Search Results (156)

The load current of the new power system has significant characteristics on a wide dynamic range, which poses challenges to current sensing technologies. This paper proposes a magnetic-sensitive element based on NdFeB/Lead Zirconate Titanate (PZT) magnetoelectric composite materials, and further develops a magnetoelectric coupling current sensor. The sensor operates in torque mode, enabling the detection of both wide dynamic range alternating currents and weak alternating currents. Experimental studies show that the sensor achieved a power-frequency current detection sensitivity of 15.56 mV/A, a linear range of (0–120) A, and a detection limit of 153 μA. The results indicate that the sensor exhibits high sensitivity in alternating current (AC) current detection, and at power frequency, possesses both a wide dynamic range and the capability to detect weak currents. Therefore, it shows great application potential in scenarios such as wide dynamic range AC current measurement and weak current detection in power systems. Full article

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. Full article

Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This review consolidates recent advances in intelligent drilling, focusing on how sensors and artificial intelligence (AI) are integrated to enable process understanding, prediction, and control. In-process monitoring modalities (e.g., cutting forces/torque, vibration, acoustic emission, motor current/active power, infrared thermography, and vision) are examined with respect to signal characteristics, feature design, and modelling choices for real-time anomaly detection, tool condition monitoring, and phase/interface recognition. Predictive quality modelling of burr, delamination, roughness, and roundness is discussed across statistical learning, kernel methods, and neural and hybrid models. Offline parameter optimisation via surrogate-assisted and evolutionary algorithms is considered alongside adaptive control strategies. Practical aspects of robotic drilling and multifunctional end-effectors are highlighted as enablers of embedded sensing and feedback. Finally, cross-cutting challenges (e.g., limited, heterogeneous datasets and model generalisability across materials, tools, and geometries) are outlined, together with research directions including curated multi-sensor benchmarks, multi-source transfer learning, physics-informed machine learning, and explainable AI to support trustworthy deployment in aerospace manufacturing. Full article

This review synthesises fault detection and diagnosis (FDD) methods for robotic actuation in healthcare, where precise, compliant, and safe physical human–robot interaction (pHRI) is essential. Actuator families—harmonic-drive electric transmissions, series-elastic designs, Cable/Bowden mechanisms, permanent-magnet synchronous motors (PMSM), and force–torque-sensed architectures—are mapped to characteristic fault classes and to sensing, residual-generation, and decision pipelines. Four methodological families are examined: model-based observers/parity relations, parameter-estimation strategies, signal-processing with change detection, and data-driven pipelines. Suitability for pHRI is assessed by attention to latency, robustness to movement artefacts, user comfort, and fail-safe behaviour. Aligned with ISO 14971 and the IEC 60601/80601 series, a validation framework is introduced, with reportable metrics—time-to-detect (TTD), minimal detectable fault amplitude (MDFA), and false-alarm rate (FAR)—at clinically relevant thresholds, accompanied by a concise reporting checklist. Across 127 studies (2016–2025), a pronounced technology-dependent structure emerges in the actuator-by-fault relationship; accuracy (ACC/F1) is commonly reported, whereas MDFA, TTD, and FAR are rarely documented. These findings support actuation-aware observers and decision rules and motivate standardised reporting beyond classifier accuracy to enable clinically meaningful, reproducible evaluation in contact-rich pHRI. Full article

This paper presents tilt-and-pivot manipulation, a robotic technique for picking large, thin objects resting on hard supporting surfaces. The method employs in-hand dexterous manipulation by reorienting the gripper around the object’s contact point, allowing a finger to enter the gap between the object and the surface, without requiring relative sliding at the contact. This finally facilitates reliable pinch grasps on the object’s faces. We investigate the kinematic principles and planning strategies underlying tilt-and-pivot, discuss effector design considerations, and highlight the practical advantages of the strategy, which is applicable to a variety of low-profile objects. Experimental results, incorporating vision and force–torque sensing, demonstrate its effectiveness in bin-picking scenarios and its applicability to more complex object-handling tasks. Full article

