Search Results (459)

Background: Digital twins (DTs) are a key technology of Industry 4.0 and play a crucial role in the digital transformation of industry. The education of experts for the needs of Industry 4.0 and the emerging 5.0 must align with the requirements of practice. However, the availability of these technologies for educational institutions is problematic due to their complexity and limited resources. Methods: This study develops and implements DT and a virtual monitoring system for a specialised 6-DOF laboratory robotic manipulator. The architecture uses a multi-layered communication via middleware KEPServerEX. AI-assisted “Vibe Coding” supports development of a custom Python control application and integration with multiple APIs. Next, the application of a virtual monitoring system enables tracking of selected metrics via a web interface. Results: Our work provides three primary contributions: (1) consolidation of multiple devices through middleware (KEPServerEX) to link robots and PLCs; (2) accelerated development through AI-assisted “Vibe Coding”; and (3) remote accessibility through a custom web-based monitoring platform. Conclusions: The study demonstrates that a fully functional digital twin, integrated with a virtual monitoring system, can be implemented in a university laboratory environment using a combination of cost-effective technologies. Full article

Passive magnetic localization for wireless capsule endoscopy degrades under involuntary gastrointestinal motion: displacement toward one sensor layer simultaneously saturates near-side magnetometers while driving far-side sensors below the noise floor, a failure mode we term asymmetric layer visibility. Existing single-layer and multi-layer systems do not model this explicitly, and full-array Jacobian conditioning degrades progressively with displacement even where a layer-conditioned decomposition remains comparatively stable. We present a two-layer magnetometer array with three algorithmic contributions: per-sensor SNR gating excluding saturated or low-SNR sensors before optimization; layer-conditioned Levenberg–Marquardt estimation on a five-dimensional manifold via spherical parameterization; and SNR-weighted fusion with geodesic interpolation on $S^2$, combined with layer-wise ellipsoid calibration and neural network initialization. Eight LIS3MDL tri-axial magnetometers in a $15\times 15\times 30$ cm³ two-layer array were validated across 150 Monte Carlo trials using a 4-DOF robotic arm. Mean positional error was $2.32\pm 0.35$ mm and orientational error $2.15\pm 0.88°$ at 12.4 Hz, with 93.85% convergence success under displacement-induced asymmetric visibility events of 1.00–4.00 cm—over 70% reduction over single-layer and layer-agnostic baselines. Hardware generalization was confirmed across three magnetometer types. The SNR weight ratio also yields a passive discriminant between capsule displacement and global array motion, cross-validated AUC $0.924\pm 0.045$, requiring no additional hardware. Reported accuracy is specific to the LIS3MDL magnetometer used in this study. Full article

The pea weevil (Bruchus pisorum L.) causes severe yield losses in cultivated pea (Pisum sativum L.), yet the molecular basis of host resistance remains unclear. Here, we performed integrated transcriptomic and metabolomic analyses on pods and seeds of resistant (YWD) and susceptible (LW1) pea genotypes to characterize the constitutive defence network against weevils. Extensive transcriptional and metabolic reprogramming was observed in resistant materials, with upregulation of defence pathways and suppression of primary metabolism. Pods directed phenylpropanoid flux toward isoflavonoid biosynthesis and enhanced cutin/wax pathways, while seeds redirected flux to flavone/flavonol biosynthesis, accumulating high levels of flavone C-glycosides including isovitexin (5260.46-fold) and vitexin (3771.09-fold). We identified Dof21 as a key transcription factor putatively regulating flavonoid biosynthesis, with its Dof domain predicted to bind promoters of 4CL1, CHS3 and CYP75B. A seed-specific lectin gene LG16 showed 8.99-fold higher expression in resistant seeds with negligible expression in pods. The ABA catabolite dihydrophaseic acid and semi-volatile compounds vanilloloside and cimidahurinine were significantly enriched in resistant seeds, implicating ABA turnover and glycosylation in defence compound stabilization. This study provides a multi-dimensional molecular framework for weevil resistance in pea and offers key candidate genes including Dof21, LG16, 4CL1, CHS3 and CYP75B for resistant germplasm screening and marker-assisted breeding. Full article

