Research on Path Planning Algorithm of Bidirectional Rapidly Exploring Random Tree Improved by Artificial Potential Field

Abstract

Aiming at the defects of path planning in rapidly exploring random tree(RRT) algorithm, such as low efficiency, strong randomness and slow convergence rate, a new algorithm based on artificial potential field was proposed in this paper. The algorithm used the scheme of bidirectional growth of exploration tree to make two growth trees explore and expand outwards from the starting point and the end point at the same time to accelerate the convergence speed of the algorithm. In the early stage, growth pretreatment was added to make the two growing trees take their respective endpoints as the target points and pass through the obstacle free area rapidly at one time. Artificial potential field method was used to modify the growth tree touching the obstacle and guide the path to grow towards the end. The adaptive change probability was used to select different target points as the growth directions in different periods with different probabilities to accelerate the meeting of two growing trees. After a lot of simulation experiments and data analysis, the improved bidirectional RRT algorithm has higher search efficiency, better growth path and fewer sampling points.