煤矿履带式定向钻机路径规划过程中存在机身体积约束和实际场景下的行驶效率需求，而常用的A*算法搜索速度慢、冗余节点多，且规划路径贴近障碍物、平滑性较差。提出一种以改进A*算法规划全局路径、融合动态窗口法（DWA）规划局部路径的煤矿履带式定向钻机路径规划算法。考虑定向钻机尺寸影响，在传统A*算法中引入安全扩展策略，即在定向钻机和巷道壁、障碍物之间加入安全距离约束，以提高规划路径的安全性；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中，以提高全局路径搜索效率；利用障碍物检测原理对经上述改进后的A*算法规划路径剔除冗余节点，并使用分段三次Hermite插值进行二次平滑处理，得到全局最优路径。将改进A*算法与DWA融合，进行煤矿井下定向钻机路径规划。利用Matlab对不同工况环境下定向钻机路径规划算法进行仿真对比分析，结果表明：与Dijkstra算法和传统A*算法相比，改进A*算法在保证安全距离的前提下，加快了搜索速度，搜索时间分别平均减少88.5%和63.2%，且在一定程度上缩短了规划路径的长度，路径更加平滑；改进A*算法与DWA融合算法可有效躲避改进A*算法规划路径上的未知障碍物，路径长度较PRM算法和RRT*算法规划的路径分别平均减小5.5%和2.9%。

In the process of path planning for tracked directional drilling rigs in coal mines, there are constraints on the body volume and the demand for driving efficiency in actual scenarios. However, the commonly used A* algorithm has slow search speed, multiple redundant nodes, and the planned path is close to obstacles and has poor smoothness. This study proposes a path planning algorithm for coal mine tracked directional drilling rigs, which uses the improved A* algorithm to plan global paths and integrates the dynamic window approach (DWA) to plan local paths. Considering the influence of directional drilling rig size, a safety extension strategy is introduced in the traditional A* algorithm. The safety distance constraints are added between the directional drilling rig, roadway walls, and obstacles to improve the safety of the planned path. Adaptive weighting is applied to the heuristic function of the traditional A* algorithm, while incorporating the influence of the parent node into the heuristic function to improve the efficiency of global path search. The principle of obstacle detection is used to eliminate redundant nodes in the path planning of the improved A* algorithm. The segmented cubic Hermite interpolation is used for quadratic smoothing to obtain the global optimal path. The improved A* algorithm is integrated with DWA for path planning of directional drilling rigs in coal mines. Matlab is used to simulate and do comparative analysis of directional drilling rig path planning algorithms under different working conditions.The results show that compared with Dijkstra algorithm and traditional A* algorithm, the improved A* algorithm accelerates the search speed while ensuring a safe distance. It reduces search time by 88.5% and 63.2% respectively, and to some extent shortens the length of the planned path, making the path smoother. The improved A* algorithm and DWA fusion algorithm can effectively avoid unknown obstacles on the path planned by the improved A* algorithm. The path length is reduced by 5.5% and 2.9% compared to the paths planned by the PRM algorithm and RRT * algorithm, respectively.