Search Results (1,691)

We developed a dual-arm motion recognition system designed for real-time upper-limb movement analysis using video input. The system integrates MediaPipe Hands for skeletal critical point detection, a feature extraction pipeline that encodes spatial and temporal characteristics from upper-limb joints, and a three-layer long short-term memory network for temporal modeling and classification. By computing directional vectors from the shoulder to the elbow and wrist, a 168-dimensional feature vector is generated per frame. Sequences of 90 frames are used to capture full motion patterns. The system effectively supports multi-class recognition of coordinated dual-arm gestures, offering applications in rehabilitation, gesture control, and human–computer interaction. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

In order to investigate the application potential of lightweight agricultural robots utilizing carbon fiber-reinforced polymer (CFRP) as the primary structural material, this study developed a dedicated rubber-tracked chassis tailored for peanut pest and disease monitoring robots. The chassis design is anchored to the widely applied “single ridge with double rows” cultivation pattern in peanut production and incorporates a real-time track tension monitoring mechanism integrated with pressure sensors. The overall structural configuration of the chassis fully conforms to the standard ridge parameters of mechanized peanut planting while fully considering the intrinsic material properties of CFRP. Additionally, a sprocketless drive wheel structure is specifically adopted to realize higher-precision motion control performance. A mathematical model was constructed to quantitatively characterize the tension correlation between the tight side and slack side of the rubber track, as well as the variation law of initial tension influenced by multiple factors including the total mass of the robot platform. With the curb weight of the robot platform set at 45 kg, the theoretical initial tension is calculated to be 24.5 N (equivalent to approximately 2.5 kg, taking the gravitational acceleration g = 9.8 m/s²). The prototype shows potential for maintaining consistent tension, though a mechanical weakness was identified and will be addressed in future work. Performance validation tests show that the chassis maintains stable operation with no sprocket slippage during field visual inspection. Full article

This article proposes an integrated adaptive algorithm to control the accuracy and reliability of linear motion in electrohydraulic systems operating in dynamic modes and external loads. This algorithm has a multilevel parallel structure in which the physical model, extended measurement information, internal adaptive parameters, and the current of measurement and model uncertainties are combined and synchronized within a single loop. The proposed structure allows the real-time extraction of information about hard-to-determine dynamic characteristics of the electrohydraulic process, which is used to maintain consistency between the mathematical model and the actual behavior of the system, including in the case of rapidly changing modes and load variations. In addition, a functional observation layer for assessing the quality of measurement information is introduced, through which the sensitivity of adaptive mechanisms is managed, and the stability of the algorithm is maintained under degraded measurement conditions. Experimental results demonstrate a significant reduction in dynamic error and a sustainable improvement in the quality of tracking relative to a basic electrohydraulic system without algorithmic correction. This confirms the applicability of the proposed approach to real energy and industrial systems. Full article

Microinjection is one of the most established and effective techniques for introducing foreign substances into cells. However, issues such as cumbersome procedures, low success rates, and poor repeatability in manual cell microinjection have seriously restricted its practical applications in biomedical research and engineering. Responding to such problems, this paper designs an automated microinjection system that combines deep learning visual positioning and adaptive neural network sliding-mode motion control. The machine vision solution based on the deep learning YOLOv8 target detection algorithm is utilized by the system to provide positional prerequisites for automated microinjection. Then, stable and fast puncture is completed by controlling the end effector (composed of a piezoelectric actuator and a displacement amplification mechanism). Since the piezoelectric actuator has strong nonlinearity, the motion control of the end effector adopts the control strategy combining sliding mode variable structure and adaptive neural networks to meet the requirements of precise displacement output of microinjection. At the same time, a host computer control system is developed to integrate hardware equipment, visual positioning algorithms and motion control algorithms to achieve corresponding automated microinjection tasks. Finally, the effectiveness of the designed automated microinjection system is successfully verified on zebrafish embryos. Full article

