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. 2022 Mar 10:13:815218.
doi: 10.3389/fpls.2022.815218. eCollection 2022.

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

Saike Jiang  1   2 , Shilin Wang  2   3 , Zhongyi Yi  2   3 , Meina Zhang  2 , Xiaolan Lv  1   2
Affiliations

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

Saike Jiang et al. Front Plant Sci. .

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.

Keywords: Lidar; SLAM; greenhouse; mobile robot; navigation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Hardware structure diagram of the robot navigation system.
Figure 2
Figure 2
Software structure diagram of the robot.
Figure 3
Figure 3
The framework of navigation functions.
Figure 4
Figure 4
Flowchart of point cloud filtering and fusion.
Figure 5
Figure 5
The pseudo-code of the Dijkstra algorithm.
Figure 6
Figure 6
The pseudo-code of the DWA algorithm.
Figure 7
Figure 7
Test environment and robot.
Figure 8
Figure 8
Schematic diagram of the positioning accuracy test.
Figure 9
Figure 9
Positioning deviation of the robot at each target point. (A) Target 1, (B) target 2, (C) target 3, (D) target 4.
Figure 10
Figure 10
Absolute heading deviation of the robot at each target point.
Figure 11
Figure 11
The navigation accuracy test scenario.
Figure 12
Figure 12
Schematic diagram of the navigation accuracy test.
See this image and copyright information in PMC

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