Search Results (769)

Manufacturing systems are complex installations designed and developed by stakeholders with differing expertise. However, system design primarily features traditional methods, long lead times, and siloed environments, which obstruct comprehension, communication, or collaboration. Prior research has used extended reality (XR) to prototype and visualise specific manufacturing applications; however, flexible system modelling remains complex. To address these issues and lay the foundation for future-facing manufacturing design as part of Industry 4.0, we introduce ARTIFY—a prototype XR framework for conceptual system prototyping. It features abstract building blocks that can be semantically reconfigured to support early-stage modelling of varied manufacturing systems in immersive environments. This paper describes ARTIFY and presents an initial think-aloud evaluation. Participants perceived ARTIFY to enhance their spatial comprehension of systems, with users reporting excellent spatial presence, reflected in a mean Igroup Presence Questionnaire (IPQ) score of 5.14. They further reported subjective preferences for spatially organising system components when compared to “normal post-it notes”. However, the evaluation also highlighted key areas for improvement around usability, such as text input via the virtual keyboard, which requires further iteration to better support rapid user engagement. We conclude by discussing the implications for future industrial applications. Full article

Quadruped robots possess strong adaptability to rugged terrain, soft ground, and multi-obstacle environments, offering broad application prospects in extraterrestrial planetary exploration. However, large diurnal temperature variations on extraterrestrial bodies exacerbate joint friction nonlinearity, degrading motion control accuracy and stability. To address this, a quadruped robot prototype with hybrid serial–parallel legs is designed for lunar exploration, and an 18-DOF dynamic model is derived using d’Alembert’s principle. Based on the PPO (Proximal Policy Optimization) reinforcement learning algorithm, joint friction parameters are identified using joint velocity and foot–ground contact force. By introducing friction compensation and contact force, an accurate dynamics-based feedback linearization control model is constructed, and a motion impedance control law is designed. Finally, joint friction parameters are identified and validated through both virtual and experimental prototypes, and the proposed control method is tested on flat and sloped terrain. Results show that the method can precisely regulate contact force and foot position, keeping RMSE (Root Mean Square Error) of position within 21.04 mm while preventing slipping and false contact. Full article

Simulator-based digital twins are widely used in robotics education and industrial development to accelerate prototyping and enable safe experimentation. However, they often hide implementation details that are essential for understanding, diagnosing, and correcting system failures. This paper introduces a technology-independent model-based design framework that provides students with full visibility of the computational mechanisms underlying robotic controllers while remaining feasible within a 150-h undergraduate course. The approach relies on representing controller behavior using networks of Extended Finite State Machines (EFSMs) and their stacked extension (EFS2M), which unify all abstraction levels of the control architecture—from low-level reactive behaviors to high-level deliberation—under a single formal model. A structured programming template ensures traceable, optimization-free software synthesis, facilitating debugging and enabling self-diagnosis of design flaws. The framework includes real-time synchronized simulation, transparent switching between virtual and physical robots, and a smart data logger that captures meaningful events for model updating and error detection. Integrated into the Intelligent Robots course, the system supports topics such as kinematics, control, perception, and simultaneous localization and mapping (SLAM) while avoiding dependency on specific middleware such as Robot Operating System (ROS) 2. Over three academic years, students reported positive hands-on experiences, strong adaptability to diverse modeling approaches, and consistently high survey ratings reflecting the course’s overall quality. The proposed environment thus offers an effective methodology for teaching end-to-end robot controller design through transparent, simulation-driven digital twins. Full article

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions. Full article

The objective of the study was to undertake a preliminary analysis of the operational accuracy of a prototype suspension therapy apparatus. This entailed the establishment of the kinematic relationship between the movements imposed by the actuators and the movements of the participants’ body segments. The experimental procedure involved the taking of measurements on six participants (average age 32 ± 8 years, weight 67 ± 7 kg, height 178 ± 7 cm). Five movement sequences were observed, including rotation of the head, shoulders, and pelvis, and alternating movement of the shoulders, relative to the pelvis, and the head, relative to the shoulders. The movement of body segments and actuators was recorded using a Vicon optoelectronic system, based on passive markers. A virtual kinematic model was prepared for each of the measurements. It was found that the relationship between the actuator-imposed rotations and the resulting segmental rotations depended on the movement sequence and the body segment involved. The mean head rotation was 46.4° ± 1.2° (27.8% greater than the actuator setting) and the mean shoulder rotation was 23.8° ± 2.4° (11.1% greater), whereas the mean pelvic rotation (20.1° ± 0.9°) showed near agreement with the actuator-imposed value. In alternating movement sequences, distinct directional patterns were observed: head rotation remained greater than the actuator setting, shoulder rotation showed near-agreement or moderate increases, and pelvic rotation in the shoulder–pelvis sequence was markedly lower than the actuator-imposed rotation. The device demonstrates a high level of efficacy in mapping movements, particularly with regard to pelvic rotation. Differences in head rotation indicate the need for further optimisation of movement sequences. The results suggest mapping stability for the majority of participants, with isolated deviations requiring further investigation. Full article

