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Abstract The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

For the difficulty of pinpointing of tunneling machines in the complex environment of coal mine tunnels,a precise positioning method of roadheader based on inertial navigation and visual fusion is proposed, which uses a positioning scheme of inertial navigation plus "vision+laser target". With the designed four feature point large-sized laser target fixed on the roof of the tunnel and the camera on the tunneling machine to collect laser target images, the scheme uses the circular fitting method to locate the center of the light spot and the EPnP algorithm based on four feature points to calculate the position of the tunneling machine. A “vision+laser target” position detection experiment was conducted in simulated condition of heading face to verify the effectiveness of the method in detecting the position of tunneling machines. The results are as follows: within 30 meters tunnel, the maximum error in the width, position, and height are no more than 28.549, 78.868, and 44.459 mm, which shows accurate detection of the position of the tunneling machine. For the inaccurate pose detection of tunneling machines in inertial navigation measurement, induced by the accumulation of pose errors over time and the machine vibration on the combined positioning system, an improved Sage-Husa adaptive filtering method for inertial navigation and visual information fusion is proposed, which could correct measurement errors by detecting new variance values to improve positioning accuracy. A combined positioning experiment of inertial navigation plus “vision+laser target” was conducted in a simulated heading face environment, using improved Sage-Husa adaptive filtering algorithm to fuse inertial navigation and visual information for comparative analysis, which show that the improved Sage-Husa adaptive filtering algorithm fusion results in smaller positioning errors. The maximum errors of pitch angle, roll angle, and heading angle are 0.029°, 0.051°, and 0.0113°, respectively. Within 30 meters, the position error of tunnel width is within the range of 0.033 m, and the position error of tunnel excavation direction is within the range of 0.062 m. Overall, the proposed inertial navigation plus visual fusion positioning method can meet the accuracy requirements of tunnel excavation positioning.

Coal mining machine is the core equipment of completely automated working face, and the research and development of intelligent coal mining robot is crucial for achieving the intellectualization of fully mechanized working face. This paper comprehensively analyzes the current research status of sensing detection, position and attitude control, speed control, cutting trajectory planning, and tracking control in the coal mining machine roboticization process, and proposes five key technologies that must be solved in the development of intelligent shearer robots, including sensing and detection, pose control, velocity control, cutting trajectory planning and tracking control. Aiming at the problem of intelligent perception, this paper proposes the construction thought of a coal mining robot intelligent perception system, as well as the architecture of a coal mining robot intelligent per-ception system. The architecture of the intelligent perception system for coal mining robots is outlined, enabling a comprehensive sensing of running state, posture, environment, and so on, thereby ensuring the safe and reliable operation of intelligent coal mining robots. In terms of the position and attitude control problem of intelligent coal mining robots, the intelligent PID position and attitude control thought is proposed, along with an improved genetic algorithm-based PID pose control method, enabling precise pose control for the coal mining robot. As to the problem of velocity control, the thought of cutting load measurement based on the fusion of “force-electricity” heterogeneous data is proposed. Additionally, a neural network-based algorithm for cutting load measurement is presented, achieving an accurate load measurement. Furthermore, a traction and cutting speed adaptive control approach is proposed, including an artificial intelligence-based decision-making method for traction and cutting speed and a sliding mode control method for traction and cutting speed with disturbance rejection. This approach enables a precise and adaptive speed control for the coal mining robot. Regarding the problem of cutting trajectory planning and tracking control, the precise cutting trajectory planning thought is proposed, incorporating geological data and historical cutting data into a cutting trajectory planning model. The precise cutting trajectory tracking control thought is proposed, and an intelligent interpolation algorithm-based cutting trajectory tracking control method is given, achieving a high-precision trajectory planning and accurate tracking control for the coal mining robot. Considering the “position-attitude-velocity” collaborative control problem, the intelligent optimization idea of "position-attitude-velocity" collaborative control parameters is proposed, which utilizes an improved particle swarm optimization method based on multi-system constraints to optimize the coordinated control parameters, resulting in intelligent and efficient operation of the coal mining robot. The in-depth investigation of these five key technologies for intelligent coal mining robot provides some valuable insights for accelerating the development of high-performance, efficient, and reliable intelligent coal mining robot.

