Abstract: With the development of 5G networks and Internet of things technology, research on the application of autonomous driving technology in mining has made significant progress. However, due to the heavy load of mining dump trucks, narrow roads in the mining loading areas, sharp bends, and numerous uphill and downhill sections, the path planning for autonomous driving of mining dump trucks in mining areas faces challenges. Paper addresses the path planning problem for dump trucks in loading areas and proposes an adaptive hybrid A* path planning method suitable for autonomous mining dump trucks. This method can plan a safe path for dump trucks to avoid obstacles and travel from the starting point to the destination on narrow mining roads. It also ensures that the dump trucks maintain straight travel on narrow uphill and downhill roads, avoiding instability caused by frequent turns, thereby improving the safety of the autonomous driving system.Finally, the feasibility and superiority of the adaptive hybrid A* search algorithm were validated through MATLAB simulation experiments.

Key words: mining autonomous driving; mining dump truck; adaptive hybrid A* algorithm; path planning

摘要： 随着 5G 网络和物联网技术的发展，无人驾驶技术在矿山的应用研究得到了显著推进。 但由于矿用自卸车的装载重量大、矿山装料区道路狭窄、弯道急且上下坡路段多，使得矿用 自卸车在矿区的无人驾驶路径规划面临挑战。文章研究了自卸车在矿山装料区的路径规划问 题，提出了一种适用于矿山无人驾驶自卸车的自适应混合 A *路径规划方法，该方法能够规划 出自卸车在狭长的矿山道路上从起点到终点的安全路径，并使自卸车在狭窄的上下坡道路上 保持直行，避免因频繁转向导致的颠簸失稳，从而提高无人驾驶的安全性。最后，通过 MATLAB 仿真实验验证了自适应混合 A *搜索算法的可行性。

关键词: 矿山无人驾驶；矿用自卸车；自适应混合 A *算法；路径规划