Search Results (161)

Humanoid robots are highly susceptible to structural damage during irrecoverable falls due to high landing velocity, short impact duration, and high peak impact force. Inspired by human protective strategies, namely instinctive postural adjustment and soft-tissue energy absorption, this paper proposes a passive–active cooperative fall-protection method that combines pre-impact motion regulation with post-impact structural energy absorption. On the passive protection side, high-risk contact regions are identified through multi-directional fall simulations, and a multi-region, multilayer protective suit is optimized considering impact energy absorption, peak-force reduction, anti-bottoming safety, added mass, and thickness constraints. On the active protection side, a variable height inverted pendulum (VHIP) model is used to optimize the center of pressure and center of mass trajectories, reducing the terminal impact energy before ground contact. The residual impact energy is then matched with the absorption capacity of the passive protective layers, forming a unified framework that integrates pre-impact motion unloading and post-impact energy absorption. Numerical validation is performed on a MATLAB–CoppeliaSim co-simulation platform, and physical experiments are conducted on the FCR humanoid robot (approx. 50 kg, 1.65 m, 22 DOF). Compared with the unprotected case, the proposed method reduces the peak equivalent impact force from 4819.1 N to 1038.2 N, i.e., a reduction of 78.5%, demonstrating its effectiveness in attenuating impact loads and enhancing protection capability. Full article

This paper presents the kinematic design and experimental validation of a two-degree-of-freedom ankle mechanism based on stepper motor actuation and ball-screw transmission. The proposed system employs a hybrid architecture, combining actively controlled motion in the sagittal plane with passively compliant motion in the frontal plane. Experimental evaluation under no-load laboratory conditions demonstrated a strong linear relationship between motor steps and joint angle within an operating range of approximately ±22°, with coefficients of determination exceeding 0.97. The results confirm the predictability and repeatability of the kinematic transformation while revealing minor hysteresis effects associated with mechanical transmission. The proposed mechanism is intended as a validation platform for studying motion transformation in multi-DoF (degree-of-freedom) ankle systems, providing a basis for future work on load analysis, torque modeling, and closed-loop control. Full article

Passive magnetic localization for wireless capsule endoscopy degrades under involuntary gastrointestinal motion: displacement toward one sensor layer simultaneously saturates near-side magnetometers while driving far-side sensors below the noise floor, a failure mode we term asymmetric layer visibility. Existing single-layer and multi-layer systems do not model this explicitly, and full-array Jacobian conditioning degrades progressively with displacement even where a layer-conditioned decomposition remains comparatively stable. We present a two-layer magnetometer array with three algorithmic contributions: per-sensor SNR gating excluding saturated or low-SNR sensors before optimization; layer-conditioned Levenberg–Marquardt estimation on a five-dimensional manifold via spherical parameterization; and SNR-weighted fusion with geodesic interpolation on $S^2$, combined with layer-wise ellipsoid calibration and neural network initialization. Eight LIS3MDL tri-axial magnetometers in a $15\times 15\times 30$ cm³ two-layer array were validated across 150 Monte Carlo trials using a 4-DOF robotic arm. Mean positional error was $2.32\pm 0.35$ mm and orientational error $2.15\pm 0.88°$ at 12.4 Hz, with 93.85% convergence success under displacement-induced asymmetric visibility events of 1.00–4.00 cm—over 70% reduction over single-layer and layer-agnostic baselines. Hardware generalization was confirmed across three magnetometer types. The SNR weight ratio also yields a passive discriminant between capsule displacement and global array motion, cross-validated AUC $0.924\pm 0.045$, requiring no additional hardware. Reported accuracy is specific to the LIS3MDL magnetometer used in this study. Full article

