Aiming at the environment of low illumination, high dust, and heavy water fog in coal mine driving face and the problems of occlusion, coincidence, and irregularity of bolt mesh laid on coal wall, a YOLOv7 bolt mesh-detection algorithm combining the image enhancement and convolutional block attention module is proposed. First, the image brightness is enhanced by a hyperbolic mapping transform-based image enhancement algorithm, and the image is defogged by a dark channel-based image defogging algorithm. Second, by introducing a convolutional block attention model in the YOLOv7 detection network, the significance of bolt mesh targets in the image is improved, and its feature expression ability in the detection network is enhanced. Meanwhile, the original activation function ReLU in the convolutional layer Conv of the YOLOv7 network is replaced by LeakyReLU so that the activation function has stronger nonlinear expression capability, which enhances the feature extraction performance of the network and thus improves the detection accuracy. Finally, the training and testing samples were prepared using the actual video of the drilling and bolting operation, and the proposed algorithm is compared with five classical target detection algorithms. The experimental results show that the proposed algorithm can be better applied to the low illumination, high dust environment, and irregular shape on the detection accuracy of coal mine roadway bolt mesh, and the average detection accuracy of the image can reach 95.4% with an average detection time of 0.0392 s.

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.

Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided image filtering and cross-transmission, termed MGFCTFuse. First, an image decomposition method based on mutually guided image filtering is designed, one which decomposes the original image into a base layer and a detail layer. Second, in order to preserve as much background and detail as possible during feature extraction, the base layer is concatenated with the corresponding original image to extract deeper features. Moreover, in order to enhance the texture details in the fusion results, the information in the visible and infrared detail layers is fused, and an enhancement module is constructed to enhance the texture detail contrast. Finally, in order to enhance the communication between different features, a decoding network based on cross-transmission is designed within feature reconstruction, which further improves the quality of image fusion. In order to verify the advantages of the proposed algorithm, experiments are conducted on the TNO, MSRS, and RoadScene image fusion datasets, and the results demonstrate that the algorithm outperforms nine comparative algorithms in both subjective and objective aspects.

Mobile robots conducting inspection tasks in coal-mine roadways and operating in complex underground environments are often subjected to demanding conditions such as low adhesion, uneven friction distribution, and localized slippery surfaces. These challenges are significant, predisposing the robots to trajectory deviation and posture instability, thereby presenting substantial obstacles to high-precision tracking control. The primary innovation of this study lies in proposing a hierarchical model predictive control (HMPC) strategy, which addresses the challenges through synergistic, kinematic and dynamic optimization. The core contribution is the construction of dual-layer optimization architecture. The upper-layer kinematic MPC generates the desired linear and angular velocities as reference commands. The lower-layer MPC is designed based on a dynamic model that incorporates ground adhesion characteristics, enabling the online computation of optimal driving forces (F_L, F_R) for the left and right tracks that simultaneously satisfy tracking performance requirements and practical actuation constraints. Simulation results demonstrate that the proposed hierarchical framework significantly outperforms conventional kinematic MPC in terms of steady-state accuracy, response speed, and trajectory smoothness. Experimental validation further confirms that, in environments with low adhesion and localized slippery conditions representative of actual roadways, the proposed method effectively coordinates geometric accuracy with dynamic feasibility. It not only markedly reduces longitudinal and lateral tracking errors but also ensures excellent dynamic stability and reasonable driving force distribution, providing key technical support for reliable operation in complex underground environments.

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation.