Search Results (159)

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

End-of-life electric-vehicle and plug-in hybrid (EV/PHEV) battery packs pose a recurrent decision: refurbish, redeploy in second-life storage, recycle, or reject. Technical condition, safety, economics, regulation, traceability, and environmental benefit interact, making pathway selection a systems-level decision problem. This paper develops a risk-informed multi-criteria framework for the conditional viability of refurbished batteries under data-scarce conditions. Failure mode, effects, and criticality analysis (FMECA) supplies a pathway-specific residual-risk penalty; multi-criteria decision analysis (weighted-sum and the Technique for Order of Preference by Similarity to Ideal Solution, TOPSIS) orders four alternatives on six benefit criteria; and a screening-level avoided-burden indicator, not a life-cycle assessment, positions the environmental criterion. An Integrated Viability Index (IVI) offsets weighted benefits against the risk penalty through one tunable coefficient. All inputs are illustrative and literature-informed; the demonstration tests decision logic, not empirical pathway performance. Preference is conditional: refurbishment leads under economic and technical priority with credible risk mitigation, second-life reuse under environmental priority, and recycling under safety, regulatory, and infrastructure constraints, while rejection never leads. As the risk penalty rises, leadership migrates traceably toward recycling, and IVI–TOPSIS divergence localizes exactly where the risk treatment changes the decision. A proposed Refurbished-Battery Suitability Index (RBSI) would couple measured diagnostics to the IVI; its calibration remains future work. Full article

Industrial process safety systems are predominantly reactive: alarms activate after limits are crossed, faults are diagnosed after deviations develop, and HAZOP knowledge remains offline during operation. This paper proposes Predictive Operational Safety Engineering (POSE) as an emerging research paradigm in which operational safety is treated as a continuously forecastable state rather than a post-event classification, shifting the operational question from what has gone wrong? to how much safe operating time remains, and which intervention is most urgent? Four integrated predictive safety metrics anchor the framework: Remaining Safety Margin (RSM), quantifying the normalized distance between the predicted process trajectory and the nearest safety boundary; Remaining Safe Operating Time (RSOT), estimating when that boundary will be crossed under the current trajectory; the Operational Vulnerability Index (OVI), combining margin depletion rate, safeguard availability, and consequence severity into a single intervention-urgency signal; and Predictive Safety Confidence (PSC), the probability that a specific named operator intervention can be executed to completion before the predicted safety boundary is crossed, coupling prediction uncertainty with action execution time. The Predictive Operational Safety Twin (POST) is proposed as a three-layer reference architecture implementing POSE through predictive process intelligence, predictive safety intelligence, and human safety intelligence. The paper synthesizes six research streams, positions POSE against seven adjacent disciplines, states ten guiding principles, and formulates a research agenda. As a conceptual narrative review, the paper does not claim empirical validation of POSE. Instead, it establishes the foundational vocabulary, reference architecture, and research agenda required to advance predictive operational safety from an emerging concept toward benchmarked and industrially validated practice. This article constitutes the conceptual and evidence-synthesis phase of a staged research program; subsequent work must test the proposed constructs through benchmark simulation, uncertainty calibration, baseline comparison, operator studies, and industrial case studies. Full article

High-precision and resilient indoor positioning is a fundamental requirement for the autonomous operation of mobile robots in GNSS-denied environments. While visual sensors are commonly used for odometry, their operational reliability can be compromised in challenging scenarios such as drastic illumination fluctuations and sparse-textured environments. To address these sensor limitations, this study presents MVO, a magneto-visual odometry framework that explores indoor magnetic field anomalies as complementary constraints for visual odometry. By integrating a 30-magnetometer planar array model with a stereo camera, the proposed system establishes a multi-modal perception framework for indoor spaces. In the frontend, magnetic field gradient information is utilized to provide relative-pose constraints, which assist in the matching of image feature points and help maintain tracking continuity under visual degradation. In the backend, a factor graph optimization (FGO) framework incorporates magnetic relative-pose factors and visual reprojection factors into a unified optimization objective, which is then solved using the incremental smoothing and mapping 2 (iSAM2) algorithm. Frontend-level simulations are conducted to analyze the effects of magnetometer spatial configuration, sensor number, and calibration-error sensitivity on magnetic relative-pose estimation and covariance. Trajectory-level evaluations are further performed on the EuRoC dataset augmented with high-fidelity synthesized magnetic field data, including localization accuracy and computational load. Under this synthesized magnetic field setting, MVO shows improved localization accuracy and moderate computational load compared with the selected MSCKF-Stereo and VINS-Fusion reference baselines. These results provide a simulation-based feasibility validation of integrating magnetic field constraints with visual information for indoor odometry, while validation with real magnetometer array measurements remains future work. Full article

Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damped least squares (DLS) are sensitive to initialization, particularly near singularities. We propose XGNN, an explainable graph neural network positioned as a model-free, interpretable warm-start initializer for downstream numerical IK refinement rather than as a standalone replacement for analytical solvers. Each IK query is encoded as a 12-node graph in which six pose nodes and six joint nodes are connected through bipartite pose-to-joint attention edges and chain edges along the kinematic structure. GATv2 message passing aggregates information at each joint node; two ablation-validated design contributions (a learnable node-type embedding and an angle-aware composite loss) enable training to convergence. Evaluated on 300,000 trajectory-style samples generated from the ABB IRB 2400 kinematic model, XGNN achieves ${3.66}^{\circ }$ joint mean absolute error (MAE), comparable to a multilayer perceptron baseline (${3.09}^{\circ }$) and a bidirectional LSTM (${3.14}^{\circ }$) under identical training. The standalone joint accuracy of all learned models is too coarse for direct industrial use, but XGNN provides the strongest warm start for DLS refinement: the convergence rate improves from $98.4\%$ to $100\%$, mean iterations drop from 14.6 to 3.2, and wall-clock time per pose drops $5.0\times$ on the IRB 2400. The benefit transfers cross-platform to the Universal Robots UR5 collaborative manipulator (convergence rate $82.2\%$ to $100\%$, $10.0\times$ speedup) and survives DH parameter perturbation of up to $\pm 10\%$, simulating calibration drift or mechanical wear. The GATv2 attention coefficients additionally provide an interpretability signal at zero inference cost. XGNN therefore complements analytical and numerical IK methods as an interpretable, calibration-robust warm start when DH parameters are unavailable, proprietary, or degraded. Full article

The accuracy of optical tracking tools is crucial for surgical navigation. While commercial tools are reliable, their proprietary design knowledge limits accessibility and adaptability for specialized clinical and research applications. This study introduces an open, reproducible optimization framework based on point-based rigid registration theory, defining a unified pose estimation deviation metric and deriving its analytical expression for both expectation and variance. The approach incorporates constraints for intra-group uniqueness and inter-group compatibility, using exhaustive configuration generation and geometric evaluation to rank designs by predicted accuracy. Numerical simulations confirmed the derived formula, with under 5% average prediction error for the expectation and strong agreement for the variance. Optimized four-fiducial tools were compared to commercial references via tip calibration, distance measurement, and registration tests. Most optimized tools (75%) achieved accuracy comparable to or modestly better than commercial tools (e.g., tip calibration 0.22 mm vs. 0.28 mm, distance measurement 0.18 mm vs. 0.20 mm, FRE 0.13 mm vs. 0.16 mm, TRE 0.48 mm vs. 0.51 mm). All four experiments showed a strong positive correlation between the theoretical metric and measured error (Pearson’s $r>0.98$, all $p<2.2\times {10}^{-7}$; exact Spearman $p\le 2.8\times {10}^{-6}$). Beyond these numerical results, the primary contribution is a systematic, open design methodology that formalizes knowledge historically proprietary to commercial vendors, enabling researchers and engineers to generate high-precision custom tracking tools for diverse surgical navigation scenarios. Full article

Material discontinuities and defects can profoundly impact the structural integrity and overall product quality. In a multitude of industries, ranging from aerospace and automotive to the nuclear sector and manufacturing, even surface discontinuities can pose significant risks to component reliability. This paper presents a planar microwave sensor for non-destructive testing (NDT) to quantify the electromagnetic response to controlled crack-like discontinuities in polylactic acid (PLA) materials. The sensor comprises a host coplanar waveguide (CPW) positioned at the base of an RO3210 substrate and a multiple split-ring resonator (MSRR) on the surface, creating a compact device measuring 30 mm × 50 mm × 1.27 mm. When a discontinuity-free PLA sample-under-test (SUT) is placed above the sensor, the transmission coefficient exhibits a resonance at 1.780 GHz. As the width of the groove-based discontinuity increases, a systematic blue shift in the resonant frequency is observed. The relationship between resonant frequency shift and discontinuity width is established through empirical calibration for both surface and subsurface configurations. The results demonstrate the feasibility of the proposed sensor for calibrated detection and sensitivity-based discrimination of millimeter-scale crack-like discontinuities in PLA within the tested dimensional range. Full article

