## 1 Introduction  
Unmanned aerial vehicles (UAVs) possess characteristics such as small size, low cost, and high maneuverability, making them capable of replacing humans in penetrating disaster-stricken areas to accomplish certain rescue tasks, thereby effectively assisting overall rescue efforts. During rescue missions, UAVs are equipped with obstacle detection sensors such as laser radar, ultrasonic sensors, or cameras to enable real-time perception of surrounding obstacles such as buildings, trees, and power lines, in order to avoid collisions. By collecting sensor data and using appropriate algorithms, UAVs can generate obstacle-avoidance path planning to ensure safe flight. Furthermore, due to the high complexity of post-disaster environments, rescue UAVs are also equipped with terrain and wind sensors, allowing them to perceive the ever-changing rescue mission scenarios in real-time. With this information, UAVs can plan low-risk flight paths and avoid collisions with ground obstacles. Additionally, they can monitor changes in wind speed and air pressure in the flight environment. The data from these sensors ensure that the path planning algorithm takes into account meteorological conditions, adjusting the flight path to ensure the stability and efficiency of the UAV under different flight altitudes and wind speeds. In the context of disaster response, it is of significant importance to plan a reasonable rescue mission path that effectively avoids obstacles while considering the UAV's flight characteristics, as this contributes to minimizing disaster losses [1]. Path planning technology is one of the hot research topics in the field of UAVs. In recent years, with the widespread use of UAVs, path planning has become crucial for UAVs to execute missions and avoid obstacles in their work environment. The objective of path planning is to determine a safe and feasible optimal path for UAVs, taking into account the operational feasibility in practical applications. Therefore, enhancing the path optimization capability of UAVs in complex flight environments holds significant research significance, while ensuring both safety and feasibility [2; 3; 4]. However, the increasingly complex flight environments imply a growing number of path constraints. These UAV path constraints refer to a series of limitations and regulations that must be adhered to during UAV flight to ensure safety, compliance, and effectiveness. Furthermore, path constraints may vary according to different mission scenarios and specific task requirements, adding to the increasing demands placed on UAV path planning algorithms. Although traditional path planning algorithms have reached a high level of maturity, they are not well adapted to the practical flight of UAVs in complex three-dimensional environments [5; 6; 7; 8]. Table 1 provides a comparative analysis of the characteristics of several commonly used planning algorithms. From the table, it can be observed that traditional path planning algorithms such as artificial potential field and velocity vector exhibit high planning efficiency but are limited in their applicability and relatively weak in solving capability. On the other hand, intelligent optimization algorithms like grey wolf optimization have a broader range of applicable scenarios and generally higher solving capabilities, but at the same time, they may sacrifice some efficiency. The APF method [12; 13; 14], as one of the traditional algorithms, represents the motion of a UAV in the environment as a virtual force in an artificial gravitational field. The UAV is attracted towards the target point and avoids obstacles through the repulsive force. Although APF has characteristics such as path smoothing and fast planning speed in local path planning, it tends to suffer from problems such as excessive repulsive force and becoming trapped in local minima, resulting in the inability to plan the shortest path or reach the target point.