Search Results (175)

Autonomous driving in underground mines faces significant challenges due to Global Navigation Satellite System (GNSS) denial and harsh environmental conditions. Mainstream multi-sensor fusion and Simultaneous Localization and Mapping (SLAM) schemes have achieved substantial progress in underground navigation, but their deployment in feature-sparse tunnels may still face challenges related to computational burden and perception robustness. This study explores an infrastructure-assisted navigation architecture that transforms the roadway into a structured luminous guidance channel by deploying programmable Light Emitting Diode (LED) strips along the tunnel roof. The proposed system simplifies complex three-dimensional pose estimation into a two-dimensional visual servoing task targeting optical signals. Central to this approach is a robust data fusion strategy that utilizes a topology matching algorithm to map noisy Ultra-Wide-band (UWB) coordinates onto a discrete LED index space, thereby providing a reliable global positioning reference. Furthermore, a hierarchical fault-tolerant controller based on a Finite State Machine (FSM) is designed to facilitate seamless degradation to a UWB-assisted ultrasonic wall-following mode in the event of visual degradation, supporting fault-tolerant operation under controlled laboratory conditions. Experimental results in a laboratory simulation environment demonstrate that the system achieves millimeter-level static initialization accuracy, a dynamic tracking Root Mean Square Error of approximately 4 cm, and a 100% autonomous recovery rate from visual failures in straight tunnels. These results demonstrate the feasibility of the proposed infrastructure-assisted route under controlled laboratory conditions and suggest its potential as an engineering reference for structured underground transport scenarios with acceptable infrastructure modification. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This~paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40~m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

Sol–gel-based optical functional sensor coatings were developed for real-time monitoring of multiphase saponification reactions in microreactors. Various pH-sensitive indicator mixtures, including bromocresol green and bromocresol purple (BCG and BCP) and methyl red–methyl orange, were incorporated into sol–gel coatings and evaluated on test plates across pH range of 2–12. Coatings with BCG and BCP 1:3 demonstrated the most pronounced color change at high pH (11–12), with distinct hue (H) transitions providing a reliable measure of local pH. These optimized coatings were integrated into microreactor channels to track the passage of oil and NaOH slugs under varying flow rates. Hue analysis produced reproducible plateaus corresponding to NaOH-rich (H = 50°) and oil-rich (H = 41°) phases, enabling droplet-level resolution of slug flow and detection of flow-regime transitions. The sensor response was fully reversible, highlighting the robustness and reusability of the coatings. Unlike previous high-resolution fluorescence-based systems, this approach relies on simple visible-light imaging and low-cost data extraction, leaving the reaction chemistry unaltered. The results demonstrate that sol–gel coatings coupled with hue-based analysis provide a practical, noninvasive, and real-time monitoring strategy for multiphase reactions in microreactors, with potential for implementation in industrial or IoT-enabled process control systems. Full article

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a two-stage autonomous navigation framework tailored for large-inertia UAVs. The framework integrates: (1) an enhanced LiDAR model with physical optical noise for improved simulation fidelity; (2) an ESDF + OctoMap dual-map construction method supporting global search and local optimization; and (3) a global BIT* planner combined with a B-spline local optimizer embedding dynamic, smoothness, and tracking accuracy constraints to ensure path feasibility and trackability. Simulation results demonstrate an average planning time of 0.86 ms, outperforming NAVIGATION, Informed RRT*, MPC Planner, and ESDF Optimization by 29.6–52.0%, with a 100% obstacle avoidance success rate and trajectory tracking RMSE of 0.28 m over a 350 m flight distance, along with strong parameter and noise robustness. Actual flight tests on a 9.4 kg quadrotor UAV confirm the algorithm’s effectiveness in map construction, path planning, and obstacle avoidance in environments with 15 obstacles, while maintaining computational overhead suitable for onboard deployment. These results establish the proposed framework as an effective solution for high-speed autonomous navigation of large-inertia UAVs in complex near-ground environments. Full article

As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed on the precision and adaptability of installation error calibration techniques for EO pods. Current mainstream calibration methods typically require specialized procedures under constrained conditions, while few approaches integrate existing UAV system capabilities and mission requirements, which leads to cumbersome, time-consuming processes and suboptimal alignment between calibration outcomes and task objectives. This paper proposes an online calibration method for UAV EO pod installation errors based on target tracking, which can rapidly compute the optimal closed-form solution for installation errors by leveraging UAV tracking missions. First, an observation equation for pod installation errors is established using tracking results. Second, multi-temporal observations are combined to model the calibration problem as an optimal rotation matrix estimation task, and then the optimal closed-form solution for installation errors is derived. Concurrently, a statistics-based approximate calibration method is introduced specifically for tracking missions. Furthermore, an online calibration system compatible with diverse UAV platforms is designed, along with different rapid calibration schemes for emergency response scenarios, fully incorporating existing system capabilities and mission needs. Finally, a fixed-wing UAV experimental platform is developed, with calibration tests conducted under various flight regimes. Experimental results validate the feasibility and robustness of the proposed methodology. Full article

