Search Results (174)

The dynamic model of a wheeled wall-climbing robot exhibits stage-specific changes when traversing different types of obstacles and during various stages of obstacle negotiation. Previous studies often employed remote control methods for obstacle-crossing control, which fail to dynamically adjust the torque distribution of magnetic wheels in response to real-time changes in the dynamic model. This limitation makes it challenging to precisely control the robot’s speed and attitude angles during the obstacle-crossing process. To address this issue, this paper first establishes a staged dynamic model for the wall-climbing robot under typical obstacle-crossing scenarios, including steps, 90° concave corners, 90° convex corners, and thin plates. Secondly, an adaptive controller based on a radial basis function neural network (RBFNN) is designed to effectively compensate for variations and uncertainties during the obstacle-crossing process. Finally, comparative simulations and physical experiments demonstrate the effectiveness of the proposed method. The experimental results show that this method can quickly respond to the dynamic changes in the model and accurately track the trajectory, thereby improving the control precision and stability during the obstacle-crossing process. Full article

This work presents a novel type of soft reconfigurable mobile robot with multimodal locomotion, which is created using a controllable magneto-elastica-reinforced composite elastomer. The rope motor-driven method is employed to modulate magnetics–mechanics coupling effects and enable the magneto-elastica-reinforced elastomer actuator to produce controllable deformations. Furthermore, the 3D-printed magneto-elastica-reinforced elastomer actuators are assembled into several typical robotic patterns: linear configuration, parallel configuration, and triangular configuration. As a proof of concept, a few of the basic locomotive modes are demonstrated including squirming-type crawling at a speed of 1.11 $\mathrm{m}\mathrm{m}/\mathrm{s}$, crawling with turning functions at a speed of 1.11 $\mathrm{m}\mathrm{m}/\mathrm{s}$, and omnidirectional crawling at a speed of 1.25 $\mathrm{m}\mathrm{m}/\mathrm{s}$. Notably, the embedded magnetic balls produce magnetic adhesion on the ferromagnetic surfaces, which enables the soft mobile robot to climb upside-down on ferromagnetic curved surfaces. In the experiment, the inverted ceiling-based inverted crawling speed is 2.17 $\mathrm{m}\mathrm{m}/\mathrm{s}$, and the inverted freeform surface-based inverted crawling speed is 3.40 $\mathrm{m}\mathrm{m}/\mathrm{s}$. As indicated by the experimental results, the proposed robot has the advantages of a simple structure, low cost, reconfigurable multimodal motion ability, and so on, and has potential application in the inspection of high-value assets and operations in confined environments. Full article

The research and development of the next generation of supersonic transports (SSTs) meets economic and environmental problems. An SST encounters critical challenges, including the need for low fuel consumption, low noise, and low gas emissions. Currently, the feasibility of developing SSTs is increasing through the application of cutting-edge technologies, such as composite materials, advanced electric systems, sustainable aviation fuel, and innovative design methodologies. The object of this study was to perform the conceptual design of a 50-seat supersonic transport utilizing general conceptual design methods. In estimating weight and flight performance, statistical formulae were correlated with data from civil supersonic and subsonic jet transports. For wing sizing, carpet plots were created to explore the optimal combination of wing aspect ratio and wing loading. The results suggested that by utilizing advanced technologies, such as the use of a composite material for the structure, the maximum takeoff weight can potentially be reduced while still meeting design requirements. The constraint of climb gradient largely affects the maximum takeoff weight, and it is anticipated that flight performance at low speeds will be improved. Full article

An aircraft that has been carefully optimized for a single flight condition will tend to perform poorly at other flight conditions. For aircraft such as long-haul airliners, this is not necessarily a problem, since the cruise condition so heavily dominates a typical mission. However, other aircraft, such as Unmanned Aerial Vehicles (UAVs), may be expected to perform well at a wide range of flight conditions. Morphing systems may be a solution to this problem, as they allow the aircraft to adapt its shape to produce optimum performance at each flight condition. This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. By employing PCA, the geometric dimensionality of airfoil surfaces is reduced from 24 to 18 design variables while preserving 100% shape fidelity, thus establishing a compressed morphing parameterization space. A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. Additionally, Shapley Additive Explanation (SHAP) analysis reveals interpretable correlations between principal component modes and aerodynamic performances. Experimental results show that the optimized airfoil achieves a 54.66% increase in low-speed cruise lift-to-drag ratio and 10.90% higher climb lift compared to the baseline. Overall, the proposed framework not only enhances the adaptability of morphing airfoils across various low-speed flight conditions but also facilitates targeted surrogate refinement and efficient data acquisition in high-performance regions. Full article

