Search Results (45)

In underground coal mines, narrow and irregular tunnels, dust, and gas hazards pose significant challenges to robotic path planning, particularly for shotcrete operations. The traditional A* algorithm has the limitations of limited safety, excessive node expansion, and insufficient dynamic obstacle avoidance capabilities. To address these, a hybrid algorithm integrating adaptive A*, Q-learning, and the Dynamic Window Approach (DWA) is proposed. The A* component is enhanced through improvements to its evaluation function and node selection strategy, incorporating dynamically adjustable neighborhood sampling to improve search efficiency. Q-learning re-plans unsafe trajectories in complex environments using a redesigned reward mechanism and an adaptive exploration strategy. The DWA module facilitates real-time obstacle avoidance in dynamic scenarios by optimizing both the velocity space and the trajectory evaluation process. The simulation results indicate that the proposed algorithm reduces the number of path nodes by approximately 30%, reduces the computational time by approximately 20% on 200 × 200 grids, and increases the path length by only 10%. These results demonstrate that the proposed approach effectively balances global path optimality with local adaptability, significantly improving the safety and real-time performance in complex underground environments. Full article

In the context of coal mine operations, the assurance of work safety relies heavily on efficient autonomous navigation for rescue robots, yet traditional path planning algorithms such as A and RRT exhibit significant deficiencies in a coal mine environment. Traditional path planning algorithms (such as Dijkstra and PRM) have certain deficiencies in dynamic Spaces and narrow environments. For example, the Dijkstra algorithm has A relatively high computational complexity, the PRM algorithm has poor adaptability in real-time obstacle avoidance, and the A* algorithm is prone to generating redundant nodes in complex terrains. In recent years, research on underground mine scenarios has also pointed out that there are many difficulties in the integration of global planning and local planning. This paper proposes an enhanced A* algorithm in conjunction with the Dynamic Window Approach (DWA) to enhance the efficiency, search accuracy, and obstacle avoidance capability of path planning by optimizing the target function and eliminating redundant nodes. This approach enables path smoothing to be performed. In order to ensure that the requirement of multiple target point detection is realized, an RRT algorithm is proposed to reduce the element of randomness and uncertainty in the path planning process, leading to an increase in the convergence rate and overall performance of the algorithm. The solution to the problem of determining the global optimal path is proposed to be simplified by means of the optimal path planning algorithm based on the gradient coordinate rotation method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. The findings of simulation experiments conducted within the MATLAB environment demonstrate that the proposed algorithm exhibits a substantial enhancement in terms of three key metrics: path planning time, path length, and obstacle avoidance efficiency, when compared with conventional methodologies. This study provides a theoretical foundation for the autonomous navigation of mobile robots in coal mines. Full article

The gangue mixed in raw coal has small feature differences from coal, in order to solve the existing gangue recognition, methods generally have slow detection speed and are difficult to deploy at the edge end of the problem, a lightweight gangue target detection algorithm is proposed to enhance the research for the field of coal mining. Firstly, a lightweight EfficientViT module is the backbone of the network; secondly is the introduction of the DRBNCSPELAN4 module, which can better capture target information at different scales; finally, the lightweight shared convolutional detection head Detect_LSCD is reconstructed in order to further reduce the model size and improve the detection speed for coal and gangue. The experimental results indicate that in the model compared with the original algorithm, mAP@50–95 is improved by 1.2%, model weight size, the number of parameters, and floating point operations are reduced by 52.34%, 55.35%, and 50.35%, respectively, and inference speed is accelerated by 20.87% on a Raspberry Pi 4B device. In the field of coal gangue sorting, the algorithm not only has high-precision, real-time detection performance, but also achieves significant results in the lightweight model, making it more suitable for deployment on edge equipment to meet the requirements of controlling the robotic arm sorting gangue. Full article

Legged robots often encounter the problem that the foot-end steps into empty spaces due to terrain collapse in complex environments such as mine tunnels and coal shafts, which in turn causes body instability. Aiming at this problem, this paper takes the hexapod robot as the research object and proposes a multi-segmented electrically driven single-leg compliance control strategy for robots with tripod and quadrupedal gaits, to reduce the impact when the foot-end touches the ground, and thus to improve the stability of the robot. First, this paper analyzes the kinematic and dynamic models of the multi-segmented electrically driven single leg of the hexapod robot. Then, the minimum tipping angle of the fuselage is obtained based on force-angle stability margin (FASM) and used as the index to design the single-leg pit-probing control algorithm based on position impedance control and the single-leg touchdown force adjustment control algorithm based on inverse dynamics control. Finally, this paper designs a finite state machine to switch between different control strategies of the multi-segmented electrically driven single leg of the hexapod robot, and the vertical dynamic impact characteristic index is applied to evaluate the effect of single-leg impedance control. The simulation and prototype test results show that the proposed method significantly reduces the foot-end touchdown force and improves the walking stability of the hexapod robot in complex environments compared with the multi-segmented electrically driven single leg without the compliance control strategy. Full article

