Search Results (112)

Kinematic modeling and parameter identification are essential for achieving high-precision robot calibration. A widely used strategy involves utilizing the end-effector position error for parameter identification. However, the strong coupling between length and angular parameters often impedes calibration accuracy. In addition, substantial differences in their scales further exacerbate this issue. To overcome these limitations, following the variable projection method, this paper reformulates the conventional Modified Denavit–Hartenberg (MDH) model into a separable nonlinear structure. This allows independent identification of the two parameter types. Non-geometric errors such as joint compliance and backlash are also explicitly taken into account. The backlash errors are separated from the angular positions of each joint by modeling their bidirectional positioning errors with Chebyshev polynomials. This method enables the establishment of a comprehensive positioning error model to mitigate the influence of backlash errors. Based on the variable projection method, an improved variable projection with modified Gram–Schmidt (IVPMGS) identification method is proposed, which also eliminates redundant parameters that hinder identification robustness. Simulations indicate that the proposed method achieves faster convergence and higher identification accuracy. Compensation experiments demonstrate that the average absolute positioning error is reduced from 0.1804 mm to 0.0917 mm compared with the traditional MDH model, corresponding to a 49.17% improvement in positioning accuracy. These findings confirm the accuracy and effectiveness of the proposed approach. Full article

Soft robots have attracted extensive attention owing to their high flexibility. Inflatable membrane tubes offer lightweight and safe environmental interaction, and external shaping actuators have further expanded their applicability. However, modeling such rigid-flexible gas coupled systems remains challenging due to the internal pressure, external loads, and complex deformations including bending, indentation, and wrinkling. To address curvature variation caused by tube deformation hysteresis, this study presents a static model based on virtual work and a segmented approach for inflatable robots. In the actuator unit, the irregular curvature variation and centerline deviation are quantified. In the cantilever unit, the effective bending moment, as well as the wrinkling and failure criteria are derived. The post-buckling deflection equation characterizes the abrupt curvature variation at the tube root caused by the local wrinkling and collapse. A multi-sensor experimental platform is conducted. The experimental results show that the proposed models achieve superior performance in static parameter identification and kinematic prediction. The bending torque error is below 7%, and the tip position error is less than 5% within the bending angle range of 0° to 100°, which confirm that the proposed models accurately predict the coupled deformation and provide a theoretical basis for the precise control of rigid–flexible gas coupled systems. Full article

Cable-driven minimally invasive surgical robots suffer from significant motion inaccuracies due to nonlinear transmission effects such as friction, elasticity, and hysteresis. These factors lead to strong nonlinear and direction-dependent behaviors, making accurate modeling and compensation challenging. To address this issue, this study investigates the error characteristics of a cable-driven surgical robot prototype based on its structural features. A kinematic model is first established, and geometric errors are corrected through Denavit–Hartenberg (DH) parameter identification using a least-squares method. To further characterize nonlinear effects, the LuGre friction model and equivalent stiffness theory are introduced to analyze friction and cable deformation behaviors. Since physics-based models alone cannot accurately capture the coupled nonlinear errors, a radial basis function (RBF) neural network is employed to approximate the residual errors. To enable real-time implementation, the predicted errors are further simplified using equivalent polynomial functions for efficient compensation. Experimental results demonstrate that the proposed method significantly improves the motion accuracy of the cable-driven system, effectively reducing both tracking error and hysteresis effects. By integrating mechanism-based modeling with data-driven compensation, this approach provides a practical and effective solution for precision enhancement in cable-driven surgical robotic systems. Full article

