In response to the limitations of the Informed-RRT* algorithm in path planning,such as slow convergence speed,inadequate targeting and non-smooth trajectory,a bidirectional regional sampling RRT* algorithm is proposed.Firstly,the bidirectional greedy search approach is introduced to expedite the identification of sampling points while simultaneously adapting the expansion rules governing the growth of the random tree.This dual-pronged strategy not only accelerates the search process but also enhances its alignment with specified objectives.Secondly,following the initial solution establishment,a heuristic sampling region is introduced to proximate to trajectory nodes,and the path length is continuously iteratively optimized through node reconstruction strategy within this region.Finally,a combination of intermediate point interpolation and cubic spline curve techniques is employed to smooth the path.Simulation results demonstrate that the proposed algorithm can generate fewer nodes,lower costs and smoother paths in different environment maps with less runtime.