## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning
#### 3.3.2 Machine Learning-Based Path Planning  
With the increasing complexity of UAV application scenarios and the growing uncertainty of environments, traditional path planning methods face significant limitations in real-time adaptability and global optimization capabilities. ML, particularly reinforcement learning (RL) and deep learning (DL), offers innovative approaches to UAV path planning in dynamic environments like SAGINs [98]. ML-driven algorithms can adapt to real-time changes and make intelligent decisions, enhancing both global optimization and local adaptability. In UAV path planning, obstacle avoidance is an essential issue that must be addressed. With the increasing application of machine learning in UAV path planning, deep learning-based object detection methods have begun to be integrated with path planning algorithms to solve real-time obstacle avoidance problems. [PERSON] et al. [88] proposed a lightweight 3D path planning algorithm based on the YOLO (You Only Look Once) object detection algorithm. This algorithm uses YOLO to quickly detect obstacles in the environment and generates collision-free paths based on the relative positions of these obstacles. This approach avoids the high computational burden of storing frontier nodes and constructing complete environmental maps, as required by traditional path planning techniques, enabling UAVs to efficiently plan paths and avoid obstacles in complex environments in real time. RL has been widely applied in UAV path planning in recent years due to its ability to learn optimal strategies through interaction with the environment, especially when dealing with dynamic and uncertain environments. [PERSON] et al. [89] proposed a multi-layer path planning algorithm based on reinforcement learning. This method uses a composite Q-value mechanism to select the optimal action in each time step, significantly improving the efficiency and performance of UAV path planning by combining global and local optimization, particularly in environments that require balancing exploration and exploitation. [PERSON] et al. [90] proposed a UAV path planning method based on DRL with global situational awareness. This approach uses the STAGE Scenario software for simulation, allowing UAVs to plan paths in dynamic environments in real time and adapt to environmental changes. The method demonstrated the potential of DRL in managing global optimization and immediate responses. [PERSON] et al. [91] developed a lightweight real-time path planning algorithm based on reinforcement learning, named adaptive soft actor-critic (ASAC). By integrating global training with local adaptability, the algorithm enhances its generalization ability. Through the introduction of cross-layer connections to prevent feature loss and an adaptive temperature coefficient that dynamically adjusts exploration probability, ASAC significantly improves the robustness and practicality of UAV path planning in complex urban environments. As UAV applications continue to expand, explainable artificial intelligence is becoming increasingly important in path planning because it provides insights into the algorithmic decision-making process, which is crucial in applications requiring transparency and interpretability. To address this issue, [PERSON] et al. [92] proposed an explainable deep neural network-based path planner by modeling the navigation problem as a Markov Decision Process (MDP) and using DRL methods to train the network in a simulation environment. They introduced feature attribution models to generate intuitive textual and visual explanations, enabling robust and interpretable path planning for quadcopter UAVs to autonomously fly in unknown environments. In summary, machine learning, particularly reinforcement learning and deep learning, has shown great promise in addressing the challenges of UAV path planning in dynamic and uncertain environments. By integrating real-time adaptability, obstacle avoidance, and global optimization, ML-driven approaches offer a significant advancement over traditional methods.