Integrated Navigation Method for Orchard-Dosing Robot Based on LiDAR/IMU/GNSS

Abstract

To enhance the localization reliability and obstacle avoidance performance of the dosing robot in complex orchards, this study proposed an integrated navigation method using LiDAR, IMU, and GNSS. Firstly, the tightly coupled LIO-SAM algorithm was used to construct an orchard grid map for path planning and obstacle avoidance. Then, a global localization model based on RTK-GNSS was developed to achieve accurate and efficient initial localization of the robot’s coordinates and heading, and a Kalman filter was applied to integrate GNSS and IMU to improve robustness. Next, an improved A* algorithm was introduced to ensure the global operational path maintained a safe distance from obstacles, while the DWA algorithm handled dynamic obstacle avoidance. Field tests showed that the global localization model achieved an accuracy of 2.215 cm, with a standard deviation of 1 cm, demonstrating stable positioning performance. Moreover, the global path maintained an average safe distance of 50.75 cm from the obstacle map. And the robot exhibited a maximum absolute lateral deviation of 9.82 cm, with an average of 4.16 cm, while maintaining a safe distance of 1 m from dynamic obstacles. Overall, the robot demonstrated smooth and reliable autonomous navigation, successfully completing its tasks.