Optical reflective markers are widely used in precision medicine, computer-assisted surgery, and robotic interventions. Nevertheless, intraoperative tracking still faces challenges such as sensor saturation, Point Spread Function (PSF) blooming, and flat-top artifacts, which affect localization precision and stability. Traditional deep learning detectors perform well in general object recognition but are limited in handling saturated infrared reflective markers due to their neglect of optical physics and inability to separate signal from blooming interference. This paper presents a physics-prior-guided network integrating a Brightness-Prior-Enhanced Spatial Attention (BPESA) mechanism for high-precision sub-pixel marker localization under saturation conditions. The method achieves a Root Mean Square (RMS) error of 0.52 pixels (approximately 0.11 mm) on a dataset of 8000 binocular images and reduces the localization error by approximately 54.4% compared with the baseline YOLOv8 model, while maintaining an inference speed of 134.6 FPS. The results demonstrate that optical blooming interference can be effectively mitigated by a learnable physics-prior branch, providing accurate marker coordinates that form a foundation for potential downstream tracking or navigation tasks. Full article

In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered stacking (OPG-TCS), a tracking-oriented post-processing method designed to stabilize target energy accumulation and improve enhancement reliability under dynamic observation conditions. OPG-TCS performs frame-wise astrometric calibration using star fields (WCS) and leverages projected orbit priors to predict target pixel locations, enabling local cropping and target-centered alignment/stacking without relying on full-frame geometric consistency. We evaluate OPG-TCS on multiple real-world dynamic-platform sequences and compare it with direct stacking and representative robust baselines. Signal-to-noise ratio (SNR) is used as the primary metric, while auxiliary indicators of peak prominence, energy concentration, and shape consistency are employed to assess robustness across varying stacking depths. The results show that OPG-TCS provides stable enhancement over different frame counts; in representative 50-frame fusions, its relative SNR surpasses direct stacking by 33.7–97.8%. These findings suggest that OPG-TCS offers a practical and robust enhancement strategy for SST-oriented observation of faint space objects, supporting more reliable detection and subsequent tracking analysis. Full article

To address the escalating security challenges posed by unauthorized Unmanned Aerial Vehicles, this paper presents a Sim2real physics-informed audio–visual fusion simulation platform designed to enhance Counter-Unmanned Aerial Vehicle detection and tracking performance. The proposed method integrates two complementary sensing pipelines: a physics-based acoustic localization system utilizing Time Difference of Arrival principles and a deep learning-driven visual detection framework. To ensure robust surveillance against non-cooperative targets, these pipelines are not only fused through strict spatiotemporal synchronization but also mutually reinforce each other—acoustic data guides visual attention in low-visibility scenarios typical of adversarial intrusions, while visual detections refine acoustic parameter estimation. Building upon prior work in multi-modal perception, we extend the framework to dynamic environments characterized by configurable visual obstructions, including smoke and fog, which frequently compromise conventional optical anti-drone systems. Experiments demonstrate that the fusion system progressively adapts to degraded visual conditions, extending tracking continuity from approximately 50% coverage under vision-only operation to near-continuous target awareness, with a moderate trade-off in average angular precision when acoustic-only segments are included. Physical validation with quadrotor Unmanned Aerial Vehicles confirms the platform’s capability to bridge simulation-to-reality gaps. Our results highlight the system’s robustness against sensor degradation and its potential to accelerate the development of resilient multisensor Counter-Unmanned Aerial Vehicle systems while reducing dependency on costly field testing. Full article

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article

Accurate temperature control in ceramic roller kilns is critical for ensuring product quality; however, it remains challenging due to nonlinear thermal dynamics and the spatial lag inherent in traditional contact-based sensors. To address the limitations of sparse wall-mounted thermocouples and optical interference in kiln images, this paper presents a multimodal spatiotemporal fusion network (MST-FusionNet) for noncontact temperature detection of ceramic bodies on roller tracks. The proposed network integrates in-furnace combustion image sequences with distributed thermocouple measurements. First, a physics-informed pseudo-heatmap generation strategy based on Gaussian distributions is introduced to align discrete thermocouple readings with visual features, enabling effective early-stage multimodal fusion. Second, a residual compensation mechanism uses thermocouple data as a stable reference to learn local temperature deviations from visual and temporal features. In addition, an attention-enhanced LSTM module is employed to model combustion dynamics and suppress unreliable frames caused by smoke and flame fluctuations. Experimental results on a real industrial dataset show that the proposed method achieves a mean absolute error of 0.9164 °C and a root mean squared error of 1.2422 °C, demonstrating better performance than single-modal methods and simple fusion baselines. The proposed framework exhibits stable spatial characteristics across different roller positions and helps bridge the spatial discrepancy between boundary measurements and the actual thermal state of ceramic products, providing an effective solution for temperature detection in roller kilns. Full article

