## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning
#### 3.3.1 Heuristic Algorithm-Based Path Planning  
The commonly used heuristic algorithms include traditional search methods such as the A* algorithm [78] and Dijkstra algorithm [79], as well as bio-inspired intelligent algorithms like ant colony optimization (ACO) [80] and genetic algorithms (GAs) [81]. These methods aim to efficiently find optimal or near-optimal solutions through intelligent search techniques, making them particularly suitable for real-time UAV path planning applications with limited computational resources. Traditional algorithms like the A* and Dijkstra algorithms are widely used in UAV path planning due to their simplicity and effectiveness. However, they exhibit long computational times in large-scale search spaces. To improve the efficiency of these algorithms, some studies have proposed enhancements. For instance, [PERSON] et al. [82] proposed an improvement to the A* algorithm by calculating the cosine values between the initial node and target node, as well as between the current node and target node, to filter out expansion nodes with large angular deviations. This reduces the search space and accelerates the algorithm's execution speed. Bio-inspired intelligent algorithms, such as GAs and ACO, have gained increasing attention due to their adaptability and efficiency. [PERSON] et al. [83] proposed a heuristic evolutionary algorithm, drawing inspiration from GAs, and designed replacement, crossover, mutation, length, and smoothness operations to enhance the path construction process. [PERSON] et al. [84] further extended this field by introducing a modified sparrow search algorithm. This algorithm incorporates a chaotic strategy to enhance population diversity, uses adaptive inertia weights to balance convergence speed and exploration ability, and combines the Cauchy-Gaussian mutation strategy to avoid stagnation. These improvements enable the algorithm to exhibit faster convergence and a broader search space in dynamic and complex path planning scenarios. Moreover, hybrid algorithms that combine multiple strategies have shown promising results in enhancing both global and local search capabilities. [PERSON] et al. [85] proposed a hybrid particle swarm optimization (PSO) algorithm that integrates simulated annealing (SA) to improve the global search strategy. By introducing a dimension-based learning strategy for each particle, the algorithm reduces oscillation and accelerates convergence, making it more effective in complex path planning tasks. [PERSON] et al. [86] proposed a heuristic intersection search and rescue optimization algorithm. By combining crossover strategies with the basic SAR algorithm, this method improves convergence speed while maintaining population diversity. The authors also introduced a real-time path adjustment mechanism to address UAV path constraints, such as the minimum turning radius, thus improving the feasibility and safety of path planning. [PERSON] et al. [87] proposed a hybrid algorithm combining chaotic eagle optimization and SA, termed CEO-SA. By using Singer mapping, this algorithm enhances population diversity and effectively explores the searchspace. SA is used as a local search mechanism to optimize the current best solution, thus improving both global and local search capabilities. In summary, heuristic algorithms, ranging from traditional methods like the A* and Dijkstra algorithms to bio-inspired approaches such as GAs and ACO, offer diverse and efficient solutions for UAV path planning. While traditional algorithms provide simplicity and reliability, bio-inspired methods and hybrid algorithms enhance adaptability, search