Rice Row Recognition and Navigation Control Based on Multi-sensor Fusion

被引：0

作者：

He J. ^{[1
,2
]}

Luo X. ^{[1
,2
]}

Li W. ^{[1
,2
]}

Man Z. ^{[1
,2
]}

Feng D. ^{[1
,2
]}

机构：

[1] Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou

[2] Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence, Guangzhou

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2022年 / 53卷 / 03期

关键词：

Fusion recognition; LiDAR; Machine vision; Rice row; Tracking navigation;

D O I：

10.6041/j.issn.1000-1298.2022.03.002

中图分类号：

学科分类号：

摘要：

Automatic mechanical tracking of rice rows is the key to increase the automation of field management in rice production. In order to avoid field management machinery rolling rice rows, machine vision and 2D LiDAR information were integrated to identify rice rows and perform navigation control of rice row tracking. Firstly, the rice row centroids were extracted from machine vision and LiDAR respectively, and the spatial coordinates and target areas were unified, and then a robust regression algorithm was used to fit the rice row centroids to obtain the navigation baseline and calculate the navigation parameters. Then a pre-sight tracking PID controller was designed. Finally, a rice row tracking and navigation test platform was built and experimental studies were conducted. The test results showed that the standard deviation of curve navigation test tracking simulated rice rows was 27.51 mm; the standard variance of lateral deviation of rice rows navigation test tracking mechanical shift was 43.03 mm and the standard variance of heading deviation was 3.38°. © 2022, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：18 / 26and137

共 33 条

[1] ZHOU Jun, HE Yongqiang, Research progress on navigation path planning of agricultural machinery, Transactions of the Chinese Society for Agricultural Machinery, 52, 9, pp. 1-14, (2021)
[2] ZHANG Man, JI Yuhan, LI Shichao, Et al., Research progress of agricultural machinery navigation technology, Transactions of the Chinese Society for Agricultural Machinery, 51, 4, pp. 1-18, (2020)
[3] LIU Zhaopeng, ZHANG Zhigang, LUO Xiwen, Et al., Design of automatic navigation operation system for Lovol ZP9500 high clearance boom sprayer based on GNSS, Transactions of the CSAE, 34, 1, pp. 15-21, (2018)
[4] HE Jie, ZHU Jinguang, ZHANG Zhigang, Et al., Design and experiment of automatic operation system for rice transplanter, Transactions of the Chinese Society for Agricultural Machinery, 50, 3, pp. 17-24, (2019)
[5] YUAN Zuoyun, Study on recognizing of crop row centre line with computer vision, (2005)
[6] JI R, QI L., Crop-row detection algorithm based on random Hough transformation, Mathematical and Computer Modelling, 54, 3-4, pp. 1016-1020, (2011)
[7] BAKKER T, ASSELTK V, BONTSEMA J, Et al., Autonomous navigation using a robot platform in a sugar beet field, Biosystems Engineering, 109, 4, pp. 357-368, (2011)
[8] JIANG G Q, WANG Z H, LIU H M., Automatic detection of crop rows based on multi-ROIs, Expert Systems with Applications, 42, 5, pp. 2429-2441, (2015)
[9] ZHANG X, LI X, ZHANG B, Et al., Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method, Computers and Electronics in Agriculture, 154, 3-4, pp. 165-175, (2018)
[10] PENG Y, QU D, ZHONG Y, Et al., The obstacle detection and obstacle avoidance algorithm based on 2-D LiDAR, IEEE International Conference on Information & Automation, (2015)

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