Design and experiment of low-power BDS-SPP/INS fusion positioning system

ZHANG Tian; ZHANG Zhigang; LUO Xiwen; PENG Mingda; ZHANG Guocheng; HUANG Haixiang; YUAN Bingxuan; ZHANG Wenyu

doi:10.7671/j.issn.1001-411X.202305028

Journal of South China Agricultural University > 2024 > 45(3): 437-445. > DOI: 10.7671/j.issn.1001-411X.202305028

ZHANG Tian, ZHANG Zhigang, LUO Xiwen, et al. Design and experiment of low-power BDS-SPP/INS fusion positioning system[J]. Journal of South China Agricultural University, 2024, 45(3): 437-445. DOI: 10.7671/j.issn.1001-411X.202305028

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Design and experiment of low-power BDS-SPP/INS fusion positioning system

1.
College of Engineering, South China Agricultural University/Key Laboratory of Agricultural Machinery and Equipment in South China, Ministry of Education/Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence, Guangzhou 510642, China
2.
Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou 510642, China

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Received Date: May 23, 2023
Available Online: March 24, 2024
Published Date: March 14, 2024

Graphical Abstract

Abstract

Abstract

Objective
Most fusion positioning devices rely on BDS-RTK, which has high power consumption. In areas with poor network signals, differential links cannot be obtained, and only standard point positioning (SPP) can be used. SPP system has problems such as large positioning errors and weak anti-interference ability. This study is committed to addressing these issues.
Method
This study proposed a low-power fusion positioning device that used inertial navigation system (INS) and BDS-SPP sensors based on the Kalman filter fusion method. The device was tested using BDS-RTK as a benchmark to evaluate the static and dynamic errors of the BDS-SPP low-power module, the attitude and heading reference system (AHRS) zero bias and noise. Additionally, filtering fusion positioning experiments were conducted to test the AHRS breakpoint continuation in the case of BDS interference with a single antenna.
Result
The static error of BDS positioning was 0.4726 m, the average standard deviation of the dynamic BDS-SPP/INS fusion positioning system was less than 1.9137 m, and the average standard deviation reduced by 0.1652 m compared to before fusion. The breakpoint continuation experiment showed that the average standard deviation of the offset distance of the fusion positioning system was 3.6365 m, which decreased by 2.5900 m compared to before fusion. The BDS-SPP/INS fusion positioning system reduced the power consumption by 33.3 W compared to the BDS-RTK positioning system, and increased the output frequency by three times.
Conclusion
The fusion positioning device in this article improves the anti-interference capability of BDS-SPP and reduces positioning errors. It can provide positioning for agricultural robots in the absence of an RTK link, and provide a technical basis for agricultural robot navigation research.
- Agricultural robot,
- Low-power,
- BDS,
- Information fusion,
- Kalman filter,
- INS

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References (32)