This paper proposes an adaptive residual extended Kalman filter method optimized by a multi-strategy improved parrot optimization algorithm (MIPO-ARKEKF) to improve the kinematic parameter calibration accuracy and efficiency of robotic polishing systems. To address the limitations of the standard extended Kalman filter (EKF), such as truncation-error accumulation during repeated linearization and sensitivity to manually selected noise parameters, an integrated improvement framework is developed. Specifically, a gradient stabilizer based on state-estimation increments is introduced to alleviate estimation degradation caused by accumulated truncation errors, while the proposed MIPO algorithm is employed to adaptively optimize the process and measurement noise covariance matrices, thereby improving the robustness of parameter identification under practical measurement uncertainty. The calibration process is established on the basis of high-precision external measurement data obtained from the robotic polishing system. In benchmark-function tests, MIPO demonstrates superior convergence performance. In physical experiments based on a KUKA KR210 R2700 robot, the proposed MIPO-ARKEKF method reduces the root mean square positioning error from 0.8927 mm to 0.4858 mm, corresponding to a 45.58% improvement in accuracy. Compared with representative hybrid calibration methods, the proposed method achieves comparable compensation accuracy while reducing computation time by 34.88% to 65.08%. Practical polishing experiments on ultra-low-expansion glass lenses further verify that the proposed method effectively improves end-effector trajectory tracking accuracy and polishing quality, providing an efficient solution for high-precision robotic polishing. Full article

Ship trajectory prediction from Automatic Identification System (AIS) data has been predominantly approached as a time-series forecasting problem, where sequential models operate on coordinate sequences to predict future positions. This paradigm, while effective, neglects a key observation: the spatial layout of multiple vessel trajectories on a chart-like plane carries rich interaction information that is difficult to capture through sequential processing alone. To address this, Mapex (Map Exploitation) is proposed as a vision-based framework that rasterizes multi-vessel AIS trajectories into chart-like multi-channel images and processes them with a visual encoder, treating trajectory prediction as a map-reading task. Each vessel contributes three image channels encoding its trajectory heatmap, speed field, and heading field, converting raw coordinates into a spatial representation where physical movement patterns become visually apparent. A parallel coordinate branch supplies the course-over-ground information that the raster does not encode explicitly, and a fusion module combines both streams for autoregressive five-channel trajectory generation. Unlike coordinate-domain models that process position sequences numerically, Mapex understands vessel motion through its spatial layout, capturing relative positions, trajectory shapes, and kinematic patterns as visual features rather than abstract number sequences. Experiments on the Piraeus AIS dataset demonstrate that Mapex reduces the average displacement error (ADE) by approximately $68\%$ compared to the best coordinate-domain baseline and the mean squared error (MSE) by over $80\%$ compared to the strongest prior method, while requiring significantly fewer parameters than recent LLM-based approaches. These results suggest that spatial visualization of trajectories provides a fundamentally richer representation than coordinate sequences for multi-vessel trajectory prediction. Full article

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

This paper presents a theoretical framework for predicting molten jet breakup at the outlet of a rotating granulation system operating without forced excitation. The study focuses on the critical regime in which mechanical excitation is absent, and jet disintegration is governed solely by intrinsic hydrodynamic instabilities. The analysis is based on the linear stability theory of viscous liquid jets, employing the Rayleigh–Plateau and Tomotika approaches adapted to melt conditions typical of industrial granulation processes. The Navier–Stokes equations are formulated in a cylindrical coordinate system for an axisymmetric, incompressible viscous jet with appropriate kinematic and dynamic boundary conditions at the free surface. The breakup mechanism is characterized using key dimensionless parameters, including the Ohnesorge, Weber, Reynolds, and Capillary numbers, enabling identification of the dominant instability regime. Analytical expressions are derived for the most unstable wavelength, perturbation growth rate, breakup time, and characteristic droplet diameter. These relationships are evaluated for representative thermophysical properties of molten urea. Theoretical predictions obtained from classical Rayleigh theory, viscosity-corrected models, and modern empirical correlations show strong agreement, with deviations not exceeding 7%. Sensitivity analysis indicates limited dependence of the predicted droplet diameter on moderate variations in viscosity, surface tension, and jet velocity. The proposed model provides a physically grounded basis for predicting and controlling granule size distribution in rotating granulation systems operating without external mechanical excitation. Full article