For the problem of interference between shearer drum and hydraulic support guard plate, an interference state intelligent recognition method for shearer drum and hydraulic support guard plate of improved YOLOv5s algorithm is proposed. The use of boundary constraint and non-linear contextual regularization based on the group's previous proposed method of defogging and dust removal to clarify the video image, improve the quality of the monitoring video image of the comprehensive mining face. The YOLOv5s model is improved by replacing the ordinary convolutional Conv in the YOLOv5s backbone network with Ghost convolution, the improved algorithm greatly reduces the number of model parameters and improves the model recognition speed. At the same time, the coordinate attention mechanism is introduced to improve the model's ability to extract the features from the guard plate and shearer, and improve the recognition accuracy. The soft non-maximum suppression algorithm (Soft-NMS) anchor frame screening method is used to reduce the problem of missed detection due to overlapping guard plates. For the problem of determining the interference state of shearer drum and hydraulic support guard plate, the method for determining anchor box overlap degree between hydraulic support guard plate and shearer drum. The improved YOLOv5s algorithm is compared with YOLOv5s and YOLOv3-tiny algorithm. The results indicate that compared with the original YOLOv5s model, the recognition accuracy of this method has been improved by about 8.1%, and GFLOPs have been reduced by 1.86 times. mAP@.5 was increased to 97.2%, and the average recognition speed is 169 frames/s. The improved YOLOv5s algorithm is used to validate the interference state recognition effect for video images of shearer drum and hydraulic support guard plate in in actual fully mechanized mining faces, and the results show that the recognition accuracy of interference state between the coal shearer drum and the hydraulic support guard plate is 96%.

At present, China’s coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberate productivity, this paper further researches the trajectory planning of the drilling and anchoring materials of the robotic arm for the drilling machine “grasping–carrying–loading–unloading” on the basis of the drilling and anchoring robotic system designed by the team in the previous stage. Firstly, the kinematic model of the robotic arm with material was established by improving the D-H parameter method. Then, the working space of the robotic arm with the material was analyzed using the Monte Carlo method. The singular bit-shaped region of the robotic arm was restricted, and obstacles were removed from the working space. The inverse kinematics was utilized to solve the feasible domain of the robotic arm with material. Secondly, in order to avoid blind searching, the guidance of the Bi-RRT algorithm was improved by adding the target guidance factor, and the two-way tree connection strategy for determining the feasible domain was combined with the Bi-RRT algorithm’s feasible domain judgment bi-directional tree connection strategy to improve the convergence speed of the Bi-RRT algorithm. Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. Finally, a simulation environment was built in a ROS environment to compare and analyze the planning effect of different algorithms. The simulation results showed that the improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field improved the optimization speed by 69.8% and shortened the trajectory length by 46.6% compared with the traditional RRT algorithm.

In response to the challenges of multiple personnel, heavy support tasks, and high labor intensity in coal mine tunnel drilling and anchoring operations, this study proposes a novel tracked drilling and anchoring robot. The robot is required to maintain alignment with the centerline of the tunnel during operation. However, owing to the effects of skidding and slipping between the track mechanism and the floor, the precise control of a drilling and anchoring robot in tunnel environments is difficult to achieve. Through an analysis of the body and track mechanisms of the drilling and anchoring robot, a kinematic model reflecting the pose, steering radius, steering curvature, and angular velocity of the drive wheel of the drilling and anchoring robot was established. This facilitated the determination of speed control requirements for the track mechanism under varying driving conditions. Mathematical models were developed to describe the relationships between a tracked drilling and anchoring robot and several key factors in tunnel environments, including the minimum steering space required by the robot, the minimum relative steering radius, the steering angle, and the lateral distance to the sidewalls. Based on these models, deviation-correction control strategies were formulated for the robot, and deviation-correction path planning was completed. In addition, a PID motion controller was developed for the robot, and trajectory-tracking control simulation experiments were conducted. The experimental results indicate that the tracked drilling and anchoring robot achieves precise control of trajectory tracking, with a tracking error of less than 0.004 m in the x-direction from the tunnel centerline and less than 0.001 m in the y-direction. Considering the influence of skidding, the deviation correction control performance test experiments of the tracked drilling and anchoring robot at dy = 0.5 m away from the tunnel centerline were completed. In the experiments, the tracked drilling and anchoring robot exhibited a significant difference in speed between the two sides of the tracks with a track skid rate of 0.22. Although the real-time tracking maximum error in the y-direction from the tunnel centerline was 0.13 m, the final error was 0.003 m, meeting the requirements for position deviation control of the drilling and anchoring robot in tunnel environments. These research findings provide a theoretical basis and technical support for the intelligent control of tracked mobile devices in coal mine tunnels, with significant theoretical and engineering implications.