References

[1]	HASSALL J. Future trends in precision agriculture[R/OL]. [2023-05-01]. https://www.nuffieldscholar.org/sites/default/files/reports/2009_AU_James-Hassall_Future-Trends-In-Precision-Agriculture-A-Look-Into-The-Future-Of-Agricultural-Equipment.pdf.
[2]	罗锡文, 王在满. 水稻生产全程机械化技术研究进展[J]. 现代农业装备, 2014(1): 23-29. doi: 10.3969/j.issn.1673-2154.2014.01.008
[3]	罗锡文, 廖娟, 汪沛, 等. 提高农业机械化水平促进农业现代化发展[J]. 中国农村科技, 2021(1): 6-11. doi: 10.3969/j.issn.1005-9768.2021.01.003
[4]	陈庆根, 廖西元. 优质水稻生产投入与产出的经济效益评价[J]. 中国农村经济, 2001(3): 18-24.
[5]	张智刚, 王进, 朱金光, 等. 我国农业机械自动驾驶系统研究进展[J]. 农业工程技术, 2018, 38(18): 23-27.
[6]	韩树丰, 何勇, 方慧. 农机自动导航及无人驾驶车辆的发展综述(英文)[J]. 浙江大学学报(农业与生命科学版), 2018, 44(4): 381-391.
[7]	张漫, 季宇寒, 李世超, 等. 农业机械导航技术研究进展[J]. 农业机械学报, 2020, 51(4): 1-18. doi: 10.6041/j.issn.1000-1298.2020.04.001
[8]	姚立健, SANTOSH K P, 杨自栋, 等. 基于超宽带无线定位的农业设施内移动平台路径跟踪研究[J]. 农业工程学报, 2019, 35(2): 17-24. doi: 10.11975/j.issn.1002-6819.2019.02.003
[9]	陈黎卿, 许鸣, 柏仁贵, 等. 高地隙植保机辅助驾驶系统设计与试验[J]. 农业机械学报, 2019, 50(9): 25-32. doi: 10.6041/j.issn.1000-1298.2019.09.003
[10]	LIU Y, MA X Y, SHU L, et al. From Industry 4.0 to Agriculture 4.0: Current status, enabling technologies, and research challenges[J]. IEEE Transactions on Industrial Informatics, 2021, 17(6): 4322-4334. doi: 10.1109/TII.2020.3003910
[11]	BECHAR A, VIGNEAULT C. Agricultural robots for field operations. Part 2: Operations and systems[J]. Biosystems Engineering, 2017, 153: 110-128. doi: 10.1016/j.biosystemseng.2016.11.004
[12]	庄朝文, 富立, 范跃祖. 多传感器融合技术在低成本车辆组合导航定位系统中的应用[J]. 中国惯性技术学报, 2002, 10(5): 1-5. doi: 10.3969/j.issn.1005-6734.2002.05.001
[13]	张京, 陈度, 王书茂, 等. 农机INS/GNSS组合导航系统航向信息融合方法[J]. 农业机械学报, 2015, 46(S1): 1-7. doi: 10.6041/j.issn.1000-1298.2015.S0.001
[14]	王力, 杨忠辉. 自适应序贯UKF的INS/BDS紧组合导航算法[J]. 计算机仿真, 2019, 36(12): 228-232. doi: 10.3969/j.issn.1006-9348.2019.12.047
[15]	SHEN C, ZHANG Y, TANG J, et al. Dual-optimization for a MEMS-INS /GPS system during GPS outages based on the cubature Kalman filter and neural networks[J] Mechanical Systems and Signal Processing, 2019, 133(106222): 1-14.
[16]	BROWN A K, LU Y. Performance test results of an integrated GPS/MEMS inertial navigation package[C]//Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004). Long Beach, California: ION GNSS, 2004: 825-832.
[17]	魏永超, 罗城仔, 邓岚, 等. 基于DS-SVR算法优化的BDS/INS组合导航[J]. 航空计算技术, 2020, 50(1): 57-60. doi: 10.3969/j.issn.1671-654X.2020.01.014
[18]	张晓寒, 赵景波, 董振振. 农机BDS/INS组合导航算法研究[J]. 中国农机化学报, 2021, 42(2): 171-177.
[19]	张智刚, 罗锡文, 赵祚喜, 等. 基于Kalman滤波和纯追踪模型的农业机械导航控制[J]. 农业机械学报, 2009, 40(S1): 6-12.
[20]	丁幼春, 何志博, 夏中州, 等. 小型履带式油菜播种机导航免疫 PID 控制器设计[J]. 农业工程学报, 2019, 35(7): 12-20. doi: 10.11975/j.issn.1002-6819.2019.07.002
[21]	李逃昌, 胡静涛, 高雷, 等. 基于模糊自适应纯追踪模型的农业机械路径跟踪方法[J]. 农业机械学报, 2013, 44(1): 205-210.
[22]	张闻宇, 丁幼春, 王雪玲, 等. 基于SVR逆向模型的拖拉机导航纯追踪控制方法[J]. 农业机械学报, 2016, 47(1): 29-36.
[23]	BEVLY D M, GERDES J C, PARKINSON B W. A new yaw dynamic model for improved high speed control of a farm tractor[J]. Journal of Dynamic Systems, Measurement and Control, 2002, 124(4): 659-667. doi: 10.1115/1.1515329
[24]	何杰, 朱金光, 张智刚, 等. 水稻插秧机自动作业系统设计与试验[J]. 农业机械学报, 2019, 50(3): 17-24. doi: 10.6041/j.issn.1000-1298.2019.03.002
[25]	段贤强, 陶建峰, 覃程锦, 等. 变速条件下农业机械路径跟踪稳定控制方法[J]. 农业机械学报, 2019, 50(9): 18-24. doi: 10.6041/j.issn.1000-1298.2019.09.002
[26]	刘兆朋, 张智刚, 罗锡文, 等. 雷沃ZP9500高地隙喷雾机的GNSS自动导航作业系统设计[J]. 农业工程学报, 2018, 34(1): 15-21.
[27]	王辉, 王桂民, 罗锡文, 等. 基于预瞄追踪模型的农机导航路径跟踪控制方法[J]. 农业工程学报, 2019, 35(4): 11-19.
[28]	张闻宇, 王进, 张智刚, 等. 基于自校准变结构Kalman的农机导航BDS失锁续航方法[J]. 农业机械学报, 2020, 51(3): 18-27. doi: 10.6041/j.issn.1000-1298.2020.03.002
[29]	靳标, 李建行, 朱德宽, 等. 基于自适应有限冲激响应−卡尔曼滤波算法的GPS/INS导航[J]. 农业工程学报, 2019, 35(3): 75-81.
[30]	洪剑青, 赵德安, 孙月平, 等. 基于卡尔曼滤波离散滑模控制的明轮船直线跟踪[J]. 农业机械学报, 2019, 50(9): 33-39. doi: 10.6041/j.issn.1000-1298.2019.09.004
[31]	白秉旭, 田光兆. 基于模糊算法的自主导航拖拉机转向系统设计[J]. 中国农机化学报, 2014, 35(6): 135-137.
[32]	刘健, 卢加华, 杨新云, 等. 中纬度地区兰勃特投影和高斯−克吕格投影变形分析[J]. 地矿测绘, 2019, 35(3): 12-14. doi: 10.16864/j.cnki.dkch.2019.0037

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