Background: The ankle joint plays a key role in stabilizing the lower limb during interaction with ground reaction forces. Instability can result in pain, weakness, and impaired movement. Although assessing ankle stability is important, few studies examine existing in vivo methodologies for dynamic load assessment, limiting effective injury management. Objective: To identify in vivo techniques using objective measurement tools for assessing ankle stability during dynamic exercise. Methods: A scoping review was performed based on PRISMA-ScR criteria. Five databases—PubMed, PEDro, Embase, SPORTDiscus, and CDSR—were searched from inception to September 2024. Results: Out of 1678 records, 32 studies met the inclusion criteria. A total of 1142 subjects were included: 293 females (25.6%), 819 males (71.7%), and 30 unspecified (2.62%). Six categories of dynamic exercise were identified: analytical, functional, balance, stair climbing, running, and walking. The techniques used included 3D motion capture, force and pressure platforms, dynamometry, electromyography, accelerometers, pressure and speed sensors, instrumented treadmills, and inertial measurement units. Conclusions: The 3D motion capture systems (240 Hz) and the force platforms (1000 Hz) were most frequently used in functional tasks and walking. Combining these with multisegmented foot models appears optimal, though tool selection depends on study goals. This review enhances our understanding of ankle stability assessment. Full article

There is a poor mechanization level among the existing chili seed extractors. The separation operation still relies on manual labor, with low efficiency and high costs. In this study, a fresh chili seed extractor for small-scale operations was designed, and the relevant parameters were optimized. The rotational speed of the drum, feeding speed, sieve diameter, threshing gap, number of peg teeth, and inclination angle of the frame were used as test factors, and the comprehensive score (loss rate, crushing rate, and impurity rate) of the effect of the chili seed extractor was set as an evaluation index. The initial parameters were selected via the Plackett–Burman test. The steepest climb test was carried out to determine the ranges of significance for the parameters. Moreover, a Box–Behnken test were conducted to obtain the optimal parameter combination: the drum rotation speed was 661 r/min, the sieve diameter was 8.5 mm, and the disengagement gap was 9.4 mm. The test results showed that the loss rate was 3.83%, the crushing rate was 2.01%, and the impurity rate was 11.31%, which met the actual production requirements for chili seeds. This study is expected to provide a necessary reference for the design of chili seed extractors. Full article

This paper presents a methodology for optimizing an aeronautical propeller to minimize power consumption. A multi-objective approach using blade element momentum (BEM) theory and evolutionary algorithms is employed to optimize propeller design by minimizing power consumption during takeoff and top-of-climb. Three different evolutionary algorithms generated a Pareto front, from which the optimal propeller design is selected. The selected propeller design is evaluated under optimal operational conditions for a specific mission. In this context, two operational approaches for the optimized propellers during flight missions are evaluated. The first approach considers the possibility of only three values for the propeller rotation, while the second allows continuous changes in the rotational speed and pitch angle values, known as the multi-rotational-speed approach. In the second approach, a modal analysis of the propeller is performed using rotating beam theory. The natural frequencies of vibration, constrained by the Campbell diagram, enable an operational analysis and ensure structural integrity by preventing resonance between propeller blades and the rotational procedures. The multi-rotational approach is conducted with and without frequency constraints, resulting in general flight energy reductions of 1.40% and 1.47%, respectively. However, substantial power savings are achieved, namely up to $10\%$ during critical flight states, which can have a significant impact on future engine design and operability. The main contributions of the research lie in analyzing the multi-rotational approach with vibrational constraints of the optimized propeller. This research advances sustainable aviation practices by focusing on reducing power consumption while maintaining performance. Full article

Icing poses a serious threat to flight safety, and ice accretion simulations are essential for addressing aircraft icing problems. In ice accretion prediction, systematic research covering all icing conditions based on actual flight phases is lacking, and the performance of anti-icing systems has not been investigated. In this study, maximum ice thickness prediction models for airfoils considering all flight phases were developed, and the performance of hot air anti-icing systems was analyzed. A hot air anti-icing system model was established, and the anti-icing effectiveness of the system under severe icing conditions was evaluated via conjugate heat transfer (CHT) calculations. The calculation results showed that during climbing above 10,000 ft under glaze ice conditions, the maximum ice thickness reached 13.47 mm at −6 °C, with a median volumetric diameter (MVD) of 20 μm. Under rime ice conditions, the maximum thickness exhibited linear relationships with the icing parameters, remaining below 5 mm. The calculation results revealed nonlinear relationships between maximum ice thickness on the airfoil leading edge and the icing conditions. Ice thickness models were established via polynomial regression. The maximum ice thickness data were classified, and 15 regression models were obtained. The relative errors between the predicted and calculated values remained below 3%, demonstrating high predictive accuracy. These models were employed to estimate the effectiveness of piccolo tube hot air anti-icing systems under the most severe icing conditions. The results indicated that 100% anti-icing efficiency was achieved at high ambient temperatures (above −10 °C). During takeoff, holding, and climbing phases with a high speed of 154.3 m/s, the system may face challenges in maintaining anti-icing protection, resulting in runback ice with a maximum thickness exceeding 5 mm. Full article

