<p>This study proposes a three-Dimensional (3D) path planning method based on hybrid strategy and artificial potential field method (HAPF) to enhance the path planning performance of mobile robots in complex 3D environments. Although the traditional heuristic algorithm has good global search capability, it is still easy to fall into local optimization in high-dimensional space, and lacks an effective response mechanism to local dynamic obstacles. Specifically, this paper firstly designs an integrated hybrid strategy of gray wolf guidance and multi-stage exponential decay mechanism to enhance the search stability and solution diversity of the algorithm. Meanwhile, based on the path points generated by the heuristic, an artificial potential field function with height potential field and target distance control potential field is introduced for the continuous optimization of local paths. The potential field model can effectively perceive the terrain changes and improve the smoothness and safety of the path while avoiding obstacles. The proposed method is validated in various 3D simulation environments, and the results show that the HAPF improves the average path length by 3.08%, 24.64%, 7.88%, and 13.52% compared with PSO, ACA, AplusPF, and HMSCSO, respectively. The method outperforms mainstream algorithms in terms of path length, computational efficiency and improved solution accuracy. It reflects strong comprehensive performance and practical application potential.</p>