Robotic bin-picking of disordered, randomly stacked workpieces remains challenging because reliable grasping depends on an accurate estimate of object pose, yet many established solutions require high-precision 3D sensing, detailed object models, or large annotated datasets that raise the cost and effort of deployment on a new production line. This work presents a complete binocular vision framework that estimates workpiece pose by image matching and executes vision-guided grasping on a 6-DOF manipulator. A pose-annotated multi-view template library is constructed automatically through robot-driven image acquisition and compressed by a coarse-to-fine clustering scheme, and object pose is estimated by discriminative template matching with rigid refinement. To characterize the geometric reliability of the matched poses, an offline cross-modal analysis relates the 2D templates to a 3D reference model of the object and measures their agreement through region and contour reprojection metrics. Grasp configurations are then generated under orientation and collision constraints and corrected online by closed-loop visual feedback. Experiments on two representative workpieces show template-matching accuracy of 89–90% against classical and learned similarity measures, and grasp success between 81 and 87% across single-object and mixed scenes, outperforming the GraspNet baseline under the tested conditions. The framework offers an accurate and deployment-friendly route to robotic bin-picking. Full article

High-voltage grid maintenance and chemical processing demand dexterous manipulation in confined, visually occluded environments. Existing teleoperation systems present a severe cost–fidelity trade-off: industrial platforms exceed USD 60,000 yet lack spatial directional resolution, while affordable alternatives omit fingertip force sensing entirely. We present STRP (Spatial Teleoperation Research Platform), a low-cost teleoperation system with concurrent motion capture and directional haptic feedback that encodes fingertip six-axis force vectors into four-directional vibrotactile cues (left, right, dorsal, volar). The master interface is a wearable 15-DoF exoskeleton glove with Hall-effect joint sensing and a 16-channel LRA array; the slave end is a 17-DoF dexterous hand with integrated micro six-axis force/torque sensors. Experimental validation demonstrates 97.5% blind discrimination accuracy for directional cues and an end-to-end latency of approximately 28 ms. In teleoperated pick-and-place tasks, multi-directional tactile feedback reduced task completion time by 41% (31.2 s to 18.4 s) and peak grip force by 32% (4.11 N to 2.79 N) versus no-feedback baselines. In visually occluded wall exploration, directional feedback enabled systematic location of all target walls, whereas single-site volar feedback permitted detection of only volar-facing surfaces and no-feedback conditions precluded any spatial reasoning. These results identify spatial directional vibrotactile feedback as a distinct sensory channel that cannot be substituted by magnitude-only vibration or vision alone. STRP establishes a foundation for affordable, high-fidelity haptic teleoperation in hazardous environments. Full article

Tendon-cable transmission can reduce distal-limb inertia in full-size humanoids, but its elasticity, hysteresis, backlash, and multi-joint coupling introduce state-dependent joint-to-motor discrepancies. We present a hierarchical whole-body tracking framework for the 28-DoF Droid X3 that separates high-level motion learning from transmission compensation. A reference-residual policy is trained in simulation by single-stage proximal policy optimization (PPO) using a unified robot-space motion representation, globally anchored tracking rewards, hierarchical hard-example sampling, and tendon-oriented domain randomization. In simulation checkpoint evaluation, more than 90% of 12,674 tested reference motions are completed. Independently, a state-conditioned mapper is trained offline through a differentiable motor–joint forward model identified from physical motor-excitation data and connected in series between the frozen policy and the low-level motor controller. Randomized repeated Mapping-OFF/ON trials are conducted on two nominally identical Droid X3 units. Within every robot–motion block, the frozen PPO checkpoint, reference trajectory, controller settings, safety bounds, and frozen mapper weights are held fixed; complete trials are the statistical units. OFF converts desired joint positions with the robot-specific fixed static calibration, whereas ON feeds the complete policy-level desired-joint vector and measured plant state to the frozen mapper, which directly outputs the complete motor-position command. Across the complete physical trials, the aggregate action-completion rate is 68% with Mapping OFF and 79% with Mapping ON, an increase of 11 percentage points. Representative walk, squat, and dance trajectories illustrate lower tracking errors under Mapping ON, while individual frames and selected temporal fragments are used only for visualization. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