Curved-spoke wheels have been proposed as an effective way to overcome stair-like obstacles with smooth, rotation-only motion. However, when the wheel’s contact point shifts, discontinuous changes in its radius of curvature cause abrupt drops in the robot’s linear speed, often leading to reduced payload stability and slip. As a result, maintaining reliable stair climbing becomes more difficult. At higher speeds, these sudden changes become stronger, further reducing dynamic stability. To address these issues, we propose a passive Compliant Spiral Torsional Suspension (C-STS) attached to the wheel’s drive axis. Through camera-based marker tracking, we analyzed wheel trajectories under various stiffness and speed conditions. In particular, we define the deceleration caused by the velocity drop during contact transitions as our dynamic stability metric and demonstrate that the C-STS significantly reduces this deceleration across low-, medium-, and high-speed climbing, based on comparisons both with and without the suspension. It also raises the average velocity, likely due to a brief release of stored elastic energy, and lowers the net torque requirement. Our findings show that the proposed C-STS greatly improves dynamic stability and suggest its potential for enhancing stair-climbing performance in curved-wheel-based robotic systems. Furthermore, our approach may extend to other reconfigurable wheels facing similar instabilities. Full article

This systematic review, conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD42024619693), aimed to evaluate the effects of physical exercise interventions on muscle function and fall risk in older adults with and without sarcopenia. Methods: A comprehensive search of PubMed and Web of Science databases identified 11 randomized controlled trials (RCTs) published between 2015 and 2025. A total of 792 participants (mean age 75.13 ± 4.71 years; 65.53% women, 34.47% men) were included. Interventions varied in type—strength, balance, aerobic, and multi-component programs—with a minimum duration of 8 weeks. Results: The reviewed studies showed that physical exercise interventions significantly improved neuromuscular function, physical performance, and postural control in older adults. Positive effects were observed in gait speed, stair-climbing ability, grip strength, muscle mass, and bone density. Specific modalities such as Tai Chi improved postural control and neuromuscular response; dynamic resistance and functional training increased muscle strength and improved posture; Nordic walking reduced postural sway; and multi-component and combined walking-resistance training enhanced mobility and force efficiency. Programs integrating strength and balance components yielded the most consistent benefits. However, reporting on FITT (Frequency, Intensity, Time, Type) principles was limited across studies. Conclusions: Exercise interventions are effective in improving neuromuscular outcomes and reducing fall risk in older adults, both with and without sarcopenia. The findings support the need for tailored, well-structured programs and greater methodological standardization in future research to facilitate broader clinical application and maximize health outcomes. Full article

Consumer-grade wrist-based wearable devices have grown in popularity among researchers to continuously collect metrics such as physical activity and heart rate. However, manufacturers rarely disclose the preprocessing sensor data algorithms, and user-generated data are typically shared leading to data governance issues. Open-source technology may address these limitations. This study evaluates the validity of the Bangle.js2 for step counting and heart rate during lab-based validation and agreement with other wearable devices (steps: Fitbit Charge 5; heart rate: Polar H10) in free-living conditions. A custom open-source application was developed to capture the sensor data from the Bangle.js2. Participants (n = 47; 25 males; 27 ± 11 years) were asked to complete a lab-based treadmill validation (3 min stages at 2, 3, 4, and 5 mph) and stair climbing procedure followed by a 24 h free-living period. The Bangle.js2 demonstrated systematic undercounting of steps at slower walking speeds with acceptable error achieved at 5 km/h. During free-living conditions, the Bangle.js2 demonstrated strong agreement with the Fitbit Charge 5 for per-minute step counting (CCC = 0.90) and total steps over 24 h (CCC = 0.96). Additionally, the Bangle.js2 demonstrated strong agreement with the Polar H10 for minute-averaged heart rate (CCC = 0.78). In conclusion, the Bangle.js2 is a valid open-source hardware and software solution for researchers interested in step counting and heart rate monitoring in free-living conditions. Full article