Facing the support challenges of short-wall working face (15–40m) roadways in the ‘excavation–backfill–retention’ tunneling method for section coal pillars, traditional equipment struggled to achieve stable, reliable, and efficient support. This paper designed a temporary support robot for the excavation and mining system of section coal pillars to ensure the safety of equipment and personnel in short-wall working faces. The support requirements of the section coal pillar excavation and mining system were analyzed, and a general ‘driving under pressure’ temporary support scheme was proposed. The working principle of the temporary support robot was analyzed. A mechanical model for the stable support of the temporary support robot was established. The mechanical properties of the surrounding rock were analyzed, and the allowable range of the temporary support robot’s supporting force was determined while ensuring the stability of the surrounding rock. Based on the Stribeck friction theory, a dynamic model of the temporary support robot in the driving under pressure state was constructed. The boundary conditions of the dynamic model were set, and the corresponding relationship between the temporary support robot’s supporting force and its maximum static friction force was determined. This accurately described the influence of the supporting force and pushing (pulling) force on the movement during the process of driving under pressure. Through finite element simulation, the stress conditions of the temporary support robot and the floor under maximum load were analyzed, indicating that this load condition would not cause damage to the temporary support robot or the surrounding rock. Through multi-body dynamics simulation, the pushing (pulling) forces required for the temporary support robot’s movement under different supporting force conditions were obtained, verifying the feasibility of the driving under pressure action under different supporting force conditions. Moreover, the model-predicted and simulated values of the required pushing (pulling) forces during the process of driving under pressure were consistent, validating the accuracy of the driving under pressure dynamic model. This research provides a new theoretical framework for the design and dynamic analysis of temporary support equipment for short-wall working faces in section coal pillar mining, holding significant academic value and broad application prospects. Full article

To meet the mining requirements of the ’excavation–backfill–retention’ tunneling method for inter-panel coal pillars, this paper proposes an integrated ‘excavation–backfill–retention’ equipment system centered on a cutting robot. An interactive design method was employed to analyze the interaction between mining conditions and the cutting robot, constructing a ’requirements–functions–structure’ model. The robot integrates a horizontal drum cutting mechanism with a slider shoe walking mechanism, offering enhanced adaptability to various mining conditions. A parameter model was constructed to explore the relationship between the cutting arm length and the robot’s structural parameters under varying mining heights. Using a hierarchical solution method that combines local search and multi−objective genetic algorithms, the robot’s fundamental parameters were determined, enabling the development of a detailed 3D model. A kinematic model based on the modified D–H method was developed to analyze the cutting arm’s swing angle, cylinder extension, propulsion velocity, and cutting velocity in practical mining scenarios. The working range of the height adjustment and feed cylinders at different mining heights was determined through simulation. A dynamics model of the cutting drum was developed, and a coupled simulation using the discrete element method (DEM) was conducted to analyze the relationship between coal/rock hardness, drum load, and cutting depth. The simulation results indicate that as the cutting depth raises the number of cutting teeth in contact with surrounding rock, the cutting depth grows, resulting in a larger reaction force from the coal seam and greater fluctuations in drum load torque. Once the maximum cutting depth is reached, load torque stabilizes within a specific range. Considering cutting efficiency, the robot achieves a maximum cutting velocity of 1 m/min with a cutting depth of 250 mm for rock strength greater than f3. For rock strength f3, the maximum cutting velocity is 1 m/min with a 400 mm depth, and for f2, it is 2 m/min with a 400 mm depth. These findings provide a theoretical foundation for the development of adaptive cutting strategies in mining operations, contributing to improved performance and efficiency in complex mining conditions. Full article

The anti-punch drilling robot is a core piece of equipment used to realize unmanned drilling and pressure relief operations in underground coal mines. Adaptive drilling, conducted according to the coal rock properties encountered during drilling, is essential to improve the safety and efficiency of the pressure relief working face. This paper analyzed the composition of the anti-punch drilling robot drilling system and workflow of the drilling system and then calculated the optimal rotary speed and the optimal feed speed for different Platts hardness coefficients of the coal rock through the analysis of the drilling rod force. Based on the characteristics of the drilling electrohydraulic control system, a rotary adaptive controller based on a self-resistant control algorithm and a feed adaptive controller based on sliding mode variable structure control were designed. A joint simulation was carried out using AMESim 2020.1 and Simulink 2020b software to analyze the control performance of each controller. Finally, an experimental platform for the drilling robot electrohydraulic control system was constructed; different hardness coefficients of concrete specimens were used to simulate the hardness of the coal rock with different traits. Single coal rock hardness experiments and drilling experiments with sudden changes in coal rock hardness were carried out. The experimental results showed that the adaptive control strategy proposed in this paper satisfies the requirements of drilling system control. Full article

