Abstract: 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.