In recent decades,the asymptotically optimal Rapidly-exploring Random Tree* (RRT*) algorithm has attracted extensive attention.In order to solve the problems of slow convergence and high costs of generating path,an improved APF-Informed-RRT* fusion algorithm for UAV trajectory planning is proposed.In combination with the Informed sampling strategy,the algorithm constrains the random points in the elliptic space,and improves the search efficiency.After the new algorithm finds the nearest node,the improved APF is introduced to generate high-quality new nodes.The target point and random sampling point are attractive to the nearest node of the growing tree,and the obstacle is repulsive to it.Then,the direction of resultant force is taken as the growth direction of the random tree to solve the problem of local minimum value and greatly decrease the convergence time.Compared with RRT* and Informed-RRT*,the new algorithm is superior and effective.