Search Results (238)

Exoskeletons are widely utilized across various domains, including biomedical and rehabilitative engineering. In clinical applications, precise joint torque evaluation is critical to ensuring exoskeleton efficiency, especially when assisting patients with impaired mobility. This work presents a straightforward inverse-dynamics framework to compute human joint torques using motion capture and force plate data. Estimating these torques is a key requirement for exoskeleton systems to deliver appropriate and individualized assistive support. A key innovation of the proposed model is the explicit integration of a three-link chain—comprising the thigh, shank, and foot—treated as a cohesive multi-segment limb. By formally incorporating the foot segment, the model enables a more rigorous representation of ground reaction forces (GRF) and the dynamic migration of the center of pressure (COP). The proposed framework was validated against OpenSim 4.0 using benchmark datasets involving walking, squatting, and drop-jump maneuvers. The results demonstrated strong agreement with OpenSim, yielding normalized root mean square errors of approximately 10% across major lower-limb joints during walking. In contrast, the squatting posture provided a significant magnitude offset, despite maintaining close temporal phase alignment. Beyond torque estimation, the results provide insight into the sensitive interplay among COP trajectories, foot geometry, and GRF orientation. The proposed framework offers a computationally efficient tool for biomechanical analysis and provides a practical foundation for future lower-limb exoskeleton and assistive robotic applications. Full article

Accurately predicting the remaining useful life (RUL) of UAV bearings is challenging due to maneuver-shock-induced stochastic jumps during their two-phase degradation, while existing numerical methods are computationally too costly for UAV onboard computing. To address this, an analytical RUL prediction method considering stochastic jumps is proposed. A two-phase Wiener process incorporating stochastic jumps is constructed to model degradation processes involving shocks. Subsequently, a combined Kalman Filter–Rauch–Tung–Striebel Smoothing–Expectation Maximization (KF-EM-RTS) framework is developed for simultaneous online updating of drift and diffusion coefficients. Furthermore, utilizing Stein’s Lemma, an analytical expression under a fixed-change-point assumption for the RUL probability density function (PDF) of the proposed model is derived, thereby reducing the reliance on repeated numerical integration. Under the experimental settings used in this study, the analytical implementation reduces the single-point PDF calculation time by approximately 90% compared with the corresponding numerical integration implementation, which is important for compute-limited UAV platforms. Moreover, RMSE is decreased by 48% and 76% versus models ignoring jumps. This approach offers a lightweight solution for real-time predictive maintenance of UAVs. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

Insect aerial righting is a critical locomotor response to flight perturbations. However, the kinematic strategy of dragonfly (Pantala flavescens) free-fall recovery under asymmetric perturbations remains unclear. In this study, free-fall experiments were conducted using single-wing release to induce an initial asymmetric body roll, and three-dimensional kinematics were reconstructed using high-speed videography. The results show that the free-fall recovery process was divided into two phases based on body velocity: a free-fall phase with increasing downward velocity and a recovery phase marked by the cessation of this trend and a rise in horizontal velocity. During the free-fall phase, thorax roll developed and then attenuated, accompanied by relative abdominal and wing posture variations, without sustained wing flapping. Near the onset of recovery, hindwings initiated the downstroke earlier than forewings, forming a hindwing-leading flapping pattern. These findings indicate that dragonflies employ a phased kinematic strategy during asymmetric free-fall recovery, involving early body and wing posture adjustments followed by asymmetric wing flapping during the transition from gravity-dominated descent to maneuvering flight. Full article

