履带式液压钻机是煤矿井下钻孔施工的主要装备，在瓦斯灾害、水害等灾害防治中发挥着重要作用。目前，煤矿履带式液压钻机在井下行驶主要依靠人工操作或遥控，工作环境恶劣、人员劳动强度大、智能化程度较低。2020年，国家发布了《关于加快煤矿智能化发展的指导意见》，指明了煤矿钻探装备朝智能化方向发展，而路径规划与行驶控制是煤矿钻机智能化关键技术。因此，本文对履带式液压钻机的路径规划与行驶控制方法进行研究，对减轻作业人员劳动强度和实现煤矿钻机安全、高效、智能作业具有重要意义。

针对履带式液压钻机工作环境地形复杂、低纹理的问题，对基于粒子滤波的Gmapping和基于图优化的Cartographer两种建图算法进行了研究，通过对比两种算法的优缺点，提出了一种基于Map to Map优化回环检测的Cartographer建图算法，并在建图算法中加入IMU传感器的位姿信息。通过Gmapping和Cartographer算法对Gazebo虚拟环境进行建图仿真对比实验，结果表明：优化后的建图算法效果更优，地图描绘更加清晰。通过实验室环境和煤矿主体实验室的建图结果表明：优化后的建图算法在实验室环境构建的地图更精确，地图尺寸相对误差最大为1.545%，平均相对误差为1.225%；在煤矿主体实验室环境构建的地图可以清楚反映全局地图和外围边缘，且对于回环检测没有发生漂移、错位的现象。

针对履带式液压钻机机身尺寸约束及传统A*算法路径贴近障碍、冗余节点多、安全性和平滑性不足的问题，提出了一种改进A*算法全局路径规划、融合动态窗口法(DWA)局部路径规划的路径规划算法。该算法考虑钻机尺寸影响，在钻机和巷道壁、障碍之间加入安全扩展策略；对传统A*算法的启发函数进行自适应权重优化，同时将父节点的影响加入到启发函数；利用障碍物检测原理对规划的路径剔除冗余节点，使用分段三次Hermite插值对路径进行二次平滑处理；最后将改进A*算法与DWA算法融合实现钻机在煤矿井下的路径规划。对钻机不同工况环境进行路径规划仿真分析，结果表明：在不同工况环境下，改进A*相比Dijkstra和传统A*在保证安全距离的前提下加快了搜索速度并得到相对平滑的路径，搜索时间分别平均减少了88.5%和63.2%，在一定程度上缩短了路径长度，并且融合算法可以有效避开全局路径上出现的未知障碍。基于搭建的机器人实验平台路径规划实验结果表明：A*融合DWA算法能够规划出平滑、可行性较强的路径，并且通过激光雷达检测障碍可以实现避障行驶。

针对履带式液压钻机在复杂工况下容易偏离预设路径的问题，通过分析钻机行驶液压系统的工作原理，提出一种基于神经网络PID的钻机行驶控制方法。建立钻机行驶控制系统传递函数模型，基于Simulink建立行驶控制系统模型并进行控制算法仿真分析，结果表明：相比PID控制算法，神经网络PID控制算法超调量更小、响应时间更短。通过基于PLC控制的履带行走机构实验平台进行行驶控制模拟实验，结果表明：相比PID控制算法，神经网络PID控制算法具有更快的响应速度和更高的控制精度，在转向控制实验中履带行走机构实验平台偏角最大稳态误差为0.322°，稳态误差相比PID算法减少了34.7%。

The crawler hydraulic drilling rig is the main equipment for drilling construction in underground coal mines, which plays an important role in the prevention and control of gas disasters, water disasters and other disasters.At present, the coal mine crawler hydraulic drilling rig mainly relies on manual operation or remote control in the underground, with poor working environment, high labor intensity and low intelligence. In 2020, the state issued the ' guidance on accelerating the intelligent development of coal mines ', pointing out that the coal mine drilling equipment is developing in the direction of intelligence, and path planning and driving control technology are the key technologies for intelligent coal mine drilling rigs. Therefore, the path planning and drive control method of crawler hydraulic drilling rig is studied, which is of great significance to reduce the labor intensity of operators and realize the safe, efficient and intelligent operation of coal mine drilling rig.

Aiming at the problem of complex terrain and low texture in the working environment of tracked hydraulic drilling rig, two mapping algorithms, Gmapping based on particle filter and Cartographer based on graph optimization are studied. By comparing the advantages and disadvantages of the two algorithms, a Cartographer mapping algorithm based on Map to Map optimized loop detection is proposed, and the pose information of IMU sensor is added to the mapping algorithm. The Gazebo virtual environment is simulated and compared by Gmapping and Cartographer algorithms. The results show that the optimized mapping algorithm has better effect and clearer map description. The mapping results of the laboratory environment and the main laboratory of the coal mine show that the optimized mapping algorithm is more accurate in the laboratory environment. The maximum relative error of the map size is 1.545%, and the average relative error is 1.225%. The map constructed in the main laboratory environment of coal mine can clearly reflect the global map and the peripheral edge, and there is no drift and dislocation in the loopback detection.

Aiming at the body size constraints of crawler hydraulic drilling rig and the problems of traditional A * algorithm path close to obstacles, many redundant nodes, insufficient safety and smoothness, a path planning algorithm combining the improved A * algorithm of global path planning and the dynamic window method ( DWA ) of local path planning is proposed. The algorithm considers the influence of the size of the drilling rig, and adds a safety expansion strategy between the drilling rig and the roadway wall and obstacle. The heuristic function of the traditional A * algorithm is optimized by adaptive weight, and the influence of the parent node is added to the heuristic function. The redundant nodes are removed from the planned path by using the principle of obstacle detection, and the path is smoothed twice by using piecewise cubic Hermite interpolation. Finally, the improved A* algorithm and DWA algorithm are combined to realize the path planning of drilling rig in coal mine.The path planning simulation analysis of drilling rig under different working conditions is carried out. The results show that under different working conditions, compared with Dijkstra and traditional A *, the improved A * accelerates the search speed and obtains a relatively smooth path under the premise of ensuring a safe distance. The search time is reduced by 88.5% and 63.2% on average, which shortens the path length to a certain extent, and the fusion algorithm can effectively avoid the unknown obstacles on the global path.The experimental results of path planning based on the built mobile robot experimental platform show that the A * fusion DWA algorithm can plan a smooth and feasible path, and obstacle avoidance can be realized by detecting obstacles through lidar.

Aiming at the problem that the tracked hydraulic drilling rig is easy to deviate from the preset path under complex working conditions, by analyzing the working principle of the drilling rig driving hydraulic system, a drilling rig driving control method based on neural network PID is proposed. The transfer function model of the drilling rig driving control system is established. The driving control system model is established based on Simulink and the control algorithm simulation analysis is carried out. The results show that compared with the PID control algorithm, the neural network PID control algorithm has smaller overshoot and shorter response time. The driving control simulation experiment is carried out on the experimental platform of the crawler walking mechanism based on PLC control. The results show that the neural network PID control algorithm has faster response speed and higher control accuracy than the PID control algorithm. In the steering control experiment, the maximum steady-state error of the deflection angle of the experimental platform of the crawler walking mechanism is 0.322 °, and the steady-state error is reduced by 34.7 % compared with the PID algorithm.

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