Owing to the existing problems of the temporary shield support device in a coal mine, such as considerable component weight, difficult disassembly, and large chamber installation, the design of the temporary rectangular shield support device was optimized. A module division method combining the functional analysis method and a similar feature clustering method was proposed, which completes the division of the functional module and establishes the structural module. A structural optimization method of “variable density topology optimization, parameter sensitivity analysis and Optimal Space-Filling Design (OSF)” was proposed. A variable-density topology optimization model of the device was established using the variable-density topology optimization method. The key variables of the device were determined using a parameter sensitivity analysis. The sample points of the critical variables were generated using the optimal space-filling design method, the approximate response surface models of mass, displacement, and stress were constructed, and the optimal solution of the device’s weight was obtained under the constraint conditions of pressure and displacement. A lightweight matching method of modularization based on transportability, disassembly, maintainability, and reliability was proposed, and the optimal matching scheme for the volume and mass of the device was determined. As a result, the weight of the temporary shield support device was reduced by 11.2%, and the maximum stress was reduced by 13.4%, under the premise of ensuring reliability. It realizes the convenience of transportation and high efficiency of assembly and disassembly in different design requirements, which is of great significance to accelerate the intelligent construction of coal mine.

A drill-anchor robot is an essential means of efficient drilling and anchoring in coal-mine roadways. It is significant to calculate the position of the drill-anchor robot based on the positioning information of the supported anchor rod to improve tunneling efficiency. Therefore, identifying and positioning the supported anchor rod has become a critical problem that needs to be solved urgently. Aiming at the problem that the target in the image is blurred and cannot be accurately identified due to the low and uneven illumination environment, we proposed an improved YOLOv7 (the seventh version of the You Only Look Once) model based on the fusion of image enhancement and multiattention mechanism, and the self-made dataset is used for testing and training. Aiming at the problem that the traditional positioning method cannot guarantee accuracy and efficiency simultaneously, an anchor-rod positioning method using depth image and RGB image alignment combined with least squares linear fitting is proposed, and the positioning accuracy is improved by processing the depth map. The results show that the improved model improves the mAP by 5.7% compared with YOLOv7 and can accurately identify the target. Through the positioning method proposed in this paper, the error between the positioning coordinate and the measurement coordinate of the target point on each axis does not exceed 11 mm, which has high positioning accuracy and improves the positioning accuracy and robustness of the anchor rod in the coal-mine roadway.

Tracked inspection robots have demonstrated their versatility in a wide range of applications. However, challenges arising from issues such as skidding and slipping have posed obstacles to achieving precise and efficient trajectory control. This paper introduces a method to determine the steering parameters of robot based on the surrounding obstacles and road information. The primary objective is to enhance the steering efficiency of tracked robots. The corresponding relationship between the track speed, driving force and track steering radius of the tracked robot is obtained. Considering the influence of track skid and slip, relationship models about the steering radius and traveling speed of the robot are established. The minimum and maximum steering radii in the obstacle avoidance process are analyzed, and a mathematical model of the relationship between the steering angle of the robot and the distance between the side obstacles is established. The trajectory deviation model of the tracked robot is established, and a principle analysis of the LiDAR ranging is completed. This lays the foundation for a steering measurement and control system for tracked robots. ADAMS(2020) software is used to establish the multi-body dynamics model of the tracked robot, and three different obstacle-avoiding steering control strategies are designed for the robot in a simulated environment with space obstacles. The simulation experiment demonstrates that the robot achieves more efficient obstacle avoidance steering through the use of differential steering, leading to a decrease in both track skid and slip rates. Through the simulation experiment, it can be seen that the robot uses differential steering to complete the obstacle avoidance steering movement more efficiently, and the track skid and slip rates are smaller. The simulation results are used to complete the steering control experiment of the tracked robot on different road surfaces. The results show that by adjusting the track driving parameters, the robot can effectively complete the obstacle-avoiding steering movement by using the differential steering control strategy, which verifies the accuracy of the steering control strategy.