The formation of a coal mine roadway cross-section is a primary task of the boom-type roadheader. This paper proposes an intelligent robust control scheme for the cutting head trajectory of a coal mine tunneling robot, which is susceptible to unknown external disturbances, system nonlinearity, and parameter uncertainties. First, the working conditions of the cutting section were analyzed, and a mathematical model was established. Then, a high-gain disturbance observer was designed based on the system model to analyze cutting loads and compensate for uncertainties and disturbances. A sliding mode controller was proposed using the backstepping design method, incorporating a saturation function control term to avoid chattering. The eel foraging optimization algorithm was also improved and used to tune the controller parameters. A simulation model of the system was developed for performance comparison tests. Finally, experimental verification was conducted under actual working conditions in a tunnel face, and the results demonstrated the effectiveness of the proposed control method. Full article

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. Full article

The imbalance between excavation and mining is significant as it restricts the efficient development of coal resources. Slow tunneling speed is primarily due to the inability to concurrently conduct excavation and permanent support operations, and temporary support is considered a key solution to this problem. However, the mechanism by which temporary support affects the surrounding rock in unsupported are as remains unclear, hindering the assurance of stability in these areas and the determination of a reasonable unsupported span. To address this issue, this work proposed a stress distribution model as temporary support, elucidating the distribution law of support forces within the surrounding rock. By analyzing the stress differences between areas with and without temporary support, the stress field distribution characteristics of temporary support were determined. Subsequently, the evolution of stress and strain in the surrounding rock within unsupported areas was analyzed concerning changes in temporary support length, support force, and unsupported distance. The results indicated that, although temporary support does not directly act on unsupported areas, it still generates a supportive stress field within them. The maximum unsupported distance should not exceed 3 m, and there is a strong linear relationship between the optimal temporary support force and the unsupported span. Furthermore, the length of temporary support should not exceed 17 m from the tunnel face. The successful application of the shield tunneling robot system verifies that temporary support can ensure the stability of the surrounding rock in unsupported areas, confirming the validity of the temporary support stress distribution model. This research can be used to design and optimize cutting parameters and temporary support parameters, arrange equipment, and design and optimize tunnel excavation processes to achieve safe and efficient tunneling. Full article

With problems of low cutting efficiency, poor forming quality, and low control accuracy in large-section semi-coal rock roadway tunneling, comprehensive coal tunneling seriously lags behind fully mechanized mining. A fuzzy PID control method was proposed, taking the cutting robot in a shield-type tunneling system as the research object. Using mathematical analysis and other methods, a kinematic model of the cutting robot was established, and the pose transformation matrix of the cutting robot during the cutting process was obtained. A limit model for the motion space of the cutting robot cutting drum was established, and the motion relationship between the driving cylinder and the cutting drum was obtained. A fuzzy PID cutting robot control scheme was designed. The feasibility of the model was validated by simulation methods, and the correctness of the model and simulation was verified through on-site experiments. The results indicate that the established cutting robot model is correct, and the proposed fuzzy PID control method for the cutting robot is feasible. The maximum error of the center control of the cutting drum was 5.5 mm, and the average date was 0.89 mm, which was far less than the standard of 50 mm for engineering of the cross-section cutting of the roadway. This study enriches the control theory of shield-type cutting robots by improving control accuracy, enhancing the cutting efficiency of large-section semi-coal rock tunnels, ensuring the quality of section forming, and narrowing the gap between comprehensive coal tunneling and fully mechanized mining. Full article

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. Full article

Rock bolting is a commonly used method for stabilizing the surrounding rock in coal-mine roadways. It involves installing rock bolts after drilling, which penetrate unstable rock layers, binding loose rocks together, enhancing the stability of the surrounding rock, and controlling its deformation. Although recent progress in drilling and anchoring equipment has significantly enhanced the efficiency of roof support in coal mines and improved safety measures, how to deal with drilling rigs’ misalignment with the through-hole center remains a big issue, which may potentially compromise the quality of drilling and consequently affect the effectiveness of bolt support or even result in failure. To address this challenge, this article presents a robotic teleoperation system alongside a hybrid visual servo control strategy. Addressing the demand for high precision and efficiency in aligning the drilling rigs with the center of the drilling hole, a hybrid control strategy is introduced combining position-based and image-based visual servo control. The former facilitates an effective approach to the target area, while the latter ensures high-precision alignment with the center of the drilling hole. The robot teleoperation system employs the binocular vision measurement system to accurately determine the position and orientation of the drilling-hole center, which serves as the designated target position for the drilling rig. Leveraging the displacement and angle sensor information installed on each joint of the manipulator, the system utilizes the kinematic model of the manipulator to compute the spatial position of the end-effector. It dynamically adjusts the spatial pose of the end-effector in real time, aligning it with the target position relative to its current location. Additionally, it utilizes monocular vision information to fine-tune the movement speed and direction of the end-effector, ensuring rapid and precise alignment with the target drilling-hole center. Experimental results demonstrate that this method can control the maximum alignment error within 7 mm, significantly enhancing the alignment accuracy compared to manual control. Compared with the manual control method, the average error of this method is reduced by 41.2%, and the average duration is reduced by 4.3 s. This study paves a new path for high-precision drilling and anchoring of tunnel roofs, thereby improving the quality and efficiency of roof support while mitigating the challenges associated with significant errors and compromised safety during manual control processes. Full article

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. Full article

A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention. Full article