In response to the International Maritime Organization’s emission reduction targets, ship power systems are transitioning toward microgrid architectures with high renewable energy penetration. In islanded mode, the lack of main grid support and the low inertia of power electronic interfaces pose significant frequency stability challenges. Virtual Synchronous Generator (VSG) technology offers an effective solution, but conventional VSG control exhibits two inherent limitations: steady-state frequency deviation under load variations due to its primary regulation nature, and poor dynamic response characterized by large overshoot and prolonged settling time. This paper proposes an enhanced VSG control strategy integrating two key innovations: (i) a communication-free secondary frequency regulation loop that eliminates steady-state error, and (ii) an adaptive control scheme for virtual inertia and damping coefficients that dynamically responds to frequency deviations and their rate of change. The adaptive mechanism reduces overshoot by 57% (from 0.14 Hz to 0.06 Hz) and shortens settling time by 40% (from 0.38 s to 0.23 s) compared to non-adaptive secondary regulation, as demonstrated through MATLAB/Simulink simulations and 6 kW experimental prototype validation. The proposed strategy ensures both steady-state accuracy and enhanced transient performance, providing a reliable solution for improving power quality in islanded shipboard microgrids and contributing to maritime decarbonization goals. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

Eye tracking in virtual reality (VR) head-mounted displays poses substantial engineering challenges, particularly under immersive display configurations with large fields of view (FOV), where optical layout, illumination, and image acquisition impose nontrivial system constraints. To address these design constraints, we present an integrated near-eye eye-tracking prototype tailored for immersive VR headsets, combining customized hardware components and a real-time software pipeline. The proposed system integrates optimized near-eye illumination and image acquisition with a pupil detection module and a deep learning-based gaze-vector estimation model, forming a real-time software pipeline for stable end-to-end gaze mapping under fixed calibration conditions. Under identical system settings, calibration procedures, and gaze-point mapping conditions, we evaluate the proposed gaze-vector estimation model through a controlled model-level ablation. The attention-enhanced model achieves an average angular deviation of 1.15°, corresponding to a 61.4% relative reduction compared with a baseline ResNet-152 model without attention. To demonstrate the usability of the system outputs at the application level, we further implement a real-time visualization example that integrates pupil diameter, gaze vectors, and blink events to depict the temporal evolution of eye-movement signals. This work provides a cost-effective and reproducible engineering reference for near-eye eye-movement acquisition and visualization in immersive VR settings and serves as a technical foundation for subsequent interaction design or behavioral analysis studies. Full article

The tooth flank distortion error occurring during the form-grinding (FG) of an involute helical gear can significantly compromise transmission performance. Conventional research approaches often focus on single-parameter optimization—either the grinding wheel installation angle (GWIA) or the contact line (CL)—without adequately accounting for the coupling relationships among GWIA, CL, and the modification curve (MC). To address this limitation, this study proposes an innovative joint optimization approach that simultaneously optimizes GWIA, CL, and MC to effectively minimize tooth flank distortion in FG. Based on the principles of form-grinding, a mathematical model is established for the contact line of the target gear and the cross-sectional profile of the grinding wheel. The relationship between GWIA and tooth flank deviation is investigated using a proprietary virtual prototype. A multi-objective artificial bee colony (ABC) optimization algorithm is employed to determine the optimal values of GWIA and CL. For the axial modification curve, this paper introduces a novel three-segment quadratic curve optimization scheme as an improvement over conventional modification methods. To validate the proposed optimization technique, form-grinding experiments are conducted on the L300G gear grinding machine. Simulation outcomes indicate that, pre-optimization, the maximum tooth flank distortion errors primarily occur at the tooth root and tip regions on both ends of the gear. After optimization, the simulated distortion error on the left tooth flank is reduced by 48.5%, while the right flank shows a reduction of 29.4%. These simulation outcomes exhibit a deviation of approximately 10% compared with the experimental results. This study provides valuable insights for enhancing the transmission performance of helical gears. Full article

This study presents the modeling, analysis, and control of a novel planar three-degrees-of-freedom TTR (Translational–Translational–Rotational) mechanism. A comprehensive kinematic and dynamic formulation is developed, with the governing equations derived analytically using the principles of virtual work and Hamiltonian mechanics. Due to the nonlinear nature of the inverse kinematics, a numerical solution based on the modified Newton–Raphson method is employed to compute joint trajectories. To ensure robust trajectory tracking in the presence of modeling uncertainties and external disturbances, a sliding-mode control strategy is designed and implemented. The proposed approach is evaluated through numerical simulations and experiments conducted on a custom-built prototype. Quantitative performance metrics, including mean squared error, are used to assess tracking accuracy and to compare simulation and experimental results. The consistency between analytical modeling, numerical solutions, and experimental observations demonstrates the feasibility of the proposed framework for planar robotic motion control applications. Full article

