Welcome to Smart Agriculture

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 63-74.doi: 10.12133/j.smartag.SA202207008

• Special Issue--Key Technologies and Equipment for Smart Orchard • Previous Articles     Next Articles

Autonomous Navigation and Automatic Target Spraying Robot for Orchards

LIU Limin1,2(), HE Xiongkui1,2(), LIU Weihong3, LIU Ziyan1,2, HAN Hu1,2, LI Yangfan1,2   

  1. 1.College of Science, China Agricultural University, Beijing 100193, China
    2.College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China
    3.College of Engineering, China Agricultural University, Beijing 100083, China
  • Received:2022-07-16 Online:2022-09-30
  • Supported by:
    National Pear Industry System (CARS-28); National National Natural Science Foundation of China(31761133019); China Agricultural University 2115 Talent Cultivation and Development Support Program; Sanya China Agricultural University Research Institute Guided Fund Project (SYND-2021-06)

Abstract:

To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss.

Key words: automatic navigation, automatic target spraying, LiDAR, random sample consensus algorithm, robot, IMU

CLC Number: