The invention discloses an unmanned aerial vehicle path
planning method based on a
batch processing prior knowledge tree, and the method comprises the steps: adding a sampling point range control parameter in a
resultant force function of an artificial
potential field method, dynamically adjusting the generation range of sampling points according to the dense degree of obstacles, defining a threshold value, and when the
resultant force function of the sampling points is smaller than the threshold value, determining the path of the unmanned aerial vehicle; if not, the point is removed. According to the process, the waste of redundant sampling and computing resources caused by a random sampling point generation strategy is greatly reduced; in the path optimization process, one elliptical sampling set is converted into two local subset elliptical sampling sets, the sampling area is reduced, and the sampling efficiency is improved; the area of a second
ellipse is increased by updating the
path cost from the starting point to the
beacon point, so that the probability of a passable area between obstacles in the
ellipse is increased; meanwhile, an obstacle density factor is introduced, the probability that
beacon points are generated beside obstacles and the possibility that paths pass through complex obstacles are increased, and the paths are optimized.