Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

The drilling robot is the key equipment for pressure relief in rockburst mines, and the accurate recognition of a pressure relief hole is the premise for optimizing the layout of pressure relief holes and intelligent drilling. In view of this, a pressure relief hole recognition method for a drilling robot, based on single-image generative adversarial network (SinGAN) and improved faster region convolution neural network (Faster R-CNN), is proposed. Aiming at the problem of insufficient sample generation diversity and poor performance of the traditional SinGAN model, some improvement measures including image size adjustment, multi-stage training, and dynamically changing iteration times are designed as an improved SinGAN for the generation of pressure relief hole images. In addition, to solve the problem that the traditional depth neural network is not ideal for small-size target recognition, an improved Faster R-CNN based on multi-scale image input and multi-layer feature fusion is designed with the improved SqueezeNet as the framework, and the sample data collected from ground experiments are used for comparative analysis. The results indicate that the improved SinGAN model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can greatly improve the training speed of the model. The accuracy and recall rate of the improved Faster R-CNN model were able to reach 90.09% and 98.32%, respectively, and the average detection time was 0.19 s, which verifies the superiority of the improved Faster R-CNN model. To further verify the practicability of the proposed method, some field images were collected from the underground rockburst relief area in the coal mine, and a corresponding test analysis was carried out. Compared with three YOLO models, the accuracy and recall rate of improved Faster R-CNN model improved significantly, although the training time and recognition time increased to a certain extent, which proves the feasibility and effectiveness of the proposed method.

Considering the complexity and difficulty of obtaining certain parameters in the electro-hydraulic proportional control system, a precise transfer function of the system was derived through parameter identification using experimental data obtained from an Amesim simulation model after establishing a basic mathematical model. This approach reduces the reliance on accurate parameters of individual components. A feedforward-feedback composite controller was designed, and its effectiveness was validated in Simulink using the system’s transfer function. Subsequently, the dead zone range of the proportional valve was determined through experiments, and a dead zone compensation strategy was designed, which reduced the time required for the proportional valve to traverse the dead zone by 89.4%. Based on the dead zone compensation, trajectory tracking experiments were conducted to validate the effectiveness of the feedforward-feedback composite controller. Under fixed disturbances, the trajectory tracking error was reduced by 53.8% compared to feedback control. Under time-varying load disturbances, the trajectory tracking error was reduced by 51.2% compared to feedback control.

The utilization of auxiliary tools employing ultrasonic high-frequency vibration to enhance rock breaking efficiency holds significant potential for application in underground hard rock excavation engineering. To investigate the failure mechanism of rocks under high frequency ultrasonic vibration load, this study employs particle flow software PFC2D for numerical simulation. By incorporating boundary conditions from actual ultrasonic vibration rock breaking experiments and utilizing a parallel bond model to construct the rock, we analyze the deformation, damage, fracture, and energy evolution process of hard rocks subjected to vibrational loads. The results demonstrate that the maximum displacement in hard rocks increases nearly linearly with vibrations until reaching 5.0199 × 10⁻³ m, after which it plateaus. Additionally, macroscopic fissures formed during rock failure exhibit an X-shaped pattern. Furthermore, based on our model, we examine the impact of amplitude variation on hard rocks with an equal number of cycles (5,000,000 cycles). Under ultrasonic vibration loads, amplitude influences the total input energy within the rock system. While increasing amplitude does not alter maximum deformation in rocks, it enhances fragmentation degree, fracture degree and energy dissipation coefficient—thereby improving rock breaking efficiency.

Drilling conditions serve as reliable indicators for evaluating drilling system performance. The recognition methodology forms a critical foundation for intelligent drilling and plays a significant role in preventing borehole accidents such as pipe sticking. This study proposes a recognition method for typical drilling conditions based on bit motion characteristics, developing intelligent algorithms that utilize fundamental motion parameters and drive signals as inputs to achieve accurate condition identification. Firstly, micro-drilling experiments were conducted under various pressure and rotational speed combinations. The variation characteristics of drilling speed, rotational speed, torque, and drilling pressure during the process were analyzed. Based on rotational and feed motion features at different stages, four typical drilling conditions were defined: stable drilling, slowing-down drilling, intermittent jamming, and pipe sticking. Subsequently, using actual coal mine drilling data, feature indicators for recognition models were selected through collective statistical characteristics and rolling statistical properties. Recognition models employing support vector machine (SVM), convolutional neural network (CNN), and long short-term memory (LSTM) networks were constructed and trained. Test results demonstrate that the LSTM-based model achieved the highest recognition accuracy (84% overall). It attained 100% accuracy in identifying slowing-down drilling and intermittent jamming conditions, though recognition accuracy for stable drilling remained relatively low. This research contributes to enhanced intelligent recognition of drilling conditions and provides theoretical support for advancing intelligent drilling technologies.

Multi-joint hydraulic robotic arms are core equipment in intelligent mining, yet their performance is often limited by strong dynamic coupling and nonlinear hydraulic effects. Traditional control methods struggle to achieve high-precision trajectory tracking and coordinated motion under high loads and flow-coupling constraints. To address these challenges, this paper establishes a coupled hydraulic–mechanical dynamic model for a multi-joint robotic arm. The mechanical dynamics are derived using the Lagrangian formulation, while the hydraulic dynamics account for flow coupling among cylinders. An improved deviation coupling control (IDCC) strategy is proposed, integrating feedforward–feedback compensation, coupling error regulation, and a flow-limiting correction term. Co-simulation in Simulink (2024b) and Amesim (2020) shows that under flow-saturation conditions, the improved strategy reduces the peak trajectory errors by approximately 47.88%, 28.08%, and 49.89% for Joints 1–3, respectively, and shortens the settling time by 27.93%. Experimental results from a three-joint hydraulic test platform confirm error reductions of 10.20–15.58% and a 31.50% decrease in overall adjustment time. The study demonstrates that the proposed control strategy effectively suppresses multi-joint coupling interferences, enhances trajectory tracking accuracy, and improves the adaptability of hydraulic robotic arms under flow-limited conditions, providing a viable solution for high-precision control in intelligent mining applications.