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. Firstly, pre-acquired point cloud maps are preprocessed with pass-through filtering and PCA algorithms to obtain maps suitable for path planning. Additionally, tree rows within the orchard are clustered and segmented based on map orientations. Subsequently, using a combination of the least squares method and the A* algorithm, global operation path planning is conducted within the local maps. Finally, 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 guarantees full-coverage navigation operations for robots in commercial orchard environments and provides a solid foundation for further research.