High-voltage grid maintenance and chemical processing demand dexterous manipulation in confined, visually occluded environments. Existing teleoperation systems present a severe cost–fidelity trade-off: industrial platforms exceed USD 60,000 yet lack spatial directional resolution, while affordable alternatives omit fingertip force sensing entirely. We present STRP (Spatial Teleoperation Research Platform), a low-cost teleoperation system with concurrent motion capture and directional haptic feedback that encodes fingertip six-axis force vectors into four-directional vibrotactile cues (left, right, dorsal, volar). The master interface is a wearable 15-DoF exoskeleton glove with Hall-effect joint sensing and a 16-channel LRA array; the slave end is a 17-DoF dexterous hand with integrated micro six-axis force/torque sensors. Experimental validation demonstrates 97.5% blind discrimination accuracy for directional cues and an end-to-end latency of approximately 28 ms. In teleoperated pick-and-place tasks, multi-directional tactile feedback reduced task completion time by 41% (31.2 s to 18.4 s) and peak grip force by 32% (4.11 N to 2.79 N) versus no-feedback baselines. In visually occluded wall exploration, directional feedback enabled systematic location of all target walls, whereas single-site volar feedback permitted detection of only volar-facing surfaces and no-feedback conditions precluded any spatial reasoning. These results identify spatial directional vibrotactile feedback as a distinct sensory channel that cannot be substituted by magnitude-only vibration or vision alone. STRP establishes a foundation for affordable, high-fidelity haptic teleoperation in hazardous environments. Full article

Tendon-cable transmission can reduce distal-limb inertia in full-size humanoids, but its elasticity, hysteresis, backlash, and multi-joint coupling introduce state-dependent joint-to-motor discrepancies. We present a hierarchical whole-body tracking framework for the 28-DoF Droid X3 that separates high-level motion learning from transmission compensation. A reference-residual policy is trained in simulation by single-stage proximal policy optimization (PPO) using a unified robot-space motion representation, globally anchored tracking rewards, hierarchical hard-example sampling, and tendon-oriented domain randomization. In simulation checkpoint evaluation, more than 90% of 12,674 tested reference motions are completed. Independently, a state-conditioned mapper is trained offline through a differentiable motor–joint forward model identified from physical motor-excitation data and connected in series between the frozen policy and the low-level motor controller. Randomized repeated Mapping-OFF/ON trials are conducted on two nominally identical Droid X3 units. Within every robot–motion block, the frozen PPO checkpoint, reference trajectory, controller settings, safety bounds, and frozen mapper weights are held fixed; complete trials are the statistical units. OFF converts desired joint positions with the robot-specific fixed static calibration, whereas ON feeds the complete policy-level desired-joint vector and measured plant state to the frozen mapper, which directly outputs the complete motor-position command. Across the complete physical trials, the aggregate action-completion rate is 68% with Mapping OFF and 79% with Mapping ON, an increase of 11 percentage points. Representative walk, squat, and dance trajectories illustrate lower tracking errors under Mapping ON, while individual frames and selected temporal fragments are used only for visualization. Full article