Background: Knee osteoarthritis (KOA) is a major cause of pain, mobility limitation, and increased fall risk among older adults. Gait dysfunction, characterized by spatiotemporal and kinematic alterations, is a key functional consequence of KOA. While sagittal-plane gait deviations are well-established, multiplanar kinematic changes—particularly in the frontal and transverse planes—remain less clearly understood. This study aimed to compare three-dimensional gait characteristics between older adults with and without KOA. Methods: Ninety older adults (45 with KOA and 45 controls) completed gait assessments using a VICON™ motion capture system. Participants walked at a self-selected speed along a straight walkway without turning movements during data collection. Spatiotemporal parameters and lower-limb joint kinematics (hip, knee, and ankle) were recorded during key gait phases: initial contact, mid-stance, toe-off, and mid-swing. Group comparisons were performed using independent t-tests with statistical significance set at p < 0.05. Results: Compared with controls, participants with KOA demonstrated significantly slower gait velocity (p = 0.001), reduced cadence (p = 0.020), shorter stride length (p = 0.011), increased step time (p = 0.006), prolonged double support time (p = 0.009), and reduced single support time (p = 0.012). Kinematic analysis revealed greater knee adduction at initial contact (p = 0.001), reduced hip adduction (p = 0.002) and greater knee adduction (p = 0.003) during mid-stance, and increased ankle plantarflexion at toe-off (p = 0.004) in the KOA group. No significant between-group differences were observed during the mid-swing phase. Conclusions: Older adults with KOA exhibit distinct spatiotemporal and multiplanar kinematic gait alterations, particularly during weight-bearing phases. These changes may reflect adaptive gait patterns associated with joint dysfunction rather than definitive compensatory mechanisms. Three-dimensional gait analysis may provide valuable biomechanical insights to support early identification of mobility impairments and inform targeted rehabilitation planning in individuals with KOA. Full article

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are treated as a single co-evolving decision vector. In the offline phase, a Dual-Strategy Adaptive Differential Evolution (DS-ADE) optimizer performs global joint identification using complementary exploration–exploitation behaviors and success-history inheritance, analogous to morphology-control co-evolution in biological systems. In the online phase, a Neuro-Physical Hybrid Jacobian (NPHJ) solver augments the analytical Jacobian with gradients from a Graph Kolmogorov–Arnold Network (GKAN), enabling sensorimotor-like real-time compensation on the learned physical manifold. Experiments on an ABB IRB 120 manipulator with 600 configurations (500 training, 100 testing) report a testing distance-residual RMSE of 0.62 mm, STD of 0.59 mm, and MAX of 0.83 mm. Relative to the uncalibrated baseline, RMSE is reduced by 86.75%; compared with the strongest published baseline, RMSE improves by 23.46%. Ablation results show that joint DS-ADE optimization outperforms a sequential pipeline by 32.6%, and the graph-structured KAN outperforms a parameter-matched MLP by 26.2%. Wilcoxon signed-rank tests ($p<0.001$) confirm statistical significance. Full article

Prediction of fatigue failure in concrete structures remains a major challenge due to progressive material degradation. Reliable prediction, therefore, requires modeling the 3D heterogeneous microstructure of concrete to explain the underlying mechanisms governing fatigue failure. While such mesoscale models can reliably predict the fatigue-induced fracture mechanisms, the identification of the associated material parameters remains a significant challenge due to the high-dimensional parameter space introduced by the model. The key challenge addressed in this study is to capture microcrack initiation and coalescence under fatigue loading, using a model capable of representing fracture process: crack initiation, crack propagation, and final failure. Firstly, concrete domain is discretized into Voronoi cells, enabling explicit representation of aggregates and mortar by randomly assigning cohesive links connecting Voronoi cells as aggregates and mortar. After this, mortar links are modeled as coupled damage–plasticity 3D Timoshenko beam elements with nonlinear kinematic hardening and isotropic softening introduced using embedded discontinuity formulation, enabling fracture Modes I–III, whereas aggregate links are modeled as elastic 3D Timoshenko beam elements. The model efficiency is additionally reinforced by using surrogate model approach, with corresponding material parameter identification carried out by multi-objective Bayesian optimization framework to reproduce experimental results. The performance of the proposed model is illustrated by reproducing experimental results obtained from concrete cube compression test and three-point bending test under low-cycle fatigue loading, where the errors between experimental and numerical results are reduced by 82% (stress) and 88% (energy) for the cube test and by 86% (force) and 93% (energy) for the bending test, relative to the initial dataset error. Full article

