Search Results (255)

The imaging quality of uncooled long wave infrared (IR) cameras is always limited by the stripe non-uniformity mainly caused by fixed pattern noise (FPN). In this paper, we propose an adaptive dual-frequency denoising network-based stripe non-uniformity correction (NUC) method, namely ADFDNet, to realize the balance between FPN removal and image detail preservation. Our ADFDNet takes the dual-frequency feature deconstruction module as its core, which decomposes the IR image into high-frequency and low-frequency features, and performs targeted processing through detail enhancement branches and sparse denoising branches. The former enhances the performance of detail preservation through multi-scale convolution and pixel attention mechanism, while the latter combines sparse attention mechanism and dilated convolution design to suppress high-frequency FPN. Furthermore, the dynamic weight fusion of features is realized using the adaptive dual-frequency fusion module, which better integrates detail information. In our study, a 420-pair image dataset covering different noise levels is constructed for better model training and evaluation. Experiments verify that the presented ADFDNet method significantly improves image clarity in both real and simulated noise scenes, and achieves a better balance between FPN suppression and detail preservation than other existing methods. Full article

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO₂ reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO₂ substrates and additional Si-SiO₂ wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures. Full article

In industrial environments such as ports and warehouses, autonomous logistics vehicles face significant challenges in coordinating multiple vehicles while ensuring safe and efficient path planning. This study proposes a novel real-time cooperative control framework for autonomous vehicles, combining reinforcement learning (RL) and distributed model predictive control (DMPC). The RL agent dynamically adjusts the optimization weights of the DMPC to adapt to the vehicle’s real-time environment, while the DMPC enables decentralized path planning and collision avoidance. The system leverages multi-source sensor fusion, including GNSS, UWB, IMU, LiDAR, and stereo cameras, to provide accurate state estimations of vehicles. Simulation results demonstrate that the proposed RL-DMPC approach outperforms traditional centralized control strategies in terms of tracking accuracy, collision avoidance, and safety margins. Furthermore, the proposed method significantly improves control smoothness compared to rule-based strategies. This framework is particularly effective in dynamic and constrained industrial settings, offering a robust solution for multi-vehicle coordination with minimal communication delays. The study highlights the potential of combining RL with DMPC to achieve real-time, scalable, and adaptive solutions for autonomous logistics. Full article

Autonomous underwater cleaning in water pools requires reliable perception, efficient coverage path planning, and robust control. However, existing autonomous underwater vehicle (AUV) cleaning systems often suffer from fragmented software frameworks that limit end-to-end performance. To address these challenges, this paper proposes an integrated vision-based autonomous underwater cleaning system that combines global-camera AprilTag localization, YOLOv8-based dirt detection, and a multi-scale A* coverage path planning algorithm. The perception and planning modules run on a host computer system, while a NanoPi-based controller executes motion commands through a lightweight JSON-RPC protocol over Ethernet. This architecture ensures real-time coordination between visual sensing, planning, and hierarchical control. Experiments conducted in a simulated pool environment demonstrate that the proposed system achieves accurate localization, efficient planning, and reliable cleaning without blind spots. The results highlight the effectiveness of integrating vision, multi-scale planning, and lightweight embedded control for autonomous underwater cleaning tasks. Full article

Reliable 3D multi-object tracking (MOT) using distributed drones remains challenging due to the lack of active sensing and the ambiguity in associating detections from different views. This paper presents a passive sensing framework that integrates multi-view data association and 3D MOT for aerial objects. First, object localization is achieved via triangulation using two onboard RGB cameras. To mitigate false positive objects caused by crossing bearings, spatial–temporal cues derived from 2D image detections and tracking results are exploited to establish a likelihood-based association matrix, enabling robust multi-view data association. Subsequently, optimized process and observation noise covariance matrices are formulated to quantitatively model localization uncertainty, and a Mahalanobis distance-based data association is introduced to improve the consistency of 3D tracking. Both simulation and real-world experiments demonstrate that the proposed approach achieves accurate and stable tracking performance under passive sensing conditions. Full article

Hyperspectral imaging systems are widely used in precision agriculture, environmental monitoring, and mineral exploration. However, current systems often suffer from high cost, large size and weight, and considerable system complexity, which hinder their widespread deployment. To overcome these limitations and achieve a better balance between performance, cost, and portability, this work aims to develop a compact, cost-effective visible-to-near-infrared (VNIR, 400–1000 nm) hyperspectral camera based on Schwarzschild configuration and commercial off-the-shelf (COTS) components. The development followed a comprehensive methodology encompassing theoretical design, simulation, prototype assembly, and performance testing. The all-reflective optical system effectively eliminates chromatic aberration and minimizes energy loss, achieving an integration time as short as several milliseconds and a push-broom frame rate of 200 fps. The optical design leveraged optical path length theory and the unobscured Schwarzschild structure to optimize off-axis mirrors and a plane grating. Optical performance was optimized and verified using simulations, which confirmed that spot sizes at all field positions were highly concentrated and that critical distortions such as smile and keystone were controlled within several pixels. A prototype was assembled on a precision optical bench using multi-axis adjustable mounts and then integrated into a precisely machined housing, achieving a total weight less than 2 kg. Calibration verified a spectral coverage of 400–1000 nm and a resolution of 5 nm. Imaging experiments demonstrated the system’s ability to resolve subtle spectral features, successfully distinguishing different vegetations and artificial materials based on their spectral signatures—particularly the strong NIR (780–1000 nm) reflectance of vegetation versus synthetic green materials. The camera offers a high-performance, low-cost solution suitable for applications including precision agriculture, environmental monitoring, mineral exploration, and others. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