Unaccustomed eccentric exercise is well established to induce exercise-induced muscle damage (EIMD), characterized by transient strength loss, delayed onset muscle soreness (DOMS), reduced range of motion, and proprioceptive disturbances. While limb dominance has been proposed as a potential modulator of susceptibility to EIMD, evidence remains inconclusive. This exploratory study aimed to compare alterations in muscle damage indices between dominant and non-dominant knee extensors 48 h after eccentric isokinetic exercise. Eighteen physically active young women (23 ± 2 years) completed two eccentric exercise sessions (5 × 15 maximal contractions at 60°/s), one per limb, with sessions separated by 24–30 days. For all participants, testing was conducted during the early follicular phase. Muscle strength (isometric and eccentric peak torque), DOMS (palpation and pain pressure threshold), range of motion, fatigue index, and position sense were assessed pre- and 48 h post-exercise. Significant reductions in isometric and eccentric peak torque, increased DOMS, impaired position sense, and altered fatigue index were observed 48 h post-exercise in the exercised limb (p < 0.001), with no differences between dominant and non-dominant limbs across all indices. These findings demonstrate that limb dominance does not influence the magnitude of EIMD in knee extensors of young women. Practical implications include equal consideration of both limbs in eccentric training, rehabilitation, and injury prevention programs. Full article

In this study, a fuzzy-logic-based decision support model is developed to predict the fatigue life of load-bearing system elements such as torque arm. Traditional methods for fatigue life prediction are mostly based on certain mathematical expressions and fixed parameters and do not adequately take into account the uncertainties caused by many factors such as material structure, surface condition, loading pattern and heat treatment. In order to overcome these deficiencies, the fuzzy logic method is preferred. The model is based on a fuzzy logic system and predicts outputs according to specific input conditions using rules derived from expert knowledge and experience. The input parameters of the model are material type, surface hardness, maximum applied stress level, and type of heat treatment. Although these parameters can be expressed numerically in the classical sense, the relationship between them is often imprecise and based on experience and engineering interpretation. Therefore, a more realistic and flexible prediction model has been created with the linguistic variables and rule-based approach of fuzzy logic. Full article

This project presents the development of an open-source educational platform based on an automotive Electrically Excited Synchronous Machine (EESM) repurposed from a KIA Sportage mild-hybrid vehicle. The introduction provides an overview of hybrid drive systems and the primary configurations employed in automotive applications, including classifications based on power flow and the placement of electric motors. The focus is placed on the parallel hybrid configuration, where a belt-driven starter-generator assists the internal combustion engine (ICE). Due to the proprietary nature of the original control system, the unit was disassembled, and a custom control board was designed using a Texas Instruments C2000 Digital Signal Processor (DSP). The motor features a six-phase dual three-phase stator, offering improved torque smoothness, fault tolerance, and reduced current per phase. A compact Anisotropic Magneto Resistive (AMR) position sensor was implemented for position and speed measurements. Current sensing was achieved using both direct and magnetic field-based methods. The control algorithm was verified on a modified six-phase inverter under simulated vehicle conditions utilizing a dynamometer. Results confirmed reliable operation and validated the control approach. Future work will involve complete hardware testing with the new control board to finalize the platform as a flexible, open-source tool for research and education in hybrid drive technologies. Full article

Much as the information generated by our fingertips is used for fine-scale grasping and manipulation, closed-loop dexterous robotic manipulation requires rich tactile information to be generated by artificial fingertip sensors. In particular, fingertip shear sensing dominates modalities such as twisting, dragging, and slipping, but there is limited research exploring soft shear predictions from an increasingly popular single-material tactile technology: electrical impedance tomography (EIT). Here, we focus on the twisting of a screwdriver as a representative shear-based task in which the signals generated by EIT hardware can be analyzed. Since EIT’s analytical reconstructions are based upon conductivity distributions, we propose and investigate five reduced-order models which relate shear-based screwdriver twisting to the conductivity maps of a robot’s single-material sensorized fingertips. We show how the physical basis of our reduced-order approach means that insights can be deduced from noisy signals during the twisting tasks, with respective torque and diameter correlations of 0.96 and 0.97 to our reduced-order parameters. Additionally, unlike traditional reconstruction techniques, all necessary FEM model signals can be precalculated with our approach, promising a route towards future high-speed closed-loop implementations. Full article

