## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning
#### 3.3.1 Heuristic Algorithm-Based Path Planning
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Ref.** & **Specific Algorithm** & **Contribution** \\ \hline \multirow{8}{*}{Heuristic Algorithm} & [82] & An improved A* algorithm & Simplifies the search space of the A* algorithm and improves path search efficiency. \\ \cline{2-4}  & [83] & A novel heuristic algorithm inspired by genetic algorithms & Enhances path diversity, aiding in escaping local optima. \\ \cline{2-4}  & [84] & A modified sparrow search algorithm & Improves path diversity, balances convergence speed and exploration ability, and effectively avoids stagnation. \\ \cline{2-4}  & [85] & A hybrid particle swarm optimization algorithm combined with simulated annealing & Reduces oscillation and accelerates convergence. \\ \cline{2-4}  & [86] & A heuristic crossing search and rescue optimization (HC-SAR) & Enhances convergence speed while maintaining path diversity and introduces a path smoothing mechanism. \\ \cline{2-4}  & [87] & A hybrid algorithm combining chaotic egg optimization and simulated annealing (CEO-SA) & Uses Singer mapping to enhance path diversity and simulated annealing for local search, balancing global and local search. \\ \hline \multirow{8}{*}{Machine Learning} & [88] & A lightweight 3D path planning algorithm based on YOLO & Enables rapid obstacle detection in complex environments, reduces computational burden, and improves path planning efficiency. \\ \cline{2-4}  & [89] & A multi-layer path planning algorithm based on reinforcement learning & Balances global and local optimization, enhancing path planning performance. \\ \cline{2-4}  & [90] & A DRL method for UAV path planning using global situational information & Establishes a rapid evaluation model, improving the generalization and real-time capabilities in obstacle avoidance. \\ \cline{2-4}  & [91] & A lightweight real-time path planning algorithm based on reinforcement learning & Integrates global training with local adaptability, dynamically adjusts exploration probability, and enhances robustness. \\ \cline{2-4}  & [92] & A path planner based on explainable deep neural networks & Incorporates a feature attribution model for generating intuitive textual and visual explanations. \\ \hline \multirow{8}{*}{Hybrid Algorithm} & [93] & A global–local hybrid UAV path planning algorithm based on A* and Q-learning & Simplifies the OPEN set, improves path planning efficiency, and addresses the exploration–exploitation dilemma. \\ \cline{2-4}  & [94] & A deep learning method trained via genetic algorithms for multi-UAV path planning efficiency & Uses genetic algorithms as a “coach” to provide training “experience” for deep learning without convergence processes, balancing efficiency and effectiveness. \\ \cline{2-4}  & [95] & A gray wolf optimization algorithm based on reinforcement learning & Employs reinforcement learning to enable independent operations by each wolf, avoiding local optima and smoothing issues through geometric and optimal adjustments. \\ \cline{2-4}  & [96] & A multi-strategy cuckoo search algorithm based on reinforcement learning & Dynamically invokes large-scale saltation and single-dimension shift strategies during iterations using deep learning, balancing exploration and convergence. \\ \hline \multirow{8}{*}{[97]} & & A multi-modal cooperative multi-objective particle swarm optimization algorithm based on reinforcement learning & Utilizes reinforcement learning to guide particle update selection and develops multi-modal cooperative strategies, improving efficiency and robustness. \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Significant research on UAV path planning algorithms. efficiency, and convergence speed, making them suitable for real-time applications in dynamic and complex environments. As UAV path planning continues to evolve, the integration of multiple strategies holds great potential for further improving performance and robustness in practical scenarios.