Traditional involute gear pumps find it difficult to meet the requirements of miniaturization and high performance because of the undercutting, trapped oil, and flow pulsation. To eliminate the phenomenon of trapped oil and reduce flow pulsation in the miniature gear pump, a novel miniature double-circular-arc line gear (MDLG) and its manufacturing method are proposed. Firstly, based on the spatial curve meshing theory, the tooth flank equation of the MDLG is established, and the design method of the MDLG hob is presented. Then, the instantaneous flow rate of the MDLG pump is analyzed by using the swept-area method. Subsequently, a hobbing machining model is built on the VERICUT virtual simulation platform, and machining experiments are conducted on a hobbing machine. Furthermore, the manufactured MDLGs are inspected at a gear measuring center. Finally, an MDLG pump prototype is developed and machined. The measurement results show that the total cumulative pitch deviations of the machined MDLGs are controlled within 32.1 μm, achieving the ISO 8 accuracy grade. The theoretical calculations and experimental results in this article verify the feasibility of the design and processing of MDLG pumps, providing a reference for the development of high-performance miniature gear pumps. Full article

The endogenous security paradigm has emerged to address the limitations of traditional cybersecurity, which relies on reactive “patching” and struggles against unknown threats, APTs, and supply chain attacks. Centered on the principle that “structure determines security”, it diverges from detection-based approaches by employing systems theory and cybernetics to architect closed-loop systems with “heterogeneous execution, multimodal adjudication, and dynamic scheduling”. This is realized through intrinsic architectural constructs such as dynamism, heterogeneity, and redundancy. Theoretically, it transforms deterministic component-level attacks into probabilistic system-level events, thereby shifting the security foundation from a “cognitive contest” to an “entropy-driven confrontation”. This paper provides a comprehensive review of this paradigm. We begin by elucidating its philosophical foundations and core axioms, focusing on the Dynamic Heterogeneous Redundancy (DHR) model, which converts attacks on specific vulnerabilities into probabilistic events under the core assumption of independent heterogeneous execution entities. Next, we trace the architectural evolution from early mimic defense prototypes to a universal framework, analyzing key developments including expanded heterogeneity dimensions, intelligence-driven dynamic policies, and enhanced adjudication mechanisms. We then explore essential enabling technologies and their integration with cutting-edge trends such as artificial intelligence, 6G, and cloud-native computing. Through case studies of the 5G core network and intelligent connected vehicles, the engineering feasibility of the endogenous security paradigm has been validated, with quantifiable security gains demonstrated. In a live-network pilot of the endogenous security micro-segmentation system for the 5G core, resource consumption (CPU/memory usage) of network function virtual machines remained below 3% under steady-state service loads. The system concurrently maintained microsecond-level forwarding performance and achieved carrier-grade core service availability of 99.999%. These results demonstrate that the endogenous security mechanism delivers high-level structural security with an acceptable performance cost. The paper also critically summarizes current theoretical, engineering, and ecosystem challenges, while outlining future research directions such as “Endogenous Security as a Service” and convergence with quantum-safe technologies. Full article

Hand exoskeletons are used in rehabilitation together with serious games to enhance patient experience and, possibly, therapy outcomes. To achieve good engagement, a realistic virtual representation of hand motion is needed; however, the relationship between exoskeleton joint motion and anatomical finger kinematics is rarely obtained using low-cost procedures. This work introduces a mechanical redesign and modeling pipeline that utilizes temporary sensors to identify the exoskeleton–finger mapping, enabling qualitatively realistic virtual hand motion driven solely by the existing on-board sensor. A recently developed hand exoskeleton prototype was redesigned to host two temporary rotary encoders aligned with the MetaCarpoPhalangeal (MCP) and Proximal InterPhalangeal (PIP) joints, in addition to the actuation encoder. Healthy subjects wore the modified device and performed full flexion–extension cycles. Encoder trajectories were processed; then each cycle was approximated by a third-order polynomial in the normalized actuation angle, and a group-level model was obtained by averaging coefficients across valid cycles. Finally, the encoder-based reconstructions of MCP and PIP motion were evaluated against measurements from a gold-standard optical motion capture system. Results indicate that the proposed polynomial model enables joint-angle estimation with sufficient accuracy for interactive rehabilitation scenarios, supporting its use to drive smooth virtual hand motion from the on-board exoskeleton encoder alone. Full article

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice. Full article

In the paper, a heterodyne quartz-enhanced photoacoustic spectroscopy (H-QEPAS)-based integrated methane (CH₄) sensor prototype is reported. The CH₄ absorption line located at 1650.96 nm was selected as the target spectral line. The design features an integrated, 3D-printed gas chamber for reduced size and weight. To realize the coordinated operation of each hardware component, a control program was designed based on LabVIEW platform, enabling the adjustment of various hardware parameters. The piezoelectric signal generated by the quartz tuning fork (QTF) was amplified via a trans-impedance amplifier (TIA), acquired by a data acquisition card (DAQ), and then transmitted to a virtual lock-in amplifier (LIA) on the PC terminal for processing. The dimensions of the integrated CH₄ sensor prototype are 33 cm in length, 27 cm in width, and 15 cm in height. The final test results demonstrate that the sensor prototype exhibits an excellent concentration linear response, with a detection limit of 26.72 ppm and a short detection time of approximately 4 s. Full article