Rapid development methodology of agricultural robot navigation system working in GNSS-denied environment

Abstract

Robotic autonomous operating systems in global n40avigation satellite system (GNSS)-denied agricultural environments (green houses, feeding farms, and under canopy) have recently become a research hotspot. 3D light detection and ranging (LiDAR) locates the robot depending on environment and has become a popular perception sensor to navigate agricultural robots. A rapid development methodology of a 3D LiDAR-based navigation system for agricultural robots is proposed in this study, which includes: (i) individual plant clustering and its location estimation method (improved Euclidean clustering algorithm); (ii) robot path planning and tracking control method (Lyapunov direct method); (iii) construction of a robot-LiDAR-plant unified virtual simulation environment (combination use of Gazebo and SolidWorks); and (vi) evaluating the accuracy of the navigation system (triple evaluation: virtual simulation test, physical simulation test, and field test). Applying the proposed methodology, a navigation system for a grape field operation robot has been developed. The virtual simulation test, physical simulation test with GNSS as ground truth, and field test with path tracer showed that the robot could travel along the planned path quickly and smoothly. The maximum and mean absolute errors of path tracking are 2.72 cm, 1.02 cm; 3.12 cm, 1.31 cm, respectively, which meet the accuracy requirements of field operations, establishing the effectiveness of the proposed methodology. The proposed methodology has good scalability and can be implemented in a wide variety of field robot, which is promising to shorten the development cycle of agricultural robot navigation system working in GNSS-denied environment.

Keywords

Agricultural robot / Global navigation satellite system (GNSS)-denied environment / Navigation system / 3D light detection and ranging (LiDAR) / Rapid developing / Methodology

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Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52105284); Leading Goose Program of Zhejiang Province(2022C02052); Applied Basic Research Project of Guangzhou Basic Research Program(202201011691)