Reliable altitude and vertical speed estimation are fundamental for unmanned aerial vehicle (UAV) autonomous flight, especially during low-altitude operations such as takeoff and landing. Barometric altimeters are widely used due to their low cost, high availability, and good long-term stability, providing smooth altitude trends over a wide operating range. However, barometric measurements are indirectly inferred from static pressure and are therefore sensitive to local airflow disturbances. In particular, rotor downwash and ground effect-induced pressure perturbations near the surface can introduce significant biases and short-term fluctuations in barometric altitude, which propagate into erroneous vertical speed estimates during critical flight phases. Time-of-flight (TOF) altimeters, such as radar or laser sensors, provide direct above-ground-level (AGL) measurements and are largely insensitive to ground effect-related pressure disturbances. Within their limited operational range, TOF altimeters typically offer higher accuracy and lower short-term noise compared with barometric altitude. Nevertheless, TOF sensors are characterized by a restricted valid measurement range and frequently exhibit non-ideal behaviors in real-world UAV operations, including out-of-range outputs, frozen measurements, and in-range biased readings. These anomalies violate the nominal sensor assumptions used in conventional Kalman filter-based fusion and can significantly degrade estimation performance if not properly handled. This paper proposes a hybrid Kalman–rule-based altitude estimation framework that fuses barometric and TOF altitude measurements to exploit their complementary characteristics while mitigating their respective limitations. A vertical dynamic state-space model is formulated to jointly estimate altitude, vertical velocity, accelerometer bias, and ground height offset. A rule-based anomaly detection and classification module is developed to identify multiple TOF altimeter failure modes observed in operational UAV flights. The detected anomaly states are incorporated into the Kalman filter to adaptively weight, accept, or reject TOF measurements, thereby improving robustness against sensor non-idealities. The proposed approach is validated using 39 real UAV flight logs covering diverse flight regimes, including low-altitude maneuvers, cruise, and autonomous landing. Experimental results show that the proposed framework provides more stable and robust altitude and vertical speed estimation under practical sensor anomaly conditions compared with conventional barometer-only and standard Kalman fusion configurations. These results demonstrate the practical effectiveness of the proposed method for fault-aware altitude estimation in UAV autonomous flight. Full article

To address tracking accuracy degradation caused by noise in sensor observations, a maneuvering target tracking algorithm based on an improved Received Signal Strength Indicator (RSSI) ranging model is proposed for Wireless Sensor Networks (WSNs). The traditional deterministic ranging model is replaced by a backpropagation neural network optimized via the Osprey Optimization Algorithm (OOA-BP), which directly maps noisy RSSI measurements to precise physical distances. Filtering and tracking are executed using an Extended Kalman Filter (EKF) combined with a uniform circular motion model, demonstrating the robustness of the observation model across dynamic predictions. Simulation results validate the efficacy of the proposed framework. In the distance estimation phase, the OOA-BP model reduces the average ranging error to 0.04 m. During dynamic tracking, the integrated OOA-BP-EKF architecture demonstrates superior tracking performance compared to standard frameworks, reducing the Root Mean Square Error (RMSE) by 15.33% and 59.89% compared to GA-BP and standard BP algorithms, respectively. Full article

In the autonomous berthing of ultra-large under-actuated vessels, the combined effects of low-speed maneuvering and shallow water conditions introduce strong nonlinear hydrodynamic characteristics which cannot be accurately captured by conventional linear models. To address this issue, a nonlinear maneuvering model is adopted to more precisely describe vessel dynamics. A 300 m class ultra-large tanker, Esso Bernicia (190,000 dwt), is selected as the case study to ensure the applicability of the proposed method to vessels with high inertia and limited maneuverability. Furthermore, to better reflect realistic berthing conditions, environmental disturbances and hydrodynamic parameters are modeled based on the experiments and statistical analysis conducted by OCIMF, and shallow water effects are incorporated to account for increased resistance in confined port areas.In contrast to existing studies that rely solely on tug assistance, this work integrates propeller–rudder control of the vessel into the berthing process. The coordinated use of onboard propulsion and tug forces significantly enhances maneuverability and operational efficiency. However, these modeling improvements result in a centralized multi-input–multi-output (MIMO) nonlinear system with increased complexity, higher-dimensional control inputs, and stronger coupling effects, posing significant challenges for control design. To address these challenges, a phased berthing control strategy is proposed. The overall multi-objective optimization problem is decomposed into stage-wise sub-problems to reduce computational complexity. In addition, a tailored berthing trajectory and stage-dependent cost functions are designed to facilitate convergence and improve computational efficiency. Simulation results demonstrate that the proposed method achieves safe, stable, and efficient autonomous berthing, with improved convergence performance and enhanced control effectiveness under complex environmental conditions. Full article

