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

doi:10.1007/s40436-023-00438-0

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (4): 601-617.doi: 10.1007/s40436-023-00438-0

• • 上一篇

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

Run-Mao Zhao^1,2, Zheng Zhu¹, Jian-Neng Chen^1,2, Tao-Jie Yu¹, Jun-Jie Ma¹, Guo-Shuai Fan¹, Min Wu^1,3, Pei-Chen Huang⁴

1. School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China;
2. Key Laboratory of Transplanting Equipment and Technology of Zhejiang, Province, Hangzhou, 310018, People's Republic of China;
3. School of Transportation, Zhejiang Industry Polytechnic College, Shaoxing, 312000, Zhejiang, People's Republic of China;
4. College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, People's Republic of China

收稿日期:2022-07-31 修回日期:2022-09-25 发布日期:2023-10-27
通讯作者: Jian-Neng Chen,E-mail:jiannengchen@zstu.edu.cn E-mail:jiannengchen@zstu.edu.cn
作者简介:Run-Mao Zhao is currently a lecturer of School of Mechanical Engineering, Zhejiang Sci-Tech University, P.R. China. He received Ph.D. from South China Agricultural University, P.R. China. His research interest is agricultural robot.
Zheng Zhu is currently a Master degree candidate of Mechanical Engineering at Zhejiang SciTech University, P.R. China. His research interest is autonomous navigation system for agricultural robot.
Jian-Neng Chen is currently a professor of School of Mechanical Engineering, Zhejiang SciTech University, P.R. China. His research interests include analysis and synthesis of mechanism, agricultural equipment and robot.
Tao-Jie Yu is currently a Master degree candidate of Mechanical Engineering at Zhejiang SciTech University, P.R. China. His research interests include 2D and 3D machine vison in agriculture.
Jun-Jie Ma is an undergraduate student at Zhejiang Sci-Tech University. His research interest is agricultural robot.
Guo-Shuai Fan is currently a Master degree candidate of Mechanicalm Engineering at Zhejiang Sci-Tech University, P.R. China. His research interests are agricultural environment sensing and modelling. Min Wu is currently a Ph.D. candidate of Mechanical Engineering at Zhejiang Sci-Tech University, P.R. China. His research interests include intelligent agricultural equipment and control technology.
Pei-Chen Huang is currently a lecturer of College of Automation at Zhongkai University of Agriculture and Engineering, P.R. China. His research interests include agricultural robot visual navigation, motion control, deep learning, etc.
Min Wu is currently a Ph.D. candidate of Mechanical Engineering at Zhejiang Sci-Tech University, P.R. China. His research interests include intelligent agricultural equipment and control technology.
基金资助:
This research is funded by the Agricultural Equipment Department of Jiangsu University (Grant No. NZXB20210106), the National Natural Science Foundation of China (Grant No. 52105284), the Leading Goose Program of Zhejiang Province (Grant No. 2022C02052), the China Agriculture Research System of MOF and MARA and Basic, and the Applied Basic Research Project of Guangzhou Basic Research Program in 2022 (Grant No. 202201011691). We also thank the anonymous reviewers for their critical comments and suggestions for improving the manuscript.

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

Run-Mao Zhao^1,2, Zheng Zhu¹, Jian-Neng Chen^1,2, Tao-Jie Yu¹, Jun-Jie Ma¹, Guo-Shuai Fan¹, Min Wu^1,3, Pei-Chen Huang⁴

1. School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China;
2. Key Laboratory of Transplanting Equipment and Technology of Zhejiang, Province, Hangzhou, 310018, People's Republic of China;
3. School of Transportation, Zhejiang Industry Polytechnic College, Shaoxing, 312000, Zhejiang, People's Republic of China;
4. College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, People's Republic of China

Received:2022-07-31 Revised:2022-09-25 Published:2023-10-27
Contact: Jian-Neng Chen,E-mail:jiannengchen@zstu.edu.cn E-mail:jiannengchen@zstu.edu.cn
Supported by:
This research is funded by the Agricultural Equipment Department of Jiangsu University (Grant No. NZXB20210106), the National Natural Science Foundation of China (Grant No. 52105284), the Leading Goose Program of Zhejiang Province (Grant No. 2022C02052), the China Agriculture Research System of MOF and MARA and Basic, and the Applied Basic Research Project of Guangzhou Basic Research Program in 2022 (Grant No. 202201011691). We also thank the anonymous reviewers for their critical comments and suggestions for improving the manuscript.

摘要/Abstract

摘要： 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00438-0

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

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00438-0

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

Run-Mao Zhao, Zheng Zhu, Jian-Neng Chen, Tao-Jie Yu, Jun-Jie Ma, Guo-Shuai Fan, Min Wu, Pei-Chen Huang. Rapid development methodology of agricultural robot navigation system working in GNSS-denied environment[J]. Advances in Manufacturing, 2023, 11(4): 601-617.

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

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

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