This paper addresses the degradation of imaging quality in high-resolution CubeSats caused by micro-vibrations from attitude control flywheels. It proposes a micro-vibration suppression scheme that incorporates multi-disciplinary integrated modeling, dual passive vibration isolation, and multi-level verification. A comprehensive model encompassing flywheel disturbance, optics, attitude control, and structure is developed to elucidate the transmission dynamics of micro-vibrations from the source to the optical payload. A dual suppression system utilizing silicone rubber isolators is engineered for both the disturbance source (flywheel) and the payload (optical camera). By optimizing stiffness matching and damping, it achieves a balance between isolation efficiency and stability in attitude control. A three-tier verification system comprising “numerical simulation–ground microgravity testing–on-orbit imaging” has been established. The findings indicate that the dual isolation system diminishes the pixel offset amplitude of the optical payload to under 0.1 pixels (down to the 0.02 pixel level in the high-frequency band), with an isolation efficiency of 80%. Consistent outcomes from terrestrial and orbital validation affirm the engineering viability of the plan. This research offers theoretical backing for the precise control of micro-vibrations in micro-nano satellites, thereby enhancing their utility in high-resolution remote sensing applications. Full article

The multi-finger robotic hand exhibits significant potential in grasping tasks owing to its high degrees of freedom (DoFs). Object grasping results in a closed-chain kinematic system between the hand and the object. This increases the dimensionality of trajectory tracking and substantially raises the computational complexity of traditional methods. Therefore, this study proposes the discrete finite-time convergent neurodynamics (DFTCN) algorithm to address the aforementioned issue. Specifically, a time-varying quadratic programming (TVQP) problem is formulated for each finger, incorporating joint angle and angular velocity constraints through log-sum-exp (LSE) functions. The TVQP problem is then transformed into a time-varying equation system (TVES) problem using the Karush–Kuhn–Tucker (KKT) conditions. A novel control law is designed, employing a three-step Taylor-type discretization for efficient implementation. Theoretical analysis verifies the algorithm’s stability and finite-time convergence property, with the maximum steady-state residual error being $O\left({\tau }^3\right)$. Numerical simulations illustrate the favorable convergence and high accuracy of the DFTCN algorithm compared with three existing dominant neurodynamic algorithms. The real-robot experiments further confirm its capability for precise grasping, even in the presence of camera noise and external disturbances. Full article

Robotic Spatial Augmented Reality (RSAR) systems present a unique control challenge as their end-effector is a projection, whose final position depends on both the actuator’s pose and the external environment’s geometry. Accurately controlling this projection first requires predicting the 6-DOF pose of a projector-camera unit from joint angles; however, loose kinematic specifications in many RSAR setups make precise analytical models unavailable for this task. This study proposes a novel deep learning model combining Long Short-Term Memory (LSTM) and an Attention Mechanism (LSTM–Attention) to accurately estimate the forward kinematics of a 2-axis Pan-Tilt actuator. To ensure a fair evaluation of intrinsic model performance, a simulation framework using Unity and unified robot description format was developed to generate a noise-free benchmark dataset. The proposed model utilizes a multi-task learning architecture with a geodesic distance loss function to optimize 3-dimensional position and 4-dimensional quaternion rotation separately. Quantitative results show that the proposed LSTM–Attention model achieved the lowest errors (Position MAE: 18.00 mm; Rotation MAE: 3.723 deg), consistently outperforming baseline models like Random Forest by 9.5% and 17.6%, respectively. Qualitative analysis further confirmed its superior stability and outlier suppression. The proposed LSTM–Attention architecture proves to be a effective and accurate methodology for modeling the complex non-linear kinematics of RSAR systems. Full article

Aiming at the problems of insufficient environmental perception capability of autonomous mobile robots and low multi-modal data fusion efficiency in the complex underground coal mine environment featuring low illumination, high dust, and dynamic obstacles, a reliable passable region identification method for autonomous mobile robots operating in underground coal mine is proposed in this paper. Through the spatial synchronous installation strategy of dual 4D millimeter-wave radars and dynamic coordinate system registration technology, it increases point cloud density and effectively enhances the spatial characterization of roadway structures and obstacles. Combining the characteristics of infrared thermal imaging and the penetration advantage of millimeter-wave radar, a multi-modal data complementary mechanism based on decision-level fusion is proposed to solve the perceptual blind zones of single sensors in extreme environments. Integrated with lightweight model optimization and system integration technology, an intelligent environmental perception system adaptable to harsh working conditions is constructed. The experiments were carried out in the simulated tunnel. The experiments were carried out in the simulated tunnel. The experimental results indicate that the robot can utilize the data collected by the infrared camera and the radar to identify the specific distance to obstacles, and can smoothly achieve the recognition and marking of passable areas. Full article

