## 1 Introduction
[17] combined the environmental potential field and RRT to optimize the sampling and expansion strategy, which guided the tree to expand in the direction away from obstacles and close to the target, improving the efficiency of UAV path planning. [PERSON] et al. [18] proposed a two-layer optimization of A* and DWA, which optimizes the node-expansion model and the DWA evaluation function, extracts the critical path nodes, and achieves the effective tracking of local paths to global paths. Additionally, meta-heuristic algorithms are getting more and more attention from scholars. Traditional representative algorithms include Genetic Algorithm (GA) [19], Particle Swarm Optimization (PSO) [20], Ant Colony Optimization (ACO) [21], Artificial Bee Colony (ABC) [22], etc. New intelligent optimization algorithms emerging in recent years include the Grey Wolf Optimizer Algorithm (GWO) [23], Whale Optimization Algorithm (WOA) [24], Sparrow Search Algorithm (SSA) [25], etc. Although these algorithms have been widely used in UAV path-planning problems, they still suffer from insufficient robustness and tend to fall into local optimization in complex environments. The African Vulture Optimization Algorithm (AVOA) was proposed in 2021 and is an intelligent optimization algorithm inspired by the foraging and social behavior of African vultures. The algorithm has a relatively simple structure, strong adaptability and search ability, and can show good results in solving a variety of complex optimization problems [26; 27]. [PERSON] et al. [28] applied AVOA to robot path planning in static and dynamic environments, and the simulation results showed that AVOA outperforms Adaptive Particle Swarm Optimization (APSO) and Hybrid Fuzzy Wind-Driven Optimization (WDO)algorithms in both environments. [PERSON] et al. [29] utilized AVOA for feature selection in a dataset in a network security intrusion-detection problem, demonstrating the potential of this algorithm for application in smart-grid network security. In the literature [30], [PERSON] et al. performed cost minimization and created a parametric design of a shell-and-tube heat exchanger (SHTHE) using AVOA and found the optimal solution with all constraints. [PERSON] et al. [31] proposed an energy-efficient clustering scheme (AVOACS) based on AVOA, which achieves a dynamic balance of node energy consumption by simulating the foraging behavior of vultures, thus enhancing the stability and lifetime of the network. Although AVOA has been used in several fields, the algorithm still has some shortcomings. First, in the early exploration stage, although AVOA added certain exploitation mechanisms to accelerate the convergence speed, this approach weakened the algorithm's global search ability, which easily led it to fall into a local optimum at a later stage. Secondly, the choice of exploration and exploitation is dependent on the level of starvation and lacks a clear regulatory mechanism, which affects the balance between exploration and exploitation [32]. To solve the above problems, this paper proposes a Sparrow-Enhanced African Vulture Optimization Algorithm (SAVOA) and applies it to UAV path planning in both mountainous and urban environments. First, in the initialization stage of the population, the Sobol sequence is used instead of the traditional random approach to ensure the initial population is more evenly distributed in space, thus effectively improving the quality and diversity of the initial individuals. Second, the discoverer mechanism of SSA is substituted for the exploration phase of AVOA, thus realizing the balance between exploration and exploitation, while the vigilante mechanism is introduced to enhance the ability to jump out of the local optimum. Finally, in the first exploitation stage of the algorithm, multi-guided difference and dynamic rotation transformation strategies are introduced to increase the perturbation of random individuals based on the optimal and suboptimal individuals.