To address the difficulty of simultaneously optimizing execution time, energy consumption, and motion smoothness for six-degrees-of-freedom (6-DOF) industrial robotic manipulators in continuous operations such as high-speed handling and assembly, this study proposes a multi-objective joint-space trajectory optimization method based on an improved Dung Beetle Optimizer (IDBO). First, to adapt DBO to constrained multi-objective trajectory optimization, an external archive, nondominated sorting, and a crowding distance mechanism were incorporated to construct and maintain the Pareto solution set. Second, Sobol low-discrepancy sequence initialization was used to improve the initial population distribution. Adaptive Lévy flight perturbation and an adaptive random perturbation mutation strategy for non-elite individuals were further combined to enhance global exploration and reduce the risk of premature convergence. Finally, seventh-degree B-spline curves were adopted to construct a continuous joint-space trajectory model. Based on this model, a multi-objective trajectory optimization model was established by considering total execution time, energy consumption, and jerk as the optimization objectives. Furthermore, simulation experiments were conducted using MATLAB R2024a, and the proposed algorithm was compared with multi-objective particle swarm optimization (MOPSO), an improved multi-objective differential evolution algorithm (GMODE), the nondominated sorting genetic algorithm II (NSGA-II), and the multi-objective Dung Beetle Optimizer (MODBO). The results showed that the proposed algorithm obtained a Pareto front with better convergence, wider coverage, and a more uniform distribution. Compared with MOPSO, GMODE, NSGA-II, and MODBO, the mean hypervolume (HV) obtained by IDBO was 13.24%, 8.43%, 2.29%, and 2.34% higher, respectively; the mean inverted generational distance (IGD) was 9.45%, 26.68%, 16.35%, and 11.05% lower, respectively; and the mean Spacing value was 55.09%, 64.81%, 58.95%, and 14.06% lower, respectively. The execution time, energy consumption index, and joint jerk of the selected compromise solution were 5.27 s, 2.48, and 9.79, respectively, which were 29.73%, 43.51%, and 18.14% lower than those of the unoptimized trajectory. Constraint verification showed that the peak joint velocities, accelerations, and jerks remained within their prescribed limits. These results indicate that the proposed method provides a feasible approach for multi-objective joint-space trajectory planning of industrial robotic manipulators. Full article

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

Traditional manual 10 kV live-line maintenance is accompanied by high personal risks and incomplete safety protection, while overall operational efficiency is limited. This paper develops an intelligent live-working robot based on a tracked insulated spider aerial vehicle. The system is equipped with vertical lifting modules and a pair of 6-DOF insulated manipulators to form a 13-DOF integrated motion platform. Binocular cameras, LiDAR, real-time insulation monitors, and electromagnetic interference detectors are integrated as multi-modal sensing hardware to achieve high-precision positioning of overhead lines and pole fittings. A master–slave collaborative control strategy combined with mixed reality (MR) and visual auxiliary force feedback is proposed to coordinate the tracked chassis, lifting structure, and dual manipulators. A four-dimensional full-cycle safety guarantee framework is further constructed, covering insulation protection, anti-interference communication, human–machine risk avoidance, and full-task supervision to support real-time early warning and motion interlock. Field tests on actual 10 kV distribution lines verify stable positioning performance under controlled test conditions, and no safety accidents occurred in all trials. The designed robotic system provides an optional technical scheme for intelligent unmanned live-line maintenance of distribution networks. Full article

Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established for an 11-DOF physical actuation system composed of six revolute joints and five prismatic telescopic joints. For inverse kinematics and template matching, the five physical telescopic joints are mapped to two equivalent prismatic variables, whereas collision checking and execution remain in the full physical joint space. Second, an improved bidirectional RRT-Connect planner is developed by integrating goal-biased sampling, multi-candidate expansion, soft low-lift constraints, and combined state and edge validity checking. Third, a pose-library-guided segmented planning strategy is introduced to reuse successful deployment sequences for known targets and to automatically generate intermediate poses for unseen targets. All post-processed trajectories are revalidated for collision and clearance before acceptance. Comparative simulations demonstrate that the proposed framework improves collision-free planning success and suppresses unreasonable high-lift configurations. The framework provides a reproducible planning solution for automated bridge inspection in confined under-bridge environments. Full article

