Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM - Jiang, Saike; Wang, Shilin; Yi, Zhongyi; Zhang, Meina; Lv, Xiaolan (Frontiers in Plant Science, 2022)
The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of greenhouse, how to achieve precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous Localization and Mapping (SLAM) technology is a hot spot in solving positioning and navigation in indoor unknown environment in recent years. Among them, the SLAM based on 2D Lidar can only collect environmental information at the level of Lidar, while the SLAM based on 3D Lidar demands high computation cost, hence, it has high requirements for industrial computer. In this paper, the robot navigation control system firstly filters the information of 3D greenhouse environment collected by 3D Lidar and fuses the information into 2D information, and then based on the robot odometers and inertial measurement unit information, and achieves timely positioning and construction of greenhouse environment by robot by using 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of greenhouse environmental map, but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm is used to plan the global navigation path of the robot, and the DWA algorithm is used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with standard deviation of less than 3 cm; the average course deviation is less than 3° with standard deviation of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2 m/s, 0.4 m/s and 0.6 m/s respectively: the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the standard deviation is less than 6 cm; the average course deviation is less than 3°, and the standard deviation is less than 1.5°. The positioning accuracy and navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.