Recently, microgrid (MG) structures include a mix of renewable energy sources (RES) and conventional sources. At high levels of RES penetration, reduced inertia and frequency stability have been confirmed in several studies. Properly designed and structured load frequency control (LFC) and virtual inertia control (VIC) are feasible solutions to these problems. In this paper, a new hybridized two-degree-of-freedom (2DOF) non-integer controller is proposed for multi-generation, multi-area MGs’ frequency regulation. The proposed new LFC is based on a 2DOF tilt-integral/tilt-derivative-double-derivative controller with a filter (TI-TD²F²). Meanwhile, the proposed design process considers coordinating capacitive energy storage (CES) to help regulate frequency deviation, as well as the high penetration of RESs (wind and PV). The incorporation of CES participation in frequency regulation helps provide fast VIC for the studied multi-MG system. Furthermore, an Enhanced Escape Algorithm (EESC) optimization algorithm is proposed to simultaneously optimize the control parameter set of the two-area MG system. The proposed EESC optimization algorithm identifies appropriate parameters for controller design, yielding better overall dynamic performance. An enhanced Escape Algorithm (EESC) is based on boosting the searching mechanism of the conventional Escape Algorithm by the integration of three modifications, including the Chaos map logistic mutation mechanism, the Fitness distance balance mechanism, and the Sorted Quasi-oppositional based learning (SQOBL). The proposed 2DOF TI-TD²F² controller demonstrates improved frequency stability and sustainable operation under load changes, variation in RESs, and other uncertainties of system parameters. The obtained results showed that the proposed EESC optimization algorithm adjusts the parameters of the TI-TD²F² controller, which significantly improves the dynamic performance in load frequency and tie-line power control. Compared to traditional TID and FOPID controllers, TI-TD²F² achieves up to a 70–80% reduction in tie-line power deviation and up to 60% faster settling time in many scenarios, demonstrating better robustness, faster response, and better overall system stability. Full article

Accurate actuator modeling is critical for robust design validation and sim-to-real control transfer in humanoid robotics. Yet, in practice, developers rely on simplified actuator models built from sparse datasheets or offline system identification, which often omit internal control logic, saturation, sensor dynamics, and electromechanical actuator dynamics. This limits model fidelity under changing conditions and contributes to sim-to-real failures. We propose actuator Digital Twins (DTs) as a scalable solution for predictive simulation. In this work, predictive simulation is defined as the forward computation of joint position and actuator torque from prescribed reference trajectories, controller parameters, mechanical configuration, and initial conditions, with prediction accuracy evaluated against measurements from the physical actuator. We validate a DT of the Pulsar PULSE115 quasi-direct-drive actuator that reproduces the actuator electromechanical dynamics, physical operating limits, sensing characteristics, and embedded cascaded controller executed at 10 kHz on a 1-DOF pendulum testbed, comparing real-world experiments with simulations using both the DT and a simplified model. Across varying trajectories and configurations, the DT maintains low error-from-real, while the simplified model degrades outside its tuned regime, particularly under changes in trajectory dynamics, mechanical load, and controller gains. We further embed the DT in a 4-DOF humanoid arm simulation and show that it runs significantly faster than the real-time version, achieving a simulation speedup factor of approximately 6.3× on a standard laptop. These results demonstrate that actuator-specific electromechanical and embedded control modeling improves the forward prediction of physical actuator behavior while remaining computationally practical for multi-joint robotic simulation. Full article

