Search Results (248)

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

Environmental perception is crucial for achieving autonomous driving of auxiliary haulage vehicles in underground coal mines. The complex underground environment and working conditions, such as dust pollution, uneven lighting, and sensor data abnormalities, pose challenges to multimodal fusion perception. These challenges include: (1) the lack of a reasonable and effective method for evaluating the reliability of different modality data; (2) the absence of in-depth fusion methods for different modality data that can handle sensor failures; and (3) the lack of a multimodal dataset for underground coal mines to support model training. To address these issues, this paper proposes a coal mine underground BEV multiscale-enhanced fusion perception model based on dynamic weight adjustment. First, camera and LiDAR modality data are uniformly mapped into BEV space to achieve multimodal feature alignment. Then, a Mixture of Experts-Fuzzy Logic Inference Module (MoE-FLIM) is designed to infer weights for different modality data based on BEV feature dimensions. Next, a Pyramid Multiscale Feature Enhancement and Fusion Module (PMS-FFEM) is introduced to ensure the model’s perception performance in the event of sensor data abnormalities. Lastly, a multimodal dataset for underground coal mines is constructed to provide support for model training and testing in real-world scenarios. Experimental results show that the proposed method demonstrates good accuracy and stability in object-detection tasks in coal mine underground environments, maintaining high detection performance, especially in typical complex scenes such as low light and dust fog. Full article

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, whether from anthropogenic or natural sources, can pose serious health risks. This paper offers a comprehensive review and meta-analysis of urban AQ literature published in the region over the past decade (2013–June 2025). We aim to provide guidance and highlight key directions for future research in the field. This paper examines key pollutants, emission sources, implications of urban sources, and the most studied countries, methodologies, limitations, and recommendations from different case studies. Our analysis reveals a significant research gap highlighting insufficient recent literature. Saudi Arabia was the most studied country with 20 papers, followed by the broader Arabian Peninsula (sixteen), Qatar (twelve), the United Arab Emirates and Iraq (seven each), Kuwait (four), Oman (three), Jordan, and Bahrain (one each). The primary methods employed included measurements and sampling (28%) and remote sensing (24%), with a focus on pollutants such as dust (23.1%), NO_x/NO₂/NO (17.2%), PM_2.5 (17.6%), and PM₁₀ (12%). Industrial emissions (27%) and natural dust (24%) were identified as significant emission sources. Monitoring methods included grab sampling (19%), integrated sampling (34%), and continuous monitoring (47%). Notably, 13.3% of AQ sensors were linked to a station, 27.6% were self-referenced, and 59.1% did not specify calibration methods. The findings highlight the need for further research, regular calibration of air quality monitors, and the integration of advanced modeling approaches. Moreover, we recommend exploring the links between air pollution and urban development to ensure cleaner air and contribute to the global dialogue on sustainable and cross-border AQ solutions. Full article

The geometric symmetry of the pipeline constitutes a critical determinant in regulating the energy propagation dynamics during the explosion process. In the present study, a transparent plexiglass pipe experimental system incorporating a range of angles (30° to 150°) was meticulously constructed. Leveraging high-frequency pressure sensors in conjunction with high-speed camera technology, this investigation examines the influence of the pipe angle, which disrupts geometric symmetry, on the coupling explosion of gas and coal dust. The experimental findings illustrate that an increase in the pipeline turning angle significantly enhances the velocity of the explosion flame front (with the maximum velocity escalating from 97.92 m/s to 361.28 m/s) and concurrently reduces the total propagation time (from 71 ms to 56.5 ms). Moreover, there is a notable reduction in the duration of the explosion flame, decreasing from 240.5 ms to 64.17 ms at the coal dust deposition point. The peak overpressure of the shock wave exhibits a significant increase with the augmentation of the turning angle (rising from 7.07 kPa at 30° to 88.40 kPa at 150°). Furthermore, the overpressure in the fore section of the turning is amplified, attributable to the superimposition of reflected waves and turbulent effects. This study elucidates critical mechanisms including turbulence-enhanced combustion, secondary dust generation from coal dust, and energy dissipation resulting from abrupt alterations in pipeline geometry, thereby offering a theoretical framework for the prevention and effective emergency management of coal mine explosion disasters. Full article

The assessment and optimization of the MIPA (Morphological Image Processing Approach) algorithm for the retrieval of Atmospheric Boundary Layer Height (ABLH) from Aerosol High-power Lidars (AHL) data are presented. MIPA has been developed at CNR-IMAA in the framework of ACTRIS, and it was tested on several lidar datasets, showing, in general, a good agreement with the traditional ABLH retrieval techniques. The main innovative feature of MIPA with respect to other approaches consists in applying optimized morphological filters and object-oriented analysis on lidar timeseries to obtain ABLH estimates. In this study, we carried out a robust MIPA validation effort based on a dedicated measurement campaign organized at CIAO (CNR-IMAA Atmospheric Observatory) in Spring 2024, where several lidar systems were operating continuously along with a quite complete set of other atmospheric sensors and two radiosounding systems. During the campaign, several case studies were considered for MIPA validation, each characterized by an intensive radiosonde schedule to ensure the establishment of a representative ABLH reference dataset. The ABLH retrieved by MIPA was compared against the corresponding ones obtained by radiosonde data. We observed a good overall agreement under different atmospheric conditions, ranging from intense dust events penetrating the ABL to cleaner atmospheric conditions. The best agreement between MIPA and reference dataset is obtained for longer wavelengths (532 nm and 1064 nm) and during daytime conditions. Full article