Robotic assembly of electrical connectors enables the automation of high-efficiency production of electronic products. A rigid gripper is adopted as the end-effector by the majority of existing works with a force–torque sensor installed at the wrist, which suffers from very limited perception capability of the manipulated objects. Moreover, the grasping and movement actions, as well as the inconsistency between the robot base and the end-effector frame, tend to result in angular misalignment, usually leading to assembly failure. Bio-inspired by the human finger, we designed a tactile finger in this paper with three characteristics: (1) Compliance: A soft ‘skin’ layer provides passive compliance for plenty of manipulation actions, thus increasing the tolerance for alignment errors. (2) Tactile Perception: Two types of sensing elements are embedded into the soft skin to tactilely sense the involved contact status. (3) Enhanced manipulation force: A rigid fingernail is designed to enhance the manipulation force and enable potential delicate operations. Moreover, a tactile-based alignment algorithm is proposed to search for the optimal orientation angle about the z axis. In the application of U-disk insertion, the three characteristics are validated and a success rate of 100% is achieved, whose generalization capability is also validated through the assembly of three types of electrical connectors. Full article

This study aimed to examine how downward load applied during instrumentation affects the stress generation and shaping properties in thermally treated and non-treated NiTi rotary instruments. ProTaper Universal (PTU; non-thermally treated) and ProTaper Gold (PTG; thermally treated) were used to prepare J-shaped canals in resin blocks. Load-controlled automated instrumentation and torque/force sensing devices were employed with preset downward loads of 1, 2, or 3 N (n = 10 each). The torque/force, instrumentation time, and canal-centering ratio were measured and analyzed using two-way or one-way analysis of variance with Tukey’s test (α = 0.05). In the PTU-1N group, instrumentation was not completed because a ledge was formed in all canals. The PTU-3N group showed significantly greater upward force (screw-in force) and clockwise torque, along with a significantly smaller canal-centering ratio (less deviation) at the apical 0 mm level, than the PTU-2N group (p < 0.05). The downward load did not influence the instrumentation time (p > 0.05). In the PTG groups, these effects of downward load on the force generation and canal-centering ratio were not significant (p > 0.05). In the non-thermally treated PTU instruments, greater downward loads enhanced screw-in force while decreasing apical canal deviation; however, these effects were abolished in the thermally treated PTG instruments. This study highlights the importance of adapting the instrumentation technique with instrument characteristics: thermally treated flexible instruments facilitate smoother use, while stiffer, non-thermally treated ones may require precise control of downward loads. Full article

Ultra-high-temperature and pressure downhole environments pose challenges for conventional electronic instruments to adapt to high-temperature formations, thereby restricting the application of downhole electronic tool technology in deep and ultra-deep wells. Given the aforementioned limitation of electronic inclination measurement systems, specifically their poor temperature resistance, this study proposes a novel shunt flow control method. This method employs a mechanical structure to overcome temperature constraints: gravitational torque generated by the mechanical structure is utilized to control valve opening and regulate flow rate. By converting sensed well inclination information into changes in flow rate, this approach enables the transformation of well inclination sensing and its associated signals. In this study, a kinetic analysis model of the shunt-regulating valve spool was established. Using computational fluid dynamics (CFD) simulations, the flow characteristics of the regulating spool were analyzed under varying valve openings. The structure of the flow control valve was optimized with the goal of maximizing internal flow. Finally, the reliability of the designed structure for well deviation sensing and flow control was verified using simulation experimental studies and theoretical analyses. Full article

The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of ±5–50 N·m. Within this range, the device achieved a mean absolute error of 0.559 N·m. It also maintained precision better than ±2.5 N·m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article