Search Results (23)

Accurate dynamic flow statistics for trackless vehicles are critical for efficiently scheduling trackless transportation systems in underground mining. However, traditional discrete time-point methods suffer from “time membership discontinuity” due to RFID timestamp sparsity. This study proposes a fuzzy five-region membership (FZFM) model to address this issue by subdividing time intervals into five characteristic regions and constructing a composite Gaussian–quadratic membership function. The model dynamically assigns weights to adjacent segments based on temporal distances, ensuring smooth transitions between time intervals while preserving flow conservation. When validated on a 29-day RFID dataset from a large coal mine, FZFM eliminated conservation bias, reduced the boundary mutation index by 11.1% compared with traditional absolute segmentation, and maintained high computational efficiency, proving suitable for real-time systems. The method effectively mitigates abrupt flow jumps at segment boundaries, providing continuous and robust flow distributions for intelligent scheduling algorithms in complex underground logistics systems. Full article

Unmanned underground articulated dump trucks (UADTs) are an important direction for the coal mining industry to vigorously promote automation and intelligence. Among these, tracking and controlling the motion trajectory is the key weak link. This paper presents a kinematic analysis of the stationary turning process of UADTs. Then, a posture state model for articulated trucks is established. The objective is to optimize the control method and further improve trajectory tracking accuracy. Based on the advantages and disadvantages of the feedback linearization control (FLC) method, a sliding mode control method based on the Ackermann formula (ASMC) and integral type switch gain (ISMC) are proposed. Finally, hardware-in-the-loop simulation verifies the superiority and tracking quality of the controller. The results show that the ASMC controller can control the lateral position deviation, course angle deviation, and curvature deviation around 10 cm, 0.04 rad, and 0.08 m⁻¹ in the hardware-in-the-loop simulation environment. The ISMC controller can control the lateral position deviation, course angle deviation, and curvature deviation near 8 cm, 0.01 rad, and 0.02 m⁻¹, and can also effectively control the jitter problem. Each deviation is stabilized within 10 s. This provides a reference for the development of trajectory tracking strategies for articulated vehicles. Full article

Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser sensor technology. The system utilizes a camera and the YOLOv5 algorithm for real-time identification and precise tracking of mining vehicles, while the laser sensor is used to accurately measure the straight-line distance between the vehicle and the positioning device. By combining the strengths of both vision and laser sensors, the system can efficiently identify mining vehicles in complex environments and accurately calculate their position using geometric principles based on laser distance measurements. Experimental results show that the YOLOv5 algorithm can efficiently identify and track mining vehicles in real time. When integrated with the laser sensor’s distance measurement, the system achieves high-precision positioning, with horizontal and vertical positioning errors of 1.66 cm and 1.96 cm, respectively, achieving centimeter-level accuracy overall. This system significantly improves the accuracy and real-time performance of mining vehicle positioning, effectively reducing operational errors and safety risks, providing essential technical support for the intelligent development of underground mining transportation systems. Full article

To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm. Full article

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article

In response to the widespread absence of global navigation satellite system (GNSS) signals in underground parking scenes, we propose a multimodal localization method that integrates enhanced berth mapping with Clothoid trajectory prediction, enabling high-precision localization for intelligent vehicles in underground parking environments. This method began by constructing a lightweight map based on the key berths. The map consisted of a series of discrete nodes, each encompassing three elements: holistic and local scene features extracted from an around-view image, and the global pose of the mapping vehicle calculated using the positions of the key berth’s corner points. An adaptive localization strategy was employed during the localization phase based on the trajectory prediction result. A progressive localization strategy, relying on multi-scale feature matching, was applied to the nodes within the map coverage range. Additionally, a compensation localization strategy that combined odometry with the prior pose was utilized for the nodes outside the map coverage range. The experiments conducted in two typical underground parking scenes demonstrated that the proposed method achieved a trajectory prediction accuracy of 40 cm, a nearest map search accuracy exceeding 92%, and a metric localization accuracy meeting the 30 cm standard. These results indicate that the proposed approach satisfies the high-precision, robust, real-time localization requirements for intelligent vehicles in underground parking scenes, while effectively reducing the map memory requirements. Full article

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy–PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy–PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control. Full article

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of ${-2.5 \mathrm{m}/\mathrm{s}}^2$ within a deviation of $\pm 0.1 \mathrm{m}/{\mathrm{s}}^2$, compared with the deviation of $0.11 \mathrm{m}/{\mathrm{s}}^2$ of fuzzy PID and the deviation of $0.13 \mathrm{m}/{\mathrm{s}}^2$ of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery. Full article

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO₂ emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle’s license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time. Full article

As rail transportation continues to advance, it provides significant convenience to the public. However, the environmental vibration and noise generated during its operation have become major concerns for residents living near rail lines. In response to these concerns, the “Law on the Prevention and Control of Noise Pollution” was promulgated in China, bringing attention to this issue within the rail transportation sector. This review summarizes the regular features observed in environmental vibration and secondary structural noise tests on different sections, including embankment sections, bridge sections, underground railroads and vehicle sections. Furthermore, it introduces several physical models utilized in the study of environmental vibration and secondary structural noise, focusing on three key aspects: excitation sources, propagation paths and the modelling of building structures. This paper also explores the introduction of data-driven models related to big data and artificial intelligence to enhance the accuracy and efficiency of research in this field and provides an overview of commonly used measures to control train-induced environmental vibrations and secondary noise in buildings. These measures are discussed in terms of excitation sources, propagation paths, and receivers, offering insights into effective strategies for mitigating the impact of rail transportation on nearby residents. Finally, this study highlights the primary findings and offers pertinent recommendations. These recommendations include considerations regarding both laboratory and on-site testing procedures, challenges associated with the deployment of data-driven models and key parameters for designing and utilizing low-stiffness fasteners. Full article