Line confocal sensors provide non-contact, high-resolution, and high-efficiency measurement and can be integrated into optical measurement systems such as Photon for three-dimensional topography measurement of complex surfaces. However, installation-induced tilt-pose errors of the sensor can couple height information with lateral position, thereby reducing the accuracy of profile reconstruction. To address this issue, this paper proposes a calibration-block-based tilt-pose error identification and compensation method for line confocal sensors. Using the known geometric features of the calibration block, the proposed method establishes a mapping relationship between sensor tilt-pose errors and measured profile distortion. Sensitivity analysis is performed to identify the dominant error components, and the tilt-pose errors are estimated in a single identification process, enabling quantitative compensation of the measured point cloud. Experimental results show that, after calibration and compensation, the maximum Z-direction height difference in the overlapping profile region of the calibration block is reduced from 12.782 μm to 0.307 μm. The proposed method requires no complex external alignment devices and provides an effective approach for high-precision integrated applications of line confocal sensors. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

Volcanic gas and isotope time series are indirect observables of coupled magmatic and hydrothermal dynamics. We formulate a reduced integer–fractional model in which ordinary differential equations describe deep recharge, pressure, gas-phase volatile inventory, and source mixing, whereas Caputo equations describe shallow hydrothermal pressure, thermal excess, gas pathway effectiveness, permeability, and scrubbing. Under explicit local regularity and admissibility assumptions, the mixed-order Volterra problem is locally well-posed and the physically admissible state set is positively invariant. We derive componentwise dissipative estimates and state conditions for global continuation under bounded trajectories and analyze finite-interval consistency with the integer-order limit and local stability of a frozen commensurate hydrothermal linearization. Conservative observation equations link hidden states to gas ratios, fluxes, and isotope ratios. The inverse problem is treated diagnostically; global identifiability is not claimed. Local sensitivity screening, Fisher information concepts, and scalar recovery tests are used only as preliminary local diagnostics of information content under known or misspecified forcing. Synthetic demonstrations and a reference forward solver illustrate how hydrothermal memory and sulfur scrubbing can reshape carbon dioxide/sulfur dioxide (CO₂/SO₂) anomalies before site-specific calibration. Full article

Dual 6 degrees of freedom (6-DOF) series-parallel cooperative robot actuators are core execution components in modern intelligent manufacturing systems, which are widely used in high-end manufacturing scenarios such as aerospace precision assembly, laser precision machining, and core component assembly of new energy vehicles. However, in actual manufacturing processes, the pose deviation between theoretical model prediction and actual motion execution of the actuator, caused by kinematic model mismatch, unquantified core parameters, incomplete error processing chain, and complex on-site environmental interference, severely restricts the assembly accuracy, product qualification rate and production efficiency of the manufacturing system. To address these critical pain points of robot actuators in precision manufacturing systems, this paper proposes a four-layer hierarchical decoupling multi-modal fusion calibration algorithm for high-precision pose control of dual series-parallel robot actuators. The algorithm integrates singular value decomposition (SVD) for cross-structure coordinate alignment of heterogeneous actuators, chaotic mapping-enhanced particle swarm optimization (PSO) for nonlinear error suppression of the actuator system, attention-enhanced deep residual network (DRN) for unmodeled residual learning of the actuator, and Kalman filter (KF) for dynamic noise reduction in the manufacturing process. Meanwhile, a full-chain error transfer model of the actuator system in the manufacturing process is constructed, and the core parameters of the algorithm are quantified via dimensional sensitivity analysis and orthogonal experiments. Experimental results show that the static position error of the actuator system after calibration reaches 1.4 ± 0.08 mm, and the static pose error reaches 0.0059 ± 0.0003 rad in the laboratory environment; in the engineering application of laser precision machining in an actual manufacturing line, the position error and pose error only increase by 8.6% and 6.8% respectively, maintaining high stability in industrial manufacturing scenarios. Compared with mainstream calibration methods, the proposed algorithm reduces the position error and pose error of the actuator by up to 55.7% and 17.9% respectively, with lower computational complexity and higher engineering reproducibility. This work constructs an end-to-end error suppression chain with quantitative parameter criteria for the series-parallel actuator system in manufacturing systems, which provides a reliable high-precision calibration solution for industrial dual-robot cooperative manufacturing and has important guiding significance for improving the motion accuracy and operation stability of actuators in precision manufacturing systems. Full article