High-definition (HD) maps, with their accurate and detailed road information, have become a core component of autonomous vehicles. These maps help vehicles with environment perception, precise localization, and path planning. However, outdated maps can compromise vehicle safety, making map updates a key research area in intelligent driving technology. Traditional surveying methods are accurate but expensive, making them unsuitable for large-scale and frequent updates. Most existing crowdsourced map update methods focus on matching perception data with map features. However, they lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. To address this, this paper proposes an HD map change detection method that considers the uncertainty of single-source perception results. This method extracts road feature information using onboard camera and Global Navigation Satellite System (GNSS) data and improves matching accuracy by combining geometric proximity and consistency. Additionally, a probability-based change detection method is introduced, which evaluates the reliability of map changes by integrating observations from multi-source vehicles. To validate the effectiveness of the proposed method, experiments were conducted on both simulation data and real-world road data, and the detection results of single-source data were compared with those of multi-source fused data. The experimental results indicate that the probabilistic estimation method proposed in this study effectively identifies the three typical scenarios of addition, deletion, and modification in HD map change detection. Additionally, the method achieves more than a 10% improvement in both precision and recall compared to single-source data. Full article

In the research field of multi-robot cooperation, reliable and low-cost motion capture is crucial for system development and validation. To address the high costs of traditional motion capture systems, this study proposes a real-time 6D pose estimation and tracking method for multi-robot systems based on YolPnP-FT. Using only an Intel RealSense D435i depth camera, the system achieves simultaneous robot classification, 6D pose estimation, and multi-target tracking in real-world environments. The YolPnP-FT pipeline introduces a keypoint confidence filtering strategy (PnP-FT) at the output of the YOLOv8 detection head and employs Gaussian-penalized Soft-NMS to enhance robustness under partial occlusion. Based on these detection results, a linearly weighted combination of Mahalanobis distance and cosine distance enables stable ID assignment in visually similar multi-robot scenarios. Experimental results show that, at a camera height below 2.5 m, the system achieves an average position error of less than 0.009 m and an average angular error of less than 4.2°, with a stable tracking frame rate of 19.8 FPS at 1920 × 1080 resolution. Furthermore, the perception outputs are validated in a CoppeliaSim-based simulation environment, confirming their utility for downstream coordination tasks. These results demonstrate that the proposed method provides a low-cost, real-time, and deployable perception solution for multi-robot systems. Full article

Traditional monocular and stereo visual SLAM systems often fail to operate stably in complex unstructured environments (e.g., weakly textured or repetitively textured scenes) due to feature scarcity from their limited fields of view. In contrast, multi-camera systems can effectively overcome the perceptual limitations of monocular or stereo setups by providing broader field-of-view coverage. However, most existing multi-camera visual SLAM systems are primarily feature-based and thus still constrained by the inherent limitations of feature extraction in such environments. To address this issue, a multi-camera visual SLAM framework based on the direct method is proposed. In the front-end, a detector-free matcher named Efficient LoFTR is incorporated, enabling pose estimation through dense pixel associations to improve localization accuracy and robustness. In the back-end, geometric constraints among multiple cameras are integrated, and system localization accuracy is further improved through a joint optimization process. Through extensive experiments on public datasets and a self-built simulation dataset, the proposed method achieves superior performance over state-of-the-art approaches regarding localization accuracy, trajectory completeness, and environmental adaptability, thereby validating its high robustness in complex unstructured environments. Full article

The preservation and digital transmission of cultural heritage have become increasingly vital in the era of immersive media. This study introduces a real-time framework for digitizing and animating traditional performing arts, with a focus on Korean traditional dance as a representative case study. The proposed approach combines three core components: (1) high-fidelity 3D avatar creation through volumetric scanning of performers, costumes, and props; (2) real-time motion capture using multi-camera edge processing; and (3) motion-to-avatar animation that integrates skeletal mapping with physics-based simulation. By transmitting only essential motion keypoints from lightweight edge devices to a central server, the system enables bandwidth-efficient streaming while reconstructing expressive, lifelike 3D avatars. Experiments with eight performers and eight cameras achieved low latency (~200 ms) and minimal network load (<1 Mbps), successfully reproducing the esthetic qualities and embodied gestures of Korean traditional performances in a virtual environment. Beyond its technical contributions, this framework provides a novel pathway for the preservation, dissemination, and immersive re-experiencing of intangible cultural heritage, ensuring that the artistry of traditional dance can be sustained and appreciated in digital form. Full article