This paper presents the structural health monitoring (SHM) system applied to a 9 m composite outer wing box (OWB) specifically designed for a brand-new regional aircraft to detect barely visible impact damage (BVID) based on structural response data. The approach relies on different technologies to offer multilevel diagnosis, including impact detection as well as disbonding identification, localization, and sizing. The use of different sensing techniques based on piezoelectric transducers and distributed fiber optic sensors deployed all over wing structures is explored. Different features are simultaneously extracted from the propagating waves and from light scattering, able to detect low-energy BVID impact. In addition, the combined use of static and dynamic interrogation allows the estimation of the delamination surface after impact with good accuracy. The final test results on the OWB provided effectiveness in detecting, localizing, and tracking impact damage in the composite structure, ensuring long-term reliability and safety, as well as characterizing barely visible damage by a fully integrated onboard SHM system. Full article

Mid-IR flat/integrated optics require low-loss, programmable phase control. We investigate femtosecond laser direct writing (FLDW) in aluminogermanate glass (Corning 9754), first mapping the processing landscape to delineate no modification, Type I index increase, and spatial broadening regimes. We then operate in a non-accumulating regime that provides a broad, stable writing window. Quantitative-phase microscopy yields Δφ and a monotonic Δn with optically limited cross-sections compatible with low loss. Transmission spectroscopy shows high values (about 90% up to 4 µm) and no additional absorptions across the near-IR and mid-IR range. FTIR reveals a redshift of the Ge–O–(Ge/Al) stretching envelope from ≈1 µJ, correlating with the high Δn onset. s-SNOM at 925 cm⁻¹ resolves the written line as reduced near-field amplitude and decreased phase, confirming a local complex permittivity change consistent with densification-driven Type I tracks. Together, these results define practical conditions for on-demand mid-IR flat/GRIN/Fresnel optics by FLDW in this commercial mid-IR transparent glass. Full article

A photoelectric tracking system is a typical bearing-only target tracking system that faces significant challenges arising from measurement origin uncertainty due to clutter and the discrepancy between continuous-time target dynamics and discrete-time optical sampling, as well as the inherent nonlinearity of bearing-only tracking. This paper addresses these issues by proposing a novel distributed probabilistic data association feedback particle filter (DPDA-FPF) framework. To resolve the tracking ambiguity at the local level, we extend the feedback particle filter to a continuous-discrete setting integrated with probabilistic data association. Subsequently, the local state estimates and covariances from spatially separated tracking systems are transmitted to a fusion center and integrated using an optimal linear covariance-weighted fusion rule to improve global observability and mitigate biases of individual systems. Numerical simulations in a 3D scenario with moderate clutter density demonstrate that while individual sensor tracks suffer from fluctuations, the proposed fused estimate achieves substantially lower root mean square errors in both position and velocity. The results validate the efficiency of the proposed architecture as a robust solution for photoelectric tracking applications. Full article

Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes. Full article

Networks of aromatic amino acid residues within microtubules, particularly those formed by tryptophan, may serve as pathways for optical information flow. Ultraviolet excitation dynamics in these networks are typically modeled with effective non-Hermitian Hamiltonians. By extending this approach to a Lindblad master equation that incorporates explicit site geometries and dipole orientations, we track how correlations are generated, routed, and dissipated, while capturing both energy dissipation and information propagation among coupled chromophores. We compare localized injections, fully delocalized preparations, and eigenmode-based initial states. To quantify the emerging quantum-informational structure, we evaluate the $L_1$ norm of coherence, the correlated coherence, and the logarithmic negativity within and between selected chromophore sub-networks. The results reveal a strong dependence of both the direction and persistence of information flow on the type of initial preparation. Superradiant components drive the rapid export of correlations to the environment, whereas subradiant components retain them and slow their leakage. Embedding single tubulin units into larger dimers and spirals reshapes pairwise correlation maps and enables site-selective routing. Scaling to larger ordered lattices strengthens both export and retention channels, whereas static energetic and structural disorder suppresses long-range transport and reduces overall correlation transfer. These findings provide a Lindbladian picture of information flow in cytoskeletal chromophore networks and identify structural and dynamical conditions that transiently preserve nonclassical correlations in microtubules. Full article