## 1 Introduction
[PERSON] combined the Ant Colony Algorithm (ACA) with the APF method and achieved efficient three-dimensional trajectory planning. This approach produces smoother paths compared to ACA alone but comes at the expense of higher computational complexity [19]. The A* algorithm combined with the Gray Wolf Optimizer (A*-GWO) proposed by [PERSON] et al. can produce safer and shorter paths in environments where wind influence is considered compared to the traditional A* algorithm. However, it cannot guarantee superiority over the A* algorithm in all scenarios [20]. [PERSON] et al. proposed the Sand Cat Swarm Optimization (SCSO) algorithm, which combines spiral search, Levy flight [21], tent chaotic mapping [22], and mechanisms such as adaptive sparrow warnings--among other random search strategies--to adapt to dynamic environmental changes [23]. The advantage of this type of algorithm lies in its ability to further optimize the performance of a single intelligent algorithm through algorithmic combinations. It enables dynamic obstacle avoidance in complex environments and supports the optimization of multiple planning objectives. However, its convergence performance is highly sensitive to initial parameter settings, resulting in suboptimal real-time performance. There is another approach that combines neural-network-based algorithms with reward designs to adapt to specific scene applications. [PERSON] et al., using a Deep Reinforcement Learning (DRL) framework combined with Independent Proximal Policy Optimization (IPPO) and employing Reference Signal Receiving Power (RSRP) as a criterion, planned UAV trajectories suitable for complicated scenarios; however, this algorithm is specifically designed for communication applications [24]. [PERSON] achieved autonomous UAV flight trajectory planning in unknown environments by designing the current reward value and state-action value selection strategy, combined with DRL algorithms; however, the scenarios are limited to indoor settings [25]. [PERSON] et al. used a residual convolutional network to learn environmental features offline and predict heading in real time, improving online planning speed; however, this method heavily depends on training data, and its generalization capability still requires validation [26]. [PERSON] et al. used a Dual Double Deep Q-Network (D3 QN) algorithm, combining a greedy strategy, heuristic search rules, and contextual graph Q-values for flight action selection. The method considers safe flights in a radar-detected but threat-prone environment, although it has high computational costs [27]. [PERSON] et al. proposed an online adaptive path planning solution based on the fusion of RRT and DRL algorithms, achieving fast planning and dynamic obstacle avoidance. It has been successfully applied in the agricultural monitoring field, but flight verification is limited to small-scale areas, requiring further resolutions of real-world scenario generalization and computational efficiency issues [28]. Algorithms based on DRL perform exceptionally well in dynamic adaptability and autonomous decision-making. However, they are currently constrained by data and perform poorly with respect to computational power, generalization ability, and explainability. As a result, they are more suitable for a specific scenario as supplements rather than a complete replacement of traditional route planning methods. The review demonstrates that effective UAV path planning requires careful algorithm selection and combination tailored to specific mission scenarios. For time-critical applications, like disaster rescue and logistics delivery, the computational efficiency of path planning algorithms becomes a paramount performance metric. Recent studies have demonstrated the effectiveness of Voronoi diagrams as a fundamental tool in path planning, serving both as a tool for initial route generation and as a basis for hybrid algorithms. For instance, in urban low-altitude logistic UAV path planning,Zhang et al.