Recent advances in extended reality (XR) have enabled immersive virtual flight experiences for applications such as entertainment and teleoperation support. However, XR-based flight systems that rely primarily on audiovisual cues often fail to evoke a compelling sense of flight and embodied sensation. This study investigates how adaptive wind feedback enhances subjective flight perception in a virtual flight simulation system, Drone Rider. We implemented direction- and velocity-adaptive wind feedback that synchronizes airflow intensity and direction with the user’s motion in the virtual environment, focusing on perceptual effects in a controlled manner to identify key design factors, rather than reproducing aerodynamically accurate airflow. To explore flexible system configurations, two fan installation positions were compared: front-mounted and bottom-mounted. A questionnaire-based user study revealed that adaptive wind feedback significantly enhanced the sense of flight, self-location, and agency compared with the constant-wind and no-wind conditions. However, no significant differences were observed between velocity-adaptive wind and direction- and velocity-adaptive wind conditions. Furthermore, wind delivered from beneath the user yielded flight sensations comparable to those generated by front-mounted airflow. These findings suggest that temporal coupling between airflow intensity and visual motion plays a central role in XR flight perception and provide practical design insights for immersive and flexible XR-based flight simulation systems. Full article

This study experimentally investigates the dynamics of air bubble plumes in water under varying thermal and hydrodynamic conditions using a two-dimensional Particle Image Velocimetry (PIV) system. The experimental setup consists of a transparent acrylic tank equipped with a bubble generator, a controlled heating system, and a synchronized PIV arrangement to capture both bubble motion and the induced liquid flow field. Experiments were conducted over a range of water temperatures (21–60 °C), air flow rates, and water depths (200–600 mm) to systematically quantify their coupled influence on bubble plume behavior. The results demonstrate that bubble rising velocity (defined here as the mean vertical, buoyancy-driven component of bubble motion measured in the fully developed plume region) increases with water temperature, gas flow rate, and water depth. For a fixed gas flow rate and water depth, increasing the water temperature from 40 °C to 60 °C resulted in an approximately twofold increase in bubble rising velocity, primarily due to reduced liquid viscosity and enhanced buoyancy forces. Bubble velocity also increased with gas flow rate and water depth, reflecting stronger momentum input and extended acceleration distances within taller water columns. PIV-resolved velocity fields further reveal that the surrounding fluid velocity increases proportionally with bubble rising velocity and temperature, confirming a strong coupling between bubble motion and plume-induced circulation. The surrounding liquid velocity reached approximately 30–60% of the corresponding bubble rising velocity, depending on operating conditions. These findings provide quantitative experimental insight into the coupled effects of thermal conditions, gas injection rate, and liquid depth on bubble–liquid interactions. The results contribute valuable validation data for multiphase flow modeling and offer practical relevance for thermal–hydraulic, chemical, and environmental engineering applications involving bubble-driven transport processes. Full article

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively. Full article

As robotics prehension systems and virtual reality applications are in constant evolution, the need for high-fidelity haptic interaction increases. This helps ensure and enhance user immersion and handling precision. While commercial haptic interfaces offer high performance, their prohibitive cost limits their widespread adoption in general-purpose robotics. Furthermore, many low-cost solutions suffer from limited transparency, where the operator constantly fights the friction of the actuator even during free motion. This article presents the design and development of an innovative, cost-effective master–slave robotic system aimed at democratizing efficient haptic feedback devices. The solution is intended for remote manipulation of objects with a maximum mass of 1 kg, while limiting the gripping force to 50 N, thus ensuring the integrity of objects being manipulated. The device includes a master haptic module in the form of a clamp that reproduces the thumb–index–middle finger gripping motion performed by the user. The system relies on a custom haptic interface measuring the angular position of the master gripper, which is transmitted in real time to the slave gripper, so as to adjust the position of the clamp accordingly, thus optimizing the grasping control loop. As soon as an object is detected, using a force sensor integrated into the slave gripper, the master motor renders a resistive force, preventing the user from closing the haptic module. The other part of the system is the slave mechanical gripper with three fingers, each with three phalanges based on human anatomy, allowing the clamp to mechanically conform to irregular object geometries with a single actuator. The last but not least innovative aspect lies in the implementation of a current sensor, which provides the haptic feedback. The force applied by the user is reproduced by the slave gripper using current sensors, eliminating the need for expensive force-torque sensors while maintaining a responsive feedback loop. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