In a fully mechanized mining face, there is interference between the hydraulic support face guard and the shearer drum. The two collisions seriously affect coal mine production and personnel safety. The identification of a shearer drum can be affected by fog generated when the shearer drum cuts forward. It is hydraulic support face guard recovery, not the timely block shearer drum, that will also affect the recognition of the shearer drum. Aiming at the above problems, a shearer drum identification method based on improved YOLOv5s with dark channel-guided filtering defogging is proposed. Aiming at the problem of fog interference affecting recognition, the defogging method for dark channel guided filtering is proposed. The optimal value of the scene transmittance function is calculated using guided filtering to achieve a reasonable defogging effect. The Coordinate Attention (CA) mechanism is adopted to improve the backbone network of the YOLOv5s algorithm. The shearer drum features extracted by the C3 module are reallocated by the attention mechanism to the weights of each space and channel. The information propagation of a shearer drum’s features is enhanced by such improvements. Thus, the detection of shearer drum targets in complex backgrounds is improved. S Intersection over Union (SIoU) is used as a loss function to improve the speed and accuracy of the shearer drum. To verify the effectiveness of the improved algorithm, multiple and improved target detection algorithms are compared. The algorithm is deployed at Huangling II mine. The experimental results present that the improved algorithm is superior to most target detection algorithms. In the absence of object obstruction, the improved algorithm achieved 89.3% recognition accuracy and a detection speed of 48.8 frame/s for the shearer drum in the Huangling II mine. The improved YOLOv5s algorithm provides a basis for identifying interference states between the hydraulic support face guard and shearer drum.

The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment.

Tracked inspection robots possess prominent advantages in dealing with severe environment rescue, safety inspection, and other important tasks, and have been used widely. However, tracked robots are affected by skidding and slipping, so it is difficult to achieve accurate control. For example, the control parameters of a tracked robot are the same during driving, but the pressure, shear force and steering resistance of the robot on the road surface are different, which affects the steering characteristics of the robot on complex terrain. Based on analysis of the structural parameters and steering radius of the robot, the traction force and resistance torque models of the tracked robot were established, and the plane dynamics of the robot’s steering were analyzed and solved. The corresponding relationships between the road parameters, relative steering radius, and lateral relative offset of the robot on three typical roads were obtained. Mathematical models of the robot’s track speed and relative steering radius with and without skid and slip were established. Through simulation analysis of Matlab software, the corresponding relationship between the relative steering radius of the robot and the velocity difference of the two tracks were obtained. Taking angular obstacles as an example, three obstacle-avoidance steering control strategies, once turning in situ center, twice turning in situ center, and large-radius steering were developed. The tracked robot and obstacle multi-body dynamic simulation models were constructed using ADAMS simulation software. The simulation results show that all three methods can complete the steering tasks according to the requirements; however, under the influence of skid and slip, the trajectory of the robot deviates from the ideal trajectory, which has a great impact on large-radius steering, even though the large-radius obstacle-avoidance steering control strategy has the advantages of a smooth trajectory, fast steering speed, and high efficiency. The obstacle-avoidance steering experiments were completed by the robot prototype, which verifies the rationality of robot steering theory, which could provide the corresponding theoretical basis for autonomous obstacle-avoidance navigation control of a tracked robot.

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining.