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Because of the randomness of the sampling points, the bidirectional rapidly-exploring random tree algorithm (Bi-RRT) has long search time and low sampling efficiency in path planning in complex environments. For this reason, an improved Bi-RRT Path planning algorithm is proposed. The algorithm introduces a heuristic search strategy to construct a two-dimensional Gaussian distribution density function with the start and end points of the robot as the center, and use this function to constrain the generation of sampling points, so that the closer the target point is, the greater the probability of occurrence of the spatial sampling point. At the same time, some uniformly distributed sampling points are retained, so that the sampling process can not only use the location information of the target point, but also ensure the completeness of the probability. Guided by the heuristic sampling points designed by the algorithm, two random trees can quickly grow toward the target area, thereby reducing the blindness of the search and improving the efficiency of the search. The simulation results : compared with the basic Bi-RRT algorithm, the planning time of the improved algorithm in complex environments is shortened by 43.9%, the number of extended nodes is reduced by 41.4%, and the path length is optimized by 8.1%. Finally, the influence of the ratio of Gaussian distribution sampling points to the total number of sampling points on the performance of the algorithm is analyzed.