应用无人机技术代替机务维修人员对飞机表面检测是飞机维修领域中的一个研究热点。基于飞机表面的形状特征对其进行区域划分，依据摄像头视场参数设定无人机位置高度，使用三次样条插值法对此位置进行插值并沿法向量偏移生成视点，提取相同高度的视点，选用不同智能算法开展二维路径规划研究。通过仿真对比不同智能算法在飞机表面检测任务中路径规划的优劣，结果表明，相比于禁忌算法、遗传算法及麻雀搜索算法，模拟退火算法效果最佳，通过合理规划不同高度间路径的连接顺序，该方法可以实现飞机表面的全覆盖检测。

Using the unmanned aerial vehicle(UAV) to perform the visual inspection of the aircraft surface instead of the maintenance staffs is a research hotspot in the field of aircraft maintenance. According to the shape characteristics of the aircraft, it's divided into several parts. Based on the parameters of the camera carried on a UAV, the position of the UAV can be set. The cubic spline interpolation was adopted to interpolate the UAV positions and an offset along normal vectors of the aircraft surface was set to generate viewpoints. Extracting viewpoints with the same height, different intelligent algorithms were employed to achieve the path planning of the aircraft surface inspection in the horizontal dimensional. Compared different intelligent algorithms, the simulated annealing algorithm(SA) has the better performance compared to tabu search(TS), genetic algorithm(GA), and sparrow search algorithm(SSA). This method will provide the coverage inspection of the aircraft surface by connecting different altitudes properly.