The growing demand for virtual reality and immersive applications has increased interest in 360° video streaming. When viewing omnidirectional content through a head-mounted display, users observe only a limited portion of the content, i.e., the viewport, at any given time. Consequently, transmitting the complete panoramic frame at uniformly high quality is bandwidth-inefficient. This review presents a system-level analysis of viewport-adaptive three-degree-of-freedom (3DoF) 360° video streaming, focusing on the coupled roles of viewport prediction, tile-based multi-rate encoding and bitrate allocation, transport mechanisms, and edge-assisted processing. The reviewed literature is examined to identify the design dependencies and trade-offs among these components. Viewport-adaptive approaches seek to reduce the bandwidth allocated to regions outside the instantaneous viewport while preserving the quality of the visible region. The analysis shows that their effectiveness cannot be attributed to prediction accuracy alone: the resulting Quality of Experience (QoE) depends jointly on tile granularity, bitrate allocation, buffer occupancy, transport delay, and whether prioritized tiles arrive before their playback deadlines. Finer tiling can improve spatial selectivity but increases coding, signaling, and request overhead. Moreover, HTTP/2, HTTP/3/QUIC, RTP/RTSP, and WebRTC present different reliability, latency, congestion-control, and scalability trade-offs across buffered video-on-demand, low-latency live streaming, and interactive immersive applications. Based on this synthesis, the review formulates a unified closed-loop cross-layer framework that coordinates prediction, tiling, bitrate allocation, request timing, transport configuration, buffering, and edge processing under bandwidth, latency, and resource constraints. Full article

To address the difficulty of simultaneously optimizing execution time, energy consumption, and motion smoothness for six-degrees-of-freedom (6-DOF) industrial robotic manipulators in continuous operations such as high-speed handling and assembly, this study proposes a multi-objective joint-space trajectory optimization method based on an improved Dung Beetle Optimizer (IDBO). First, to adapt DBO to constrained multi-objective trajectory optimization, an external archive, nondominated sorting, and a crowding distance mechanism were incorporated to construct and maintain the Pareto solution set. Second, Sobol low-discrepancy sequence initialization was used to improve the initial population distribution. Adaptive Lévy flight perturbation and an adaptive random perturbation mutation strategy for non-elite individuals were further combined to enhance global exploration and reduce the risk of premature convergence. Finally, seventh-degree B-spline curves were adopted to construct a continuous joint-space trajectory model. Based on this model, a multi-objective trajectory optimization model was established by considering total execution time, energy consumption, and jerk as the optimization objectives. Furthermore, simulation experiments were conducted using MATLAB R2024a, and the proposed algorithm was compared with multi-objective particle swarm optimization (MOPSO), an improved multi-objective differential evolution algorithm (GMODE), the nondominated sorting genetic algorithm II (NSGA-II), and the multi-objective Dung Beetle Optimizer (MODBO). The results showed that the proposed algorithm obtained a Pareto front with better convergence, wider coverage, and a more uniform distribution. Compared with MOPSO, GMODE, NSGA-II, and MODBO, the mean hypervolume (HV) obtained by IDBO was 13.24%, 8.43%, 2.29%, and 2.34% higher, respectively; the mean inverted generational distance (IGD) was 9.45%, 26.68%, 16.35%, and 11.05% lower, respectively; and the mean Spacing value was 55.09%, 64.81%, 58.95%, and 14.06% lower, respectively. The execution time, energy consumption index, and joint jerk of the selected compromise solution were 5.27 s, 2.48, and 9.79, respectively, which were 29.73%, 43.51%, and 18.14% lower than those of the unoptimized trajectory. Constraint verification showed that the peak joint velocities, accelerations, and jerks remained within their prescribed limits. These results indicate that the proposed method provides a feasible approach for multi-objective joint-space trajectory planning of industrial robotic manipulators. Full article