The increasing operational variability in distribution networks (e.g., abrupt load changes and distributed generation integration) increases the demands on voltage regulation devices and, in particular, on transformers with on-load tap changers (OLTCs). This paper develops and validates a discrete supervisory control scheme based on Petri nets, implemented in Stateflow and coupled to an electromagnetic model of the OLTC transformer in Simulink/Simscape. The Petri net formalizes the conditional and sequential logic of OLTC operation, enabling state- and time-dependent decisions (e.g., delays between maneuvers) to improve voltage regulation and reduce unnecessary tap operations. The evaluation is performed by simulation under transient scenarios that include sudden load variations anda phase-to-ground fault in the IEEE 13-node standard network, specifically at node 634. In the base case, the controller maintains the voltage within the tolerance band $\pm 1.875\%$ during 96% of the simulated time, with an 88% reduction in RMS error (from 1.92% to 0.23%) and 100% operational efficiency (16 effective maneuvers, with a single hunting event). Subsequently, the scheme is validated on the standard IEEE 13-node network, with four disturbances applied over 600 s (two load increments, photovoltaic injection, and a temporary line disconnection). In this case, regulation remains within a precision zone of $\pm 0.3\%$ for 96.8% of the time, with an average RMS error of 0.23% and 100% efficiency, with no hunting events. The results confirm that a Petri net-based supervisory logic can simultaneously improve the OLTC’s voltage quality and switching efficiency, providing a reproducible alternative for distribution network automation. Full article

The safe and efficient operation of deep-sea towing systems is heavily governed by the highly nonlinear dynamic interaction between the flexible towing cable and complex seabed topographies. While existing studies accurately predict cable dynamics in mid-water or over flat seabeds, the transient responses—such as local stress concentrations and extreme tension fluctuations—induced by discontinuous topographies (e.g., stepped or 3D irregular seabeds) remain inadequately quantified. In this study, we develop an advanced 3D dynamic numerical model combining the lumped-mass finite element formulation with a modified non-linear penalty-based seabed-contact mechanics algorithm. This framework systematically evaluates the tension distribution, bending curvature, and spatial configuration shifts in the cable during the touchdown and detachment phases across inclined, stepped, and 3D seabeds. Quantitative validation against established benchmarks demonstrates robust accuracy. Results indicate that steeper seabed inclinations linearly reduce detachment time but exponentially amplify initial contact tension. Over-stepped terrains, “point-to-line” transient collisions trigger sudden tension spikes exceeding steady-state values by up to 45%. Furthermore, 3D irregular seabeds induce severe multi-directional spatial deformations, precipitating destructive whiplash effects at high towing speeds (e.g., V > 2.2 m/s). These findings provide critical physical insights and a quantitative reference for optimizing tugboat maneuvering strategies and designing fatigue-resistant cables in complex sub-sea environments. Full article

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next $\tau =0.2$ s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor ($w=d=0.1$ s, $k=2$) is warnable on only $18.3\%$ of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget $\alpha$ with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at $\tau =0.2$ s, the scorer achieves AUPRC $\approx 0.251$ against a base rate of $0.638\%$, AUROC $\approx 0.986$, and ECE $\approx 0.034$. After calibration at $\alpha =5\%$, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR_run, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported $\mu$-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

Low-level flight or maneuvering defines a flight phase that is particularly common and, in some cases, central to helicopter operations, but brings several safety concerns. At low altitude, helicopters are more susceptible to collisions with objects, while there is also limited time and space in which to perform an emergency landing. A total of 403 helicopter accidents in the low-level flight phase that occurred between 1 January 2009 and 31 December 2022 in the US were analyzed for their most common causes and differentiated based on the type of flight operation to gain insight into low-level flight accidents. It is shown that, for low-level flights, the proportion of fatal accidents in flights conducted under Federal Aviation Regulations Part 91, General Aviation, is 30%, but in flights conducted under Part 137, aerial application or agricultural flights, only 12%. Logistic regression analysis shows that while controlling for other factors, the proportion of fatal accidents was significantly higher in Part 91 operations. Flight experience measured as total flight hours was not a significant factor for estimating fatality. It is recommended that low-level helicopter training includes low-altitude autorotations in simulators to optimize the mitigating effect of this emergency procedure in this flight phase with a specific focus on Part 91 operations. Full article