Additive manufacturing has reached a level of reliability and credibility that has already been integrated into specific industries producing final parts or tooling. Among Material Extrusion (ME) techniques, the Fused Granular Fabrication (FGF) method has enabled the development of Large Format Additive Manufacturing (LFAM) using polymeric materials, which has also established its presence in industries working with large prototypes, molds, and tools. This cost-efficient process has proven its applicability and success in manufacturing molds for composites, particularly in short and medium production runs, significantly reducing production times and costs. This paper presents two experiments designed to optimize process parameters when producing molds using the combined FGF and milling approach. These experiments identified optimal extrusion temperatures and extrusion multipliers to minimize defects at both the macro- and microscales for ASA 20 wt.% carbon fiber (CF) material; additionally, a correlation between milling speed, milling strategy, and surface roughness was established. These findings are valuable for industries adopting this innovative production method, as they provide guidance for defining process parameters to achieve the desired surface roughness of a specific part. A case study of the design of an automobile carter mold is presented, concluding that a specific range of milling speeds is required for conventional or climbing milling strategies to achieve a defined surface roughness range. Full article

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models—YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection—in addition to drones, these include robotic platforms, climbing robots, and smart hangars—and discuss key challenges for their deployment, such as regulatory constraints, human–robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations. Full article

The motor drive system is pivotal for vehicles, particularly in new energy applications. However, conventional hybrid systems, which combine generator sets and single batteries in parallel configurations, fail to meet the operational demands of large pure electric mining dump trucks under fluctuating power requirements—such as high reserve power during acceleration and robust energy recovery during braking. Traditional single-motor configurations struggle to balance low-speed, high-torque operations and high-speed driving within cost-effective ranges, often necessitating oversized motors or multi-gear transmissions. To address these challenges, this paper proposes a dual-motor symmetric powertrain configuration with a seven-speed gearbox, tailored to the extreme operating conditions of mining environments. By integrating a high-speed, low-torque motor and a low-speed, high-torque motor through dynamic power coupling, the system optimizes energy utilization while ensuring sufficient driving force. The simulation results under extreme conditions (e.g., 33% gradient climbs and heavy-load downhill braking) demonstrate that the proposed configuration achieves a peak torque of 267 kNm (200% improvement over single-motor systems) and a system efficiency of 92.4% (vs. 41.7% for diesel counterparts). Additionally, energy recovery efficiency reaches 85%, reducing energy consumption to 4.75 kWh/km (83% lower than diesel trucks) and life cycle costs by 38% (USD 5.34/km). Field tests in open-pit mines validate the reliability of the design, with less than a 1.5% deviation in simulated versus actual performance. The modular architecture supports scalability for 60–400-ton mining trucks, offering a replicable solution for zero-emission mining operations in high-altitude regions, such as Tibet’s lithium mines, and advancing global efforts toward carbon neutrality. Full article

This study investigates the effects of a vibration force field on the mixing and structural properties of polylactic acid (PLA), polybutylene succinate (PBS), and ethylene–glycidyl methacrylate terpolymer (EGMA) blends. A balanced triple-screw dynamic extrusion process was utilized to prepare PLA/PBS/EGMA composites under various vibration parameters, specifically amplitude and frequency. The results indicate that the introduction of a vibration force field significantly enhances the dispersion of the PLA/PBS/EGMA blend, leading to improved mechanical properties, thermal stability, and crystallization behavior. When the vibration frequency was 6 Hz and the amplitude was 1.0 mm, the impact strength increased from the steady-state value of 70.86 KJ/m² to 88.21 KJ/m². When the amplitude was 0.4 mm and the frequency was 10 Hz, the impact strength reached 81.86 KJ/m². The orthogonal experimental design and entropy method analysis revealed that vibration frequency and amplitude play a dominant role in optimizing mechanical performance, whereas processing temperature and rotor speed exhibit minimal impact. Scanning electron microscopy (SEM) analysis confirmed that the vibration force field reduces phase separation, promoting a finer and more homogeneous dispersion of PBS and EGMA within the PLA matrix. Additionally, TGA and DTG curves suggest that when the vibration amplitude and frequency are lower than specific thresholds, the thermal stability of the blend deteriorates. In contrast, when they exceed those thresholds, thermal stability improves. For instance, with an amplitude of 1.0 mm, the initial degradation temperature (T5) climbs from 328.6 °C to 333.7 °C. At a frequency of 10 Hz, T5 reaches 333.1 °C. These findings provide theoretical support for the application of vibration-assisted extrusion in the development of high-performance biodegradable polymer blends. Full article