Research on Robot Path Planning Based on Point Cloud Map in Orchard Environment

Abstract

In response to the navigation requirements of robots in orchard environments, this paper presents a navigation method for orchard robots based on point cloud maps. First, point cloud maps are processed with pass-through filtering and PCA algorithms to make them suitable for path planning. Next, tree rows within the orchard are clustered and segmented based on map orientations. Then, the least squares method and the $\text{A}^{\ast} $ algorithm are combined for global path planning on local maps. Lastly, the TEB local path planning algorithm is employed to ensure that the robot navigates along the operation path. Experimental results indicate that the robot can successfully navigate orchards at speeds ranging from 0.4 to 1.0 m/s. The average longitudinal deviation obtained under these conditions is 26.7 cm, with a maximum value not exceeding 46.2 cm. The average heading angle deviation is 4.09°, with a maximum value of 8.65°. In conclusion, this approach ensures comprehensive navigation for orchard robots and lays a strong foundation for future research.

Keywords

• Orchard robot
• path planning
• 3D point cloud
• ROS