Background/Objectives: Biomechanical research in surfing provides important insights into performance optimization and injury prevention, but the evidence remains fragmented across multiple domains. Methods: This scoping review aimed to systematically organize the existing literature on surfing biomechanics and evaluate the quality of the included studies. Searches were conducted by two independent reviewers in PubMed, Scopus, and Web of Science in accordance with the PRISMA Extension for Scoping Reviews. Systematic searches were performed up to 31 July 2025 using Boolean operators guided by the PECO framework. Methodological quality was assessed using the Downs and Black Quality Assessment Checklist. Results: Of the 195 records identified, 53 duplicates were removed. Following screening and fulltext review, 26 studies were included. Five studies employed randomized controlled designs, while 21 were non-randomized. Publications ranged from 2010 to 2025, with the majority conducted in Australia (65.4%). A total of 490 healthy surfers (mean age: 22.9 ± 16.1 years) were analyzed, with sample sizes ranging from 6 to 42 participants. Research topics included anthropometry, paddling biomechanics, aerial maneuvers, core and trunk strength and mobility, lower-limb function, frontside bottom turns, and pop-up performance. The studies’ methodological quality score was 11.7 points with substantial inter-reviewer agreement (κ = 0.77). Research on surf biomechanics remains limited in volume and exhibits methodological heterogeneity. Conclusions: Although existing studies provide valuable insights into key performance actions, further high-quality and standardized research on performance phases (e.g., paddling, pop-up, turns, aerials) and with different research designs (e.g., longitudinal, sex inclusive, ecological designs integrating lab and in-water measures) is needed. Full article

Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and dynamic symmetry, real wind disturbances are often asymmetric, disrupting the original balance and leading to intensified attitude oscillations, position drift, and degraded data quality. To effectively address the challenges of wind-induced oscillation and positional deviation, this paper proposes a fuzzy logic-based linear active disturbance rejection control (Fuzzy-LADRC) strategy. This approach employs a hybrid algorithm combining particle swarm optimization and gray wolf optimization to optimize controller parameters and incorporates fuzzy logic to enhance the adaptive capability of the linear active disturbance rejection controller (LADRC). Simulation experiments conducted in MATLAB/Simulink under complex wind-field conditions demonstrate that the proposed method significantly outperforms traditional PID controllers: in the regulation of roll and pitch angles, control performance improves by approximately 5%, while in yaw angle control, the improvement reaches up to 30%. Furthermore, this method can significantly suppress position deviation and fluctuation in the X and Y directions, and reduce the overshoot in the Z-axis during the UAV’s takeoff phase by 75%. Full article

This paper presents robotic manipulation methods for rapid high-arc object transfer using dynamic, non-prehensile interactions. Two complementary techniques are introduced, two-fingered scoop-and-flick and one-fingered topple-and-flick, designed for objects with low and high centers of mass, respectively. Both methods enable a robot to retrieve objects resting on a surface and launch them into controlled projectile trajectories without requiring stable grasp formation. To support these maneuvers, we develop physics-based models of object acquisition and release, and combine them with a data-driven framework. While analytical modeling guides the acquisition phase, the highly nonlinear flicking dynamics are captured using learned predictive models that enable accurate selection of control parameters for desired trajectories. The proposed techniques enable dynamic object transfer, reduced grasp planning complexity, and adaptability to environmental constraints. Experiments conducted on a custom robotic platform demonstrate reliable and accurate high-arc object transfer, in which the majority of object displacement is achieved through projectile motion. Full article

Autonomous vegetation management in unstructured forest environments imposes a conflicting requirement: maximizing wide-area coverage while maintaining close-proximity safety around irregular obstacles. Conventional repulsion-based avoidance methods often fail to meet mowing requirements by prematurely steering robots away from target trees, resulting in significant unmowed gaps. To address this limitation, this paper proposes a Hybrid Path Planning (HPP) framework that combines a shared global Boustrophedon coverage scaffold with a local orbital maneuvering strategy inspired by celestial two-body dynamics. Rather than redefining the full environment model, the proposed method treats the currently active tree as the dominant local interaction center and generates orbit-like trunk-proximal motion around it. A variable virtual mass model is introduced so that the local attraction weakens as mowing progresses, thereby supporting transition to a rejoining phase governed by a finite state machine (FSM). MATLAB simulations indicate that the proposed framework can improve the trade-off among near-tree coverage, clearance preservation, and trajectory continuity relative to repulsion-centered local-avoidance baselines under the same global traversal scaffold. Full article