With the growing global fleet of motor vehicles and rising demand for unmanned services, enhancing the efficiency and intelligence of refueling operations at gas stations has become a critical industry priority. This review focuses on refueling robots as its core research subject, providing a systematic review of its developmental history and system architecture. Building upon this foundation, this review conducts an in-depth analysis and synthesis of three key enabling technologies: (1) the end effector—integrating multi-degree-of-freedom actuators and sensor modules to precisely control fuel tank lid actuation and fuel nozzle insertion/removal; (2) refueling interface identification—enabling vehicle-type classification, refueling interface location extraction, and recognition of refueling interface features; and (3) refueling interface localization—determining the 6 DoF pose of the refueling interface relative to the robot. Through this technical analysis, it is shown that refueling robots have attained an initial level of intelligence; however, significant challenges remain in achieving high precision and robust performance, ensuring safety and reliability, and establishing standardization and broad interoperability. Future research efforts should therefore prioritize improving environmental adaptability—particularly in complex, unstructured settings—advancing autonomous decision-making capabilities, and enhancing product universality, thereby accelerating the commercial deployment of refueling robots. Full article

To address high-precision trajectory tracking challenges in multi-joint robots facing model uncertainties, complex friction, and abrupt disturbances, this paper proposes a radial basis function (RBF) neural network-improved integral terminal sliding mode control scheme optimized by state-aware adaptive particle swarm optimization (ASPSO), denoted as ASPSO-optimized RBF-IITSMC. First, a fractional-memory integral terminal sliding surface incorporating a boundary-layer saturation mapping is constructed. The proposed terminal mapping is shown to be globally Lipschitz continuous, and an explicit approximation-error bound relative to the conventional terminal power mapping is established. Second, an RBF neural compensator driven by the sliding variable is incorporated into the reconstructed sliding dynamics to estimate lumped uncertainties and reduce the compensation burden on the robust feedback term. Furthermore, a state-aware adaptive PSO variant combining population-diversity monitoring and differential mutation is developed to jointly tune the 15-dimensional controller parameter vector. The practical finite-time reachability of the sliding variable and the uniform ultimate boundedness of the sliding variable and neural-weight estimation error are analyzed using a Lyapunov framework. Simulation results on a six-degree-of-freedom (6-DOF) robotic arm demonstrate improved tracking accuracy and disturbance-rejection performance, together with reduced high-frequency torque oscillations, compared with the evaluated baseline controllers. Full article

As core components for ultra-precision positioning, grating encoders have technologically evolved toward higher accuracy, faster speeds, and multi-degree-of-freedom (multi-DOF) integration. This review systematically traces the technological progression of commercial grating encoders from imaging scanning to interferential scanning, from incremental to absolute encoding, and from single axis to multi-DOF integration. It compares the distinct technical strategies adopted by Heidenhain and Renishaw for robustness enhancement and absolute encoding within the imaging scanning paradigm. It also uncovers the physical mechanism of optical subdivision that allows interferential scanning encoders to surpass the sub-nanometer resolution barrier. Building upon this foundation, recent academic advances in multi-DOF interferometric measurement systems and Fizeau interferometer-based grating self-calibration methodologies are also reviewed. Full article

Stroke and other neurological injuries impair hand function. Although rehabilitation therapists encourage reintegration of the affected hand into daily activities, there are few tools that can be worn during everyday life that provide quantitative feedback on how much or how effectively the hand is used. Wearable sensors that can accurately track hand movements and are easily applied and removed can present intuitive feedback that could motivate hand use similarly to how pedometers encourage walking. While tracking all the hand and finger joints is needed for scientific studies, under-sensorization, or using fewer sensors than required for tracking all degrees of freedom, may suffice for providing users feedback about hand use in everyday life. Further, it may enable a wearable device to be more easily donned and doffed, more power efficient, and more cost efficient. Here we develop a low-cost, multi-sensor, wireless wearable system for tracking selected hand and wrist movements during everyday life. The system includes fabricated soft, stretchable “e-skin” bend sensors and off-the-shelf inertial measurement units (IMUs) that accurately measure finger bend angles and wrist movements. The system also includes an application and removal protocol that enabled naïve unimpaired participants to apply and remove the system in ~5 min and ~4 min, respectively. The system cost was $111 per device, with prices falling to an estimate of $55 when manufactured at scale. This hand wearable demonstrates accurate kinematic tracking and user-friendly donning/doffing workflows for unimpaired participants, making it a promising platform for everyday hand tracking. Future work will extend this platform to the movement-impaired population for neurorehabilitation applications. Full article