Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM

Abstract

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving 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 the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was 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 a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, 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 SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Robotics Perception and Control: Key Technologies and Applications

“…However, the information provided by OSM may be insufficient for complex planning tasks, and future work needs to incorporate additional semantic knowledge, such as traffic signs and building shapes, to enhance the practicality of the navigation framework. Jiang et al [10] proposed a precise autonomous navigation system for greenhouse robots combining 3D and 2D LiDAR with SLAM to streamline real-time localization and environmental mapping. The integration of various sensors facilitates a comprehensive navigation framework, thus utilizing Dijkstra for global path planning and DWA for agile local maneuvering.…”

“…The advent of robots represents a significant milestone in technological evolution. Robots are now increasingly prevalent across a broad spectrum of applications, from industrial processes [1][2][3][4][5] and medical surgeries [6][7][8][9] to various real-world scenarios [10][11][12][13][14]. The advancement of robotics technology is fundamentally propelled by the development and integration of sensor technologies, which equip robots with essential tools for effective environmental interaction.…”

Autonomous navigation system for exhibition hall service robots via laser SLAM

“…are common 2D laser SLAM algorithms currently used for service robots for environmental map building. Considering that this research uses a Jetson nano with 2G video memory as the main controller, its arithmetic power level is limited, and by comparing the simulated environment, real environment, and central processing unit (CPU) utilization (Santos et al 2013;Jiang et al 2022;Hess et al 2016), this study chooses Cartographer SLAM algorithm. Cartographer, a graph optimization-based algorithm capable of generating resolution 5 r  cm raster maps, is a SLAM algorithm introduced by Google.…”

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

“…However, 3D LiDAR point cloud data required extensive processing of computational costs and computational requirements [ 24 ]. Therefore, Saike et al processed 3D LiDAR data in 2D to achieve autonomous navigation operations in a greenhouse [ 25 ].…”