In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

The tunneling work belongs to the group operation of semi-closed space, and the work is difficult with a high risk coefficient. It is an urgent requirement of coal mining to achieve unmanned and intelligent tunneling work. The path rectification planning of roadheaders is a necessary step before roadway cutting. In the traditional dynamic modeling analysis of roadhead tracks, problems such as compaction resistance, bulldozing resistance, steering resistance, tunnel dip angle, ditching, and obstacle-crossing capacity are not considered. In order to approximate the kinematic and dynamic parameters of a roadheader’s deviation correction under actual working conditions, this paper establishes kinematic and dynamic models of a roadheader’s path rectification at low speeds and under complex working conditions, and calculates the obstacle-crossing ability of roadheaders in the course of path rectification by modes based on roadway conditions, crawler resistance, and driving performance of the roadheader. Field experiments were carried out to verify the effectiveness of the dynamic model. The dynamic roadheader model was used in combination with actual working conditions of roadways in order to establish a roadway grid model. The grid model was simplified using rectifying influence degree and distance cost. The roadheader dynamic model and grid model were then used to propose a path rectification planning and tracking algorithm based on particle swarm optimization of the actual roadway conditions and roadheader driving performance. Finally, the effectiveness and superiority of the algorithm were verified using MATLAB simulation. The algorithm can provide strong technical guarantee for the intelligence of roadheader and unmanned mining. The results presented here can provide theoretical and technical support for the structural optimization and intelligent travel control of roadheaders.

A coal mine roadway is a longitudinally limited space with curves and branches, low illumination and high humidity, a large amount of dust, and an unstructured terrain environment. Traditional ICP algorithms have the defects of slow convergence speed and it is easy to fall into local optimums. While the NDT algorithm in the NDT + ICP algorithm has high registration efficiency, poor stability, and low registration accuracy, which are not suitable for point clouds with noise and a large amount of data. By calculating the FPFH value, the detailed description of the point cloud will be greatly increased to increase the robustness and accuracy Therefore, a feature registration method based on the FPFH + ICP algorithm is proposed to reduce the modeling error of excavation roadways and meet the requirements of intelligent excavation. First, outliers caused by dust are treated by the Euclidean clustering point cloud segmentation method, and then the calculation of the normal vector in the FPFH feature descriptor is optimized based on extracting key points from the roadway structure. The surface normal vector of each key point and its neighborhood point is estimated according to the measured point and its neighborhood point. The initial coordinate transformation matrix of a point cloud of an excavated roadway is obtained by the SAC-IA algorithm and transferred to the ICP algorithm. Finally, KD-tree is introduced into the ICP algorithm to accelerate the search speed of corresponding point pairs, and the Gauss–Newton method is used to optimize the solution of the nonlinear objective function of the algorithm to complete accurate registration of point clouds in an excavation roadway.

This paper proposes a temporary support system for improving the efficiency and safety of underground roadway excavation in coal mines. Firstly, this study establishes a calculation model for the initial supporting force of the excavation of roadway temporary support and a gray system-based automatic prediction model for the initial supporting force level, based on the mechanism of temporary support controlling the roof. These models enable the prediction of the required initial supporting force at different locations along the roadway’s temporary support area, thereby providing a basis for controlling the initial supporting force of the temporary support system. To achieve efficient and adaptive control of the initial supporting force of temporary supports at different locations, this study designs a support force controller based on Simulated Annealing Particle Swarm Optimization Proportional-Integral-Derivative (SAPSO-PID). This study establishes a mathematical model for the hydraulic cylinder pressure system controlled by the temporary support overflow valve and conducts a stability analysis and model verification. The study constructs a simulation control system for the initial supporting force based on SAPSO-PID using the combined simulation platform of AMESim and Matlab. The simulation results demonstrate that the proposed support force control system efficiently achieves adaptive control of the initial supporting force of temporary supports. An experimental system in the underground roadway of a coal mine is constructed to validate the results of the simulation analysis.

In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation. This paper proposes an integrated odometry-based method to improve position estimation and mitigate location ambiguities for simultaneous localization and mapping (SLAM) in large-scale, GNSS-denied, and perceptually degraded subterranean transport roadway scenarios. Additionally, this paper analyzes the robot dynamic model and presents a nonlinear control strategy for the robot to autonomously track a planned trajectory based on the path-following error dynamic model. Finally, the proposed algorithm and control strategy are tested and validated both in a virtual transport roadway environment and in an active underground coal mine. The test results indicate that the proposed algorithm can obtain more accurate and robust robot odometry and better large-scale underground roadway mapping results compared with other SLAM solutions.