Abstract: Aiming at the problems of low efficiency and rough path planning due to the randomness and blindness of node expansion in Bi-directional rapidly-exploring random tree algorithm, this paper proposes a goal-directed Bi-RRT algorithm based on information inspiration. In order to reduce the randomness and blindness of node expansion, the tree node expansion method is optimized. The node information generated by regression analysis is used to optimize the extended node evaluation function to strengthen the target tropism of node growth, and the expansion direction is constrained by node and environmental cost. The redundant nodes in the initial path are eliminated by branch and bound method, and the path satisfying the maximum steering Angle constraint is obtained. The cubic B-spline curve is used to smooth the path to improve the smoothness and continuity of the path. Finally, the proposed algorithm is compared with other classical algorithms in different environments based on the MATLAB simulation platform, and the experimental results verify the effectiveness and enforceability of the proposed algorithm.

Key words: path planning, rapidly-exploring random tree, information inspiration, path optimization