Light detection and ranging (LiDAR) provides three-dimensional environmental information that is critical for maintaining the safety and reliability of autonomous driving systems. However, dust accumulation on the LiDAR window can cause detection errors and degrade performance. This study determined the optimal spray conditions for accumulating dust to evaluate LiDAR sensor cleaning performance. A primary optimization experiment using spray pressure, spray speed, spray distance, and the number of sprays as variables showed that spray pressure and number of sprays had the most significant influence on the kinetic energy and distribution of dust particles. Notably, the interaction between spray distance and number of sprays—related to curvature effects—was identified as a key variable increasing process sensitivity. A supplementary experiment, which added spray angle as a variable, indicated that while spray pressure remained the most significant factor, spray angle and number of sprays had an indirect influence through interaction terms. Both experiments used the same response variable (point cloud data) interactions to stepwise analyze particle transfer and spatial diffusion. The resulting optimal conditions offer a standard basis for evaluating LiDAR cleaning performance and may help improve cleaning efficiency and maintenance strategies. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. Full article

This work presents a hybrid optical fiber detector by combining the sensing mechanism of the fiber Bragg grating (FBG) and the light extinction method to enable the simultaneous measurement of dust concentration and temperature. Compared with the existing dust concentration sensors, the proposed detector offers three key advantages: intrinsic safety, dual-parameter measurement capability, and potentially network-based monitoring. The critical sensing components of the proposed detector consist of two optical collimators and an FBG. Using the extinction effect of light between the two collimators, the dust concentration and temperature are simultaneously determined by monitoring the intensity and the wavelength of the FBG reflectance spectrum, respectively. The measurement feasibility has been evaluated demonstrating that the two parameters of interest can be effectively sensed with minimally coupled outputs of ±3 pm and ±0.1 mW, respectively. Calibration experiments demonstrate that the change in the intensity of light from the FBG is exponentially related to the dust concentration variation with fitting coefficients equal to 0.948, 0.946, and 0.945 for 200 meshes, 300 meshes, and 400 meshes, respectively. The detector’s relative measurement errors were validated against the weighing method, confirming low measurement deviations. Full article

This article presents research and a discussion on the proper use of filtration in optical measurements. Measurements were taken using a Werth multisensory machine using a Werth Zoom optical sensor. During optical measurements, the filtration option can be used. The manufacturer defines filters as “Dust”. They allow the machine operator to define the appropriate size depending on the type of inclusions or artifacts created in the production process. They can occur in processes such as punching on presses or production in the injection molding process of plastics. The presented research results and statistical analyses confirm the assumptions regarding the validity of using filters and their values. The use of filters with a higher value significantly affects the obtained results and forces the machine user to make a reasonable choice. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article

Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM₁₀ resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM₁₀ content, achieving a high adjusted R² of 0.765. The surface wavelength and sL–PM₁₀ interaction were statistically significant (p < 0.01). The PM₁₀ concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (<4 mm) resulted in 30–50% higher PM₁₀ emissions compared to a longer wavelength (>30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design. Full article

The performance of photovoltaic systems is highly dependent on environmental conditions, with soiling due to dust accumulation often being referred to as a predominant energy degradation factor, especially in dry and semi-arid environments. This paper introduces an AI-based robotic cleaning system that can independently forecast and schedule cleaning sessions from real-time sensor and environmental data. Methods: The system integrates sources of data like embedded sensors, weather stations, and DustIQ data to create an integrated dataset for predictive modeling. Machine learning models were employed to forecast soiling loss based on significant atmospheric parameters such as relative humidity, air pressure, ambient temperature, and wind speed. Dimensionality reduction through the principal component analysis and correlation-based feature selection enhanced the model performance as well as the interpretability. A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. Results: Performance, based on accuracy, precision, recall, and F1-score, demonstrated that logistic regression and SVM provided optimal classification performance with accuracy levels over 92%, and F1-scores over 0.90, demonstrating outstanding balance between recall and precision. The KNN and decision tree models, while slightly poorer in terms of accuracy (around 85–88%), had computational efficiency benefits, making them suitable for utilization in resource-constrained applications. Conclusions: The proposed system employs a dry-cleaning mechanism that requires no water, making it highly suitable for arid regions. It reduces unnecessary cleaning operations by approximately 30%, leading to decreased mechanical wear and lower maintenance costs. Additionally, by minimizing delays in necessary cleaning, the system can improve annual energy yield by 3–5% under high-soiling conditions. Overall, the intelligent cleaning schedule minimizes manual intervention, enhances sustainability, reduces operating costs, and improves system performance in challenging environments. Full article

This study introduces a digital twin paradigm that uses both stationary and mobile sensors and cutting-edge machine learning for urban air quality monitoring. By boosting $R^2$ values from 0.29 to 0.87–0.95, our two-step calibration method increased the accuracy of low-cost PM sensors, showing the possibility of growing monitoring networks without sacrificing measurement accuracy. Significant temporal and spatial variability in PM concentrations was found by mobile sensor deployments, with variations of up to 300% over short distances, predominantly during heavy traffic. During rush hours, peak concentrations were found on multi-lane boulevards and intersections, indicating important exposure concerns usually overlooked by stationary monitoring networks. According to our Graph Neural Network model, which successfully described pollutant dispersion patterns, road dust resuspension predominates in residential areas, while vehicle emissions account for 65% of PM_2.5 along high-traffic corridors. Urban green areas lower PM levels by 30%, yet when the current low-emission zones were first implemented, they had no discernible effect on air quality. Municipal authorities can use this digital twin strategy to acquire practical insights for focused air quality improvements. The method helps make evidence-based traffic management and urban planning judgments by identifying unidentified pollution hotspots and source contributions. The technique offers a scalable option for establishing healthier urban development and marks a substantial leap in environmental monitoring. Full article

Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks. Full article