## 1 Introduction
While many experts and scholars have conducted research on UAV path planning in post-disaster rescue scenarios, there is limited research specifically focused on post-disaster mountain rescue scenarios. Furthermore, most existing algorithms are not suitable for meeting the requirements of mountain rescue operations, and their efficiency in path planning for mountainous environments is often suboptimal. Therefore, the research presented in this paper aims to address this gap by proposing an innovative algorithm that efficiently caters to UAV post-disaster mountain rescue. By designing an algorithm that can plan efficient and safe flight paths, the proposed approach enables UAVs to effectively navigate around mountainous obstacles and quickly reach rescue points for reconnaissance and aid. Therefore, the research focus of this paper is defined as UAVs performing post-disaster mountain rescue missions. The UAV's flight environment is primarily defined as a 10*10 km mountainous terrain, with the main threat being the terrain itself. The task environment is constructed using elevation maps. The task involves the UAV taking off from the starting point (0, 0, 0), traversing mountainous obstacles, and reaching the designated rescue point (100, 100, 100) to initiate the next phase of the rescue mission. In actual post-disaster mountain rescue scenarios, UAVs encounter various complex problems, such as the complexity of the rescue terrain and environment, obstacle recognition and avoidance, operational feasibility, and safety considerations. These challenges impose stringent requirements on the algorithm's optimization capability and convergence efficiency [31]. Therefore, when UAVs fly in mountainous terrain, it is necessary to consider not only the threats posed by the environment to the UAV but also the performance constraints of the UAV itself and the efficiency of task completion. Therefore, in the context of mountain rescue missions, this paper requires the algorithm to have a fast planning rate and efficient solving capability to meet the requirements of rescue mission scenarios. Based on the analysis in Table 1, it can be observed that the most commonly used UAV-path planning algorithms currently available have certain limitations and are not fully applicable to mountain rescue scenarios. Hence, considering the requirements posed by post-disaster rescue scenarios for UAV path planning algorithms, this paper conducts an analysis and selection process to ultimately choose the APF method and GWO for improvement and integration. Our research makes three main contributions. Firstly, this paper defines and constructs the task environment by establishing a benchmark terrain model to simulate mountain rescue mission scenarios. In terms of considering the smoothness of the path, the cubic B-spline curve method is used to smooth the path generated by the algorithm. Secondly, addressing the shortcomings of the GWO in terms of optimization performance, long planning time, and the unsuitability of the APF method for three-dimensional task environments, as well as its tendency to get stuck in local optima due to excessive repulsion forces, we propose improvements and integration of the GWO and APF algorithms. Nonlinear adjustment strategies for control parameters are employed in the GWO to balance global and local search capabilities. The update strategy for individual positions is optimized to coordinate the search capability of the algorithm and reduce the probability of falling into local optima. In addition, based on the initial path generated by IGWO, the APF algorithm is improved by assigning route attraction to the path points on the initial path, so that the APF algorithm can adapt to the three-dimensional environment and plan the final path. Finally, we conduct simulation experiments comparing the path planning performance of various algorithms, including the integrated algorithm, in mountainous terrain with obstacles, to validate their effectiveness.