Automatic parking trajectory planning based on random sampling and nonlinear optimization

Abstract

The current parking trajectory planning algorithms based on geometric connections or formulation of optimization problems in automatic parking systems have strict requirements on the starting position, lower planning efficiency and discontinuous curvature of the reference trajectory. In order to solve these problems, a hierarchical planning algorithm which is combined with nonlinear optimization and the improved RRT* algorithm (Rapidly-exploring Random Tree Star) with Reeds-Shepp curve is proposed in this paper. First, the improved RRT*RS algorithm with the rapid repulsion-straddle experiment is designed for enhancing the efficiency of path planning. Second, because of the shortcomings of the Reeds-Shepp curve that can meet the minimum turning radius but not realize the continuous curvature of the path, a nonlinear optimization problem based on convex-set obstacle constraints is formulated and solved. Finally, simulation results show that the proposed parking trajectory planning algorithm in this paper can plan an effective parking trajectory with continuous curvature in different starting positions and multiple parking scenarios.