and compensating workpiece flatness, the PM system achieves dimensional accuracies of ±0.05, ±0.08, and ±0.10 μm for nominal 1, 2, and 3 μm marks, respectively, with positional accuracy better than ±0.2 μm. Marking consistency improves markedly, and the indentation force closely matches the target mark size, validating the approach. These techniques provide both theoretical and practical support for the engineering deployment of PM systems and are significant for improving the quality and productivity of micrometer-scale precision marking. Full article