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Aiming at the problems of blind search, node redundancy, unsmooth and unsafe path of unmanned vehicle in global path planning in complex environment, a comprehensive improved path planning algorithm based on rapid exploring random tree (RRT) is proposed. Firstly, the target dynamic probability sampling strategy and the artificial potential field guided random tree expansion mechanism are introduced. Secondly, according to the vehicle kinematics model, the angle constraint and collision detection are carried out on the planned path to ensure the safety of the path. Then the reeds sheet curve is introduced to connect directly with the target pose to avoid redundant pose adjustment. Finally, the path is pruned and smoothed to get a shorter and smoother path. In the experimental part, according to different simulation environments, taking the planning time, path length and number of nodes as evaluation indexes, the path planning effects of basic RRT algorithm, basic RRT * algorithm and this algorithm are compared. The experimental results show that this algorithm has certain advantages in path planning efficiency and path quality, the planned path length is better, and meets the vehicle kinematics constraints.