A RRT path planning algorithm based on A* for UAV

Abstract

Rapid-exploration Random Tree (RRT) is an efficient algorithm to search non-convex and high-dimensional spaces via randomly constructing spatial filling trees. This algorithm has been widely used in autonomous robot path planning. However, the basic RRT algorithm has some shortcomings. In order to improve the defects of low search efficiency and poor path quality of the RRT algorithm, this paper proposes an A* based RRT path planning algorithm with the advantages of completeness and optimality of the A* algorithm and fast extensibility of the RRT algorithm. During the procedure of random node sampling of the RRT algorithm, A* path is used to formulate the sampling strategy. Meanwhile, the constraint of the path turning angle is added to the nearest neighboring search of the RRT algorithm, which can enhance the rationality of the search tree node selection and improve the obtained path quality. Simulation experiments have been performed to verify the effectiveness of the proposed method for unmanned aerial vehicle path planning.