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and risk mitigation. This paper built an integrated hydrogeochemical machine learning model to predict the fluoride concentration and classify exceedance risk in the Rechna Doab aquifer Tehsil Jaranwala, Punjab, Pakistan. Nested cross-validation and independent test evaluation were performed on conventional models (linear regression, random forest, XGBoost) and a deep tabular model (FT-Transformer). Model reliability was evaluated using discrimination and probability-calibration metrics, while Shapley Additive Explanations (SHAP) and permutation importance were applied to identify the main hydrogeochemical controls on fluoride prediction. Moreover, the robustness was tested by noise sensitivity experiments. Fluoride concentrations showed a positive skewed distribution with some local exceedances related to the geogenic and hydrochemical influences. Nonlinear models greatly outperformed the linear baseline; XGBoost showed robust regression performance (test R² = 0.878; RMSE ≈ 0.190 mg/L). The FT-Transformer showed strong exceedance-classification performance, with high sensitivity (recall = 0.875) and good probability calibration (Brier ≈ 0.021). Interpretability analyses identified EC/TDS, Mg²⁺, and Ca²⁺ as important predictors, linking fluoride enrichment to chemically evolved groundwater with reduced calcium activity, sodium enrichment, and alkalinity buffering. The proposed framework provides accurate, interpretable, and risk-oriented support for groundwater fluoride monitoring in alluvial aquifer systems. Full article

This work formulates semiclassical gravity within a causal-diamond framework where a finite-resolution boundary provides the edge structure for a local Wheeler–DeWitt description. Because the diffeomorphism-invariant Hilbert space does not factorize, each diamond is equipped with a boundary-completed algebra ${\mathfrak{A}}_O$, ensuring the operational state ${\rho }_O$ and the semiclassical reference family ${\sigma }_O[\Lambda ]$ share identical operator content. Dynamics are posed as local statistical inference: the relative-entropy functional $S_{\mathrm{rel}}({\rho }_O\parallel {\sigma }_O[\Lambda ])$ quantifies the mismatch between data and reference. This yields the minimal operational axioms defining subsystems, intrinsic clocks, and regulated observables in a finite-resolution, background-independent setting. The topology-locked boundary capacity budget fixes an effective channel multiplicity $N\approx 1.23\times {10}^{11}$. Calibrating its coherent fraction to Newton’s constant determines a matching scale $M_s\approx 3.02\times {10}^{13}\mathrm{GeV}$. In the modular/KMS regime, the relative-entropy Hessian (Kubo–Mori metric) block-diagonalizes into orthogonal tensor, vector, and scalar response sectors. A heat-kernel expansion on the fixed $S^3\times S^1$ history manifold maps this near-equilibrium response to a matching-scale effective field theory, yielding the Einstein–Hilbert tensor structure, Yang–Mills susceptibilities, and leading mass deformations. Vector and scalar responses remain intensive, while the tensor response scales extensively with coherent channel multiplicity. The fixed modular protocol and quantized boundary currents imply ${\alpha }^{-1}\left(M_s\right)=4\pi k$ at integer levels k, while the reduced $R^2$ plateau sector yields linked cosmological targets: $n_s\simeq 0.965$, $r\simeq 0.0038$, and $A_s\simeq 2.1\times {10}^{-9}$. Translations between causal diamonds act as completely positive trace-preserving (CPTP) updates. The resulting open-modular Walsh filtration selects the three-dimensional degree-one sector as the algebraic basis for family structure. Treating continuum fields as the structured response of a finite boundary, the framework yields correlated, falsifiable relations for gravitational stiffness, gauge response, plateau cosmology, and threefold matter-sector organization from one minimal operational architecture. Full article

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article

Precision alignment and target contact are critical tasks for mobile manipulators in industrial inspection and flexible manufacturing. However, achieving high accuracy after navigation remains challenging due to accumulated errors from mobile base localization, perception noise, and calibration uncertainty. This paper proposes a vision-guided precision alignment framework for mobile manipulators using a single front-facing RGB-D camera. The method integrates YOLO-based target detection, AR marker-assisted plane depth estimation, and depth-based 3D localization within a coarse-to-fine alignment strategy. After navigation, the manipulator first moves to a predefined pre-alignment pose, followed by visual localization and iterative refinement to compensate for residual errors before executing precise target contact. The proposed system is implemented and evaluated in a Gazebo-based simulation environment using a mobile manipulator platform model. In a static touch panel experiment with 50 trials, the system achieves a success rate of 98%, with positioning errors maintained within a millimeter-level range. Simulation results demonstrate that the proposed method provides stable alignment performance in the simulation environment without relying on external sensing devices such as force sensors or multi-camera systems. The proposed approach shows promising potential for precision contact tasks in mobile manipulation. Full article