Aiming at the intelligent design of underground roadway support and the precise positioning of unmanned full excavation faces, a positioning and measurement method of bolt robots based on the monocular vision principle was proposed. In this paper, a vehicle body positioning model based on image data was established. The data were obtained with a camera, and the conversion between image coordinates and world coordinates was carried out through coordinate system conversion. A monocular vision positioning system of the bolt robot was designed, and the simulation’s experimental model was established. Under the simulation’s experimental conditions, the effective positioning distance of the monocular vision positioning system was measured. An experimental platform for the bolt robot was designed, and real-time human positioning data measurement of the vehicle was carried out. The experimental error was analyzed, and the reliability of the method was proven. This method realizes the real-time positioning of underground mines through the bolt robot, improves the accuracy and efficiency of the positioning, and lays a foundation for the positioning control of the heading face and the unmanned bolt robot. Full article

Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects present a significant challenge for automated drilling under non-ideal surfaces, which constrains the intelligentization of rock excavation. To address this issue, this paper proposes a method for extracting contour surfaces and planning drilling paths based on a vehicle-mounted 3D scanner. This method effectively extracts contour surfaces and optimizes drilling paths, thereby improving work efficiency and safety. Specifically, the proposed method includes: (i) the real-time scanning of cross-sectional contours using a vehicle-mounted 3D scanner to construct an accurate three-dimensional point-cloud model and obtain contour over-digging information; the acquired data are compared with theoretical drilling maps in the vehicle’s coordinate system to re-plan the blasting-hole point set; (ii) the development of a volume-based dynamic search algorithm based on the irregularities of contour surfaces to detect potential collisions between holes; and (iii) the conversion of the drilling sequence planning based on the new blasting hole point set into a traveling salesman problem (TSP), and optimization using a Hybrid Greedy Genetic Algorithm (HGGA) to achieve path traversal of all drilling positions. The effectiveness of the proposed method was verified using rock excavation in a certain mine as an example. The results show that the overall recognition rate of the contour over-digging reached over 80%, the number of arm collisions was significantly reduced, and the distance traveled by the drilling rig was reduced by 35% using the improved genetic algorithm-based rock-drilling rig path planning. Full article

In recent years, the use of massive multiple-input multiple-output (MIMO) systems and higher frequency bands for next-generation urban rail transportation systems has emerged as an intriguing research topic due to its potential to significantly increase network capacity by utilizing available narrowband and broadband spectrums. In metro and mining applications, the high-reliability wireless sensor network (WSN) plays a vital role in providing personal safety, channel optimization, and improving operational performance. Through the duration of 1921–2023, this paper provides the survey on the progress of fifth-generation (5G) and beyond-fifth-generation (B5G) wireless communication systems in underground environments such as tunnels and mines, the evolution of the earliest technologies, development in channel modeling for vehicle-to-vehicle (V2V) communications, and realization of different wireless propagation channels in high-speed train (HST) environments. In addition, the most recent advanced channel modeling methods are examined, including the development of new algorithms and their use in intelligent transportation systems (ITS); mathematical, analytical, and experimental techniques for propagation design; and the significance of the radiation characteristics, antenna placing, and physical environment effect on wireless communications. Leaky coaxial cable (LCX) and distributed antenna system (DAS) designs are introduced in the demonstrated systems for improving the channel capacity of narrowband and wideband channels as well as the spatial characteristics of various MIMO systems. The review article concludes by figuring out open research directions for future technologies. Full article

High-precision maps are widely applied in intelligent-driving vehicles for localization and planning tasks. The vision sensor, especially monocular cameras, has become favoured in mapping approaches due to its high flexibility and low cost. However, monocular visual mapping suffers from great performance degradation in adversarial illumination environments such as on low-light roads or in underground spaces. To address this issue, in this paper, we first introduce an unsupervised learning approach to improve keypoint detection and description on monocular camera images. By emphasizing the consistency between feature points in the learning loss, visual features in dim environment can be better extracted. Second, to suppress the scale drift in monocular visual mapping, a robust loop-closure detection scheme is presented, which integrates both feature-point verification and multi-grained image similarity measurements. With experiments on public benchmarks, our keypoint detection approach is proven robust against varied illumination. With scenario tests including both underground and on-road driving, we demonstrate that our approach is able to reduce the scale drift in reconstructing the scene and achieve a mapping accuracy gain of up to 0.14 m in textureless or low-illumination environments. Full article

In an attempt to solve the problems of the low intelligent distribution degree and high working intensity of auxiliary transportation systems in underground coal mines, an intelligent distribution strategy of materials in the whole mine is put forward. Firstly, combined with the characteristics of materials and standard containers, a three-dimensional loading model is established with the goal of maximizing the space utilization of standard containers, and a three-dimensional space segmentation heuristic algorithm is used to solve the material loading scheme. Then, the multi-objective optimization model of distribution parameters is established with the goal of the shortest delivery distance, the shortest delay time, and the fewest number of delivery vehicles, and the dual-layer genetic algorithm is used to solve the distribution scheme. Finally, the spatiotemporal conversion coefficient is designed to solve the task list by hierarchical clustering, and the solution time is reduced by 30%. The results show that the dual-layer genetic algorithm based on hierarchical clustering has good adaptability in complex material scheduling scenarios. Full article