In order to improve the autonomy, safety and efficiency of agricultural robots in orchards, effective and reasonable motion planning methods are essential. Aiming at the problems of the traditional RRT* (rapidly exploring random tree star) global path planning algorithm in the continuous corridor environment, such as low search efficiency, low utilization of sampling points, and large corners of the generated path. The Ackerman chassis spray robot was used as the motion model, and an improved bidirectional RRT* algorithm was proposed. Firstly, a two-dimensional plane map of the orchard was established based on lidar, and the fruit trees and obstacles were regarded as obstacle areas. The obstacles were expanded with the kinematic constraints of the spray robot. Then, the improved bidirectional RRT* algorithm was used to search the path. In the process of searching the path, the dynamic terminal node guidance and potential field guidance were combined to conduct bias sampling, and the paths generated initially were de-redundant and adjacent broken line segment angle constraint processing. Finally, the third-order quasi-uniform B-spline curve was used to optimize the trajectory of the processed path points, and the collision detection and the curvature constraint of the spray robot were mainly considered in the optimization process. Experimental results showed that compared with the traditional bidirectional RRT* algorithm, the proposed improved algorithm reduced the planning time by 57.5% on average, improved the sampling point utilization by 28.55 percentage points on average, and shorted the final path by 7.14% on average. The trajectory obtained by the third-order quasi-uniform B-spline curve optimization satisfied the maximum curvature constraint of the spray robot in both environments with and without obstacles, and only turns occurred at line breaks and obstacles, which conformed to the operating trajectory conditions of the spray robot, and improved the work efficiency and autonomy of the spray robot.