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article

Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators. Full article

Guided by principles of symmetry to achieve a proper balance among model consistency, accuracy, and complexity, this paper proposes a new approach for identifying the unknown parameters of nonlinear one-degree-of-freedom mechanical systems using nonlinear regression methods. To this end, the steps followed in this study can be summarized as follows. Firstly, given a proper set of input time histories and a virtual model with all parameters known, the dynamic response of the mechanical system of interest, used as output data, is evaluated using a numerical integration scheme, such as the classical explicit fixed-step fourth-order Runge–Kutta method. Secondly, the numerical values of the unknown parameters are estimated using the Levenberg–Marquardt nonlinear regression algorithm based on these inputs and outputs. To demonstrate the effectiveness of the proposed approach through numerical experiments, two benchmark problems are considered, namely a mass-spring-damper system and a simple pendulum-damper system. In both mechanical systems, viscous damping is included at the kinematic joints, whereas dry friction between the bodies and the ground is accounted for and modeled using the Coulomb friction force model. While the source of nonlinearity is the frictional interaction alone in the first benchmark problem, the finite rotation of the pendulum introduces geometric nonlinearity, in addition to the frictional interaction, in the second benchmark problem. To ensure symmetry in explaining model behavior and the interpretability of numerical results, the analysis presented in this paper utilizes five different input functions to validate the proposed method, representing the initial phase of ongoing research aimed at applying this identification procedure to more complex mechanical systems, such as multibody and robotic systems. The numerical results from this research demonstrate that the proposed approach effectively identifies the unknown parameters in both benchmark problems, even in the presence of nonlinear, time-varying external input actions. Full article

Background/Objectives: Running is among the most prevalent forms of physical activity in preschool-aged children and constitutes a fundamental component for the effective execution of other motor patterns. The main aim of this study is to determine how fundamental running parameters change with age and whether there are differences between sexes. Methods: Four-hundred and five pre-school children with the mean (SD) age = 4.9 (1.1) years, height = 111.2 (9.3) cm, weight = 20.0 (4.2) kg, 53.5% girls were recruited from 34 kindergartens in four major cities. The inclusion criteria involved children aged 3–6 years with typical development and without any locomotor or mental disorders and diseases, who were enrolled in day care. Running performance was assessed in preschool children using a 10-m sprint test. Sprint parameters were measured with the OptoJump modular system, an infrared platform that accurately quantifies kinematic variables. Sex (boys vs. girls) and age (3 to 6 years old) differences were calculated by using analysis of variance (ANOVA) or Kruskal–Wallis H-test with post hoc comparison test between the groups. Results: In general, the results indicated that statistically significant differences between boys and girls were observed across the following levels: (1) temporal–kinematic step phase, (2) spatiotemporal movement characteristics, and (3) propulsive phase as an indicator of muscular activity. However, these differences were not consistent across all age groups. Conclusions: This study provides new insights into the spatiotemporal characteristics of running in preschool-aged children. The findings may assist in the early identification of potential motor deviations and in the planning of more effective strategies to promote physical activity during the preschool period. Full article

Flapping-wing micro-air vehicles (FWMAVs) exhibit unique aerodynamic characteristics that differ fundamentally from other aircraft, yet little is known about their dynamic stability derivatives. This study aims to identify pitch-rate stability derivatives of an in-house prototype, CKopter-1, to advance the modeling and control of bio-inspired flight. Experiments were conducted using a robotic-arm fan-array system that enabled prescribed pitching motions under controlled inflow. Aerodynamic forces and moments were measured with a six-axis load cell, while vehicle kinematics were captured using motion tracking and synchronized during post-processing. Tests consisted of quasi-static cycles and dynamic cycles at pitch rates of 35°/s, 58.8°/s, and 68.4°/s. The results revealed static instability below an angle of attack of 33°, a trim condition near 58.5°, and positive stability up to 72.5°. Dynamic cases showed clear pitch-rate effects in the longitudinal components, from which the derivatives were extracted. A comparison with previous studies confirmed comparable magnitudes, with systematic differences attributable to wing dihedral and tail length. This study demonstrates that the fan-array robotic-arm method enables stability derivative identification even beyond feasible flight regimes, providing valuable parameters for future flight dynamics modeling and control of FWMAVs. Full article