Floating liquefied natural gas (FLNG) facilities integrate natural gas liquefaction, storage, and offloading into a single vessel. During ship-to-ship (STS) side-by-side offloading, an LNG carrier (LNGC) moors alongside the FLNG to transfer liquefied cargo through a loading-arm system. The hydrodynamic interactions between the two vessels, combined with environmental loads, can lead to excessive relative motions that pose a risk of collision or damage to the loading arms and fenders. Accurate short-term prediction of vessel motions would provide operators with advance warning of potentially dangerous conditions, allowing preventive actions to be taken. This study presents a data-driven approach to short-term motion prediction using experimental data obtained from comprehensive basin model tests of an FLNG system. The model tests covered 15 environmental conditions, including survival conditions (100-year return period) and operating conditions (1-year return period), under both single-vessel and side-by-side configurations. Three prediction methods were evaluated: an autoregressive linear model, a single-degree-of-freedom multi-layer perceptron, and a multi-head attention cross-coupling network (MAC-Net) that leverages temporal attention, cross-DOF graph message passing, and multi-task learning with uncertainty-weighted loss. The results show that surge, sway, and yaw can be predicted with high skill scores at model-scale horizons of up to 4 s (32 s full-scale equivalent), while heave and pitch exhibit limited predictability beyond 2 s model scale. The MAC-Net model demonstrates particular advantages for roll prediction, achieving a skill score of 0.88 at a 4 s model-scale horizon compared to 0.76 for the conventional method, attributable to the physical coupling between roll and the horizontal-plane motions through the mooring system. These findings support a practical early warning concept in which horizontal-plane motions provide advance collision alerts and heave/pitch are treated as short-horizon monitoring quantities. Full article

This paper proposes a dynamic model incorporating centrifugal forces and elastohydrodynamic lubrication (EHL) to analyze the vibration characteristics of high-speed rolling bearings with localized outer raceway defects. A four-degree-of-freedom (4-DOF) motion equation is established using Hertzian contact theory and isothermal EHL equations. Numerical solutions incorporating defect-induced time-varying displacement excitations are experimentally and theoretically validated. Results confirm the oil film’s vibration-damping effect, reducing peak acceleration by 11.8% and the root-mean-square (RMS) value by 3.7% compared to dry contact conditions. Higher rotational speeds thin the oil film and reduce comprehensive stiffness, amplifying vibration and impact intensity without altering fault characteristic frequencies, which remain stable with a relative error within 0.5%. As the defect size increases, RMS and peak values rise monotonically, with the RMS acceleration increasing by 69.5% (from 0.6102 m/s² to 1.0344 m/s²) as the outer race defect expands from 0.2 mm to 0.8 mm, while kurtosis peaks and subsequently declines. The dual-impact phenomenon is most prominent under low rotational speed and large defect conditions, providing a basis for a a quantitative fault diagnosis strategy to invert defect size from dual-impact time intervals is proposed and experimentally validated, yielding an inversion error of less than 2% under such favorable conditions. While this inversion method is condition-dependent—with its precision degrading under increased speeds and micro-defect scenarios—it provides an accurate and reliable quantitative tool within its applicable boundaries. The developed dynamic model and multi-index diagnostic approach provide a theoretical basis and practical reference for fault diagnosis, condition monitoring and quantitative defect identification of rotating machinery. Full article

Multi-manipulator cooperative systems are widely deployed in industrial assembly, intelligent manufacturing and other fields, but collision safety and efficient motion coordination during coordinated operation remain key challenges. In this paper, a novel cooperative control strategy based on relative velocity information is derived to guarantee collision-free maneuvers for multiple m-degree-of-freedom (m-DOF) manipulator systems with general Lagrangian dynamics. One key advantage is that it ensures reliable safety while achieving smoother avoidance maneuvers, reduced interference with objective tasks, lower energy consumption, and improved task efficiency; notably, the avoidance control depends not only on the relative distance between manipulators but also on their relative motion, making it less conservative as manipulators avoid unnecessary spreading during collision avoidance. Another is that it integrates collision avoidance, disturbance attenuation, and deadlock elimination into a unified closed-form control law, which yields a closed-form solution and is easy to implement in engineering practice. Theoretically, this paper adopts the generalized Lyapunov stability theory to rigorously prove the asymptotic convergence and persistent collision-free property. Finally, simulation results on a dual two-DOF manipulator system further verify the effectiveness and reliability of the proposed control strategy. Full article

