Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described.

Intelligent traffic systems for railway object detection have become the focus of research in recent years. Accurate and fast object detection using a camera is an important but challenging problem in the railway industry. In this study, we propose an object detector with low power yet robust detection for collision warning in a train safety system. The proposed object detector comprises three modules. First, a stable sampling module is used to reduce the dimensions of the feature map and image information loss. Second, a lightweight feature extraction module utilizes a dynamic bottleneck structure to control the calculation load and enhance the expressive ability of the model. Third, a feature-fusion module combines high- and low-level features to enhance the semantic information and improve the accuracy of detection of multiscale and small objects. Experimental results demonstrate that the proposed network achieves reasonable results for railway object detection and outperforms the current state-of-the-art detectors. Finally, we design an obstacle-avoidance device that can be installed at the front of the train for real-time security warning in real-world conditions.

In order to effectively reduce the coal dust concentration in a fully mechanized mining face, this research used laboratory experiment, numerical simulation, and field test to conduct an in-depth exploration of the ejector precipitator installed at the low-level caving coal hydraulic support. Firstly, through the experimental platform in the laboratory, the dust removal effect of the nozzle with different structural parameters was tested, and the 3D particle dynamic analyzer was adopted to verify its atomization characteristics; then, the structural parameters corresponding to the nozzle in the best test results were obtained. Secondly, by using Fluent, the negative pressure flow field in the ejector barrel was numerically simulated. The results indicated that when the pressure of supply water was 12 MPa, the negative pressure value formed in the flow field was the lowest and the inspiratory velocity was the largest, which was conducive to dust removal. Finally, the tests of liquid–gas ratio and dust removal ratio were carried out in a fully mechanized mining face. The results showed that when the nozzle specification recommended by the experiment and the pressure of supply water recommended by the numerical simulation were used, the removal ratios of the total coal dust and the respirable coal dust were 89.5% and 91.0%, respectively, at the measuring point of the highest coal dust concentration. It indicates that the ejector precipitator has a good application effect in reducing the coal dust concentration in a fully mechanized mining face and improving the work environment of coal mine workers.

The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.

Spur gears are widely used transmission components. In the traditional design process, the noninvolute part of the tooth profile curve is difficult to describe with mathematical equations. This article puts forward a new parametric modeling method, which can describe the modified involute part of spur gears and parameterize and optimize the transition part of the involute curve of the spur gear. And this model of the spur gear can be created by parameters which is input in Scilab software and the spur gear graphic can be completed correspondingly. The experiments show that this modeling method can more quickly produce the standard spur or modified spur gear, and it also improves the efficiency and accuracy of spur gear modeling.

The prop is the most important part to ensure the proper functioning of hydraulic support. When the hydraulic support is impacted by the roof, the prop is prone to extrusion deformation, expansion, and even bursting. In order to study the stress on a doubly-telescopic hydraulic support prop under impact load, an impact simulation was carried out based on the drop weight method. First, the impact model of the hydraulic system of the prop was established in AMESim software and the dynamic response curves of the bottom and middle cylinder of the prop obtained. Then, according to the conservation of energy, the pressure formulaes in the hydraulic cylinder of the prop under impact load were derived and verified by the AMESim simulation results, and the maximum pressure on the inner surface of the hydraulic cylinder of the prop obtained. Lastly, the transient dynamic simulation of a hydraulic support prop was carried out in ANSYS Workbench software, and cloud diagrams of stress and deformation of the prop obtained using Workbench simulation. The AMESim simulation process describes the change in fluid pressure in the prop, and the pressure formulae can be used to estimate the internal pressure of the prop under impact load. The finite element analysis results show that the stress of the middle hydraulic cylinder is much greater than that on the bottom hydraulic cylinder under impact load, which can provide a reference for prop design.