Path planning for UAV based on improved dynamic step RRT algorithm

Abstract

Abstract Path planning is an important problem in the field of unmanned aerial vehicles (UAVs). However, it is inefficient for many rapidly-exploring random tree (RRT) based methods to rapidly find a feasible solution in a complex environment. To solve the path planning problem for the UVA in a complex environment, an improved dynamic step size RRT algorithm combined with a new path length control strategy is proposed. Firstly, the algorithm adopts a biased-goal sampling strategy to guide the growth of the tree, and an expansion direction constraint strategy is adopted to limit the expansion direction of the tree. Secondly, an improved dynamic step size strategy is proposed to speed up the path searching process. Thirdly, a path length constraint strategy is designed, which is used to constrain the path length during the searching process for a solution. Finally, simulation results demonstrate that the proposed method achieves improvement in both computational time and the path quality.