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

Dual 6 degrees of freedom (6-DOF) series-parallel cooperative robot actuators are core execution components in modern intelligent manufacturing systems, which are widely used in high-end manufacturing scenarios such as aerospace precision assembly, laser precision machining, and core component assembly of new energy vehicles. However, in actual manufacturing processes, the pose deviation between theoretical model prediction and actual motion execution of the actuator, caused by kinematic model mismatch, unquantified core parameters, incomplete error processing chain, and complex on-site environmental interference, severely restricts the assembly accuracy, product qualification rate and production efficiency of the manufacturing system. To address these critical pain points of robot actuators in precision manufacturing systems, this paper proposes a four-layer hierarchical decoupling multi-modal fusion calibration algorithm for high-precision pose control of dual series-parallel robot actuators. The algorithm integrates singular value decomposition (SVD) for cross-structure coordinate alignment of heterogeneous actuators, chaotic mapping-enhanced particle swarm optimization (PSO) for nonlinear error suppression of the actuator system, attention-enhanced deep residual network (DRN) for unmodeled residual learning of the actuator, and Kalman filter (KF) for dynamic noise reduction in the manufacturing process. Meanwhile, a full-chain error transfer model of the actuator system in the manufacturing process is constructed, and the core parameters of the algorithm are quantified via dimensional sensitivity analysis and orthogonal experiments. Experimental results show that the static position error of the actuator system after calibration reaches 1.4 ± 0.08 mm, and the static pose error reaches 0.0059 ± 0.0003 rad in the laboratory environment; in the engineering application of laser precision machining in an actual manufacturing line, the position error and pose error only increase by 8.6% and 6.8% respectively, maintaining high stability in industrial manufacturing scenarios. Compared with mainstream calibration methods, the proposed algorithm reduces the position error and pose error of the actuator by up to 55.7% and 17.9% respectively, with lower computational complexity and higher engineering reproducibility. This work constructs an end-to-end error suppression chain with quantitative parameter criteria for the series-parallel actuator system in manufacturing systems, which provides a reliable high-precision calibration solution for industrial dual-robot cooperative manufacturing and has important guiding significance for improving the motion accuracy and operation stability of actuators in precision manufacturing systems. Full article

Dynamic three-dimensional (3-D) reconstruction of small objects moving at high speed is fundamentally limited by the number of viewpoints that a fixed camera array can provide at any single time instant. When the camera count is insufficient, single-frame multi-view stereo produces incomplete or inaccurate geometry. This paper proposes a multi-frame temporal integration approach that overcomes this limitation by exploiting the rigid-body assumption: because a falling object maintains its shape across consecutive frames, images captured at different time instants can be combined into a single, viewpoint-enriched reconstruction. A three-layer circular array of 32 synchronized RGB cameras captures 1440 × 1080 images at 160 fps, and a free-fall-oriented algorithm automatically detects active frames, selects informative temporal windows, and feeds the accumulated multi-frame images into a structure-from-motion and multi-view stereo (SfM-MVS) pipeline, effectively multiplying the number of viewpoints without additional hardware. The algorithm simultaneously recovers the 6-DOF pose trajectory of each object from the SfM-estimated camera parameters. Progressive accumulation experiments on freely falling soybeans (approximately 9–10 mm diameter) show that a single 32-camera frame already achieves an F-score exceeding 0.97 at a 0.5 mm threshold against an industrial structured-light scanner reference, and that accumulating additional temporal frames reaches a stable convergence plateau with both objects reaching a plateau F-score of 0.984. Beyond approximately one to two accumulated frames, additional frames yield diminishing returns, confirming that a small number of temporal frames is sufficient for convergent sub-millimeter accuracy. Across 30 independent free-fall trials with three objects, the system achieves an overall mean error of $0.146\pm 0.033$ mm and an overall F-score of $0.980\pm 0.006$—a mean relative error of approximately 1.6% on 8–10 mm targets—and fine surface features such as structural cracks are resolved at a fidelity sufficient for visual defect identification. These results establish rigid-body multi-frame temporal integration as an effective strategy for high-throughput, non-contact 3-D inspection of small objects in motion. Full article