Background: Gait analysis offers a comprehensive assessment of locomotion and postural control, which are often altered in individuals with spinal deformities. After validating a stereophotogrammetric protocol for whole-body kinematics, including spinal motion in healthy subjects, its application to clinical populations is needed to assess its clinical relevance. Patients treated with spinal arthrodesis for scoliosis may show reduced trunk mobility and compensatory gait strategies. Methods: The validated spinal protocol was applied to 10 patients with scoliosis who underwent arthrodesis and 5 healthy controls. For each participant, the range of motion (ROM) of the upper thoracic, lower thoracic, and lumbar districts was computed. Group differences were assessed with the Mann–Whitney U test, and time-normalized angular curves were compared using Statistical Parametric Mapping (SPM1d). Results: In the pathological group, the protocol showed moderate-to-excellent intra- and inter-operator reliability (ICC > 0.594). Compared with controls, patients exhibited a significant reduction in ROM in fused or adjacent districts. SPM analysis identified altered upper thoracic flexion–extension patterns, particularly relative to the lower thoracic segment, throughout the gait cycle. Conclusions: The protocol demonstrated preliminary feasibility and sensitivity in identifying segmental and phase-dependent changes in spinal motion after arthrodesis, indicating that it may serve as a useful tool for exploratory postoperative gait evaluation. Full article

Accurate prediction of store trajectories at the point of release from an unmanned/manned aircraft is an essential requirement for safety and precision. Captive Trajectory System (CTS) is a well-known feature of wind-tunnel testing to simulate the dynamics of store separation. To accurately replicate real-world aerodynamic conditions based on measured forces and moments, it utilizes a six-degree-of-freedom (6-DOF) robotic arm controlled by a closed-loop control system that solves the store’s equations of motion. In this study, a wing–pylon–store configuration is used as a sample case, and published experimental trajectories are used as input. A 6-DOF robotic arm named ROBO-S is designed to follow these trajectories in a CTS setup. The kinematic analysis of ROBO-S is performed in this study. The Denavit–Hartenberg (DH) method is used for the calculation of forward kinematics, whereas geometric techniques are used for inverse kinematics calculations. A simulation of kinematic analysis is performed in MATLAB R2021a. The mechanical design of ROBO-S is carried out in PTC CREO 9.0. MATLAB simulations confirm that the robotic arm can follow the trajectory obtained from published experimental results. To demonstrate the feasibility of the design, the robotic arm is fabricated using 3D printing. The results demonstrate the potential of the developed system in accurately following trajectories for wind-tunnel testing applications. Full article

Background: Closing extraction spaces with clear aligners remains a significant biomechanical challenge, frequently involving difficulties in sagittal control, torque expression, and intra-arch anchorage. Although various sequential or phased retraction strategies exist, the Caterpillar Motion protocol has not yet been formally defined. This clinical report describes the Caterpillar Motion staging protocol and illustrates its application through representative extraction cases, rather than providing a systematic review or experimental comparison. Case Presentation: Two adult patients with extraction-based malocclusions were treated using the Caterpillar Motion staging protocol. Case 1 involved bimaxillary first-premolar extractions with maximum anchorage requirements and periodontal limitations in the mandibular incisors. Case 2 presented as a full Class II malocclusion requiring maxillary first-premolar extractions with moderate anchorage for sagittal camouflage. In both cases, tooth movement was organized into alternating functional groups, with waves limited to 2 mm of sagittal closure. Discussion: The Caterpillar Motion protocol reduces the risk of aligner bowing effect, increases effective crown engagement, and redistributes anchorage demands by preventing simultaneous shortening of both arch extremities. Both cases demonstrated controlled anterior retraction, stable posterior anchorage, and favorable root parallelism. Conclusions: Caterpillar Motion offers a biomechanically coherent and clinically reproducible staging strategy for clear aligner extraction therapy. Further controlled studies are needed to validate its advantages over traditional linear and en-masse protocols. Full article

Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models. Full article

A multi-objective tuning framework for optimizing Tuned Mass Damper (TMD) systems is presented. This framework optimizes the controlling parameters of TMDs while considering building resilience. The employed optimizer is the multi-objective HBA. Since TMDs are used in tall structures to mitigate seismic-induced structural responses, the proposed framework must be applicable to real-world scenarios; therefore, it determines both the placement and parameters of TMDs by accounting for the effects of various soil types and multiple earthquake records. Based on the obtained results, the optimal TMDs achieved an average roof-displacement reduction of 17% in soft soil, 7% in fixed-base conditions, and only 4% in dense soil, highlighting the decisive influence of soil–structure interaction on system efficiency. Moreover, there was considerable outcome variability across different earthquake records—ranging from 0.8% to 26% reduction—along with the observed negative effect (response amplification of up to 13.9% in certain fixed-base cases), which occurs when the TMD becomes detuned relative to the dominant frequency of the specific ground motion. This confirms the necessity for a robust design approach that simultaneously considers an ensemble of ground motions rather than optimizing for a single record. Full article