In order to promote the mechanized harvesting of bulk tea and improve the harvesting efficiency and quality of fresh leaves of bulk tea, a fusion 2D-LiDAR and Attitude and heading reference system(AHRS)was proposed in view of the fact that the sensing sensor of the current profiling tea picker is easily affected by contact force, natural light or the gap between the leaves of the tea canopy. Based on the estimation method of profiling distance of cutting knife of tea picker, an accuracy verification test bench and an automatic profiling tea picker were designed and developed, indoor and field experiments were carried out respectively. The tea picker used 2D-LiDAR to measure the profiling distance at first. In order to improve the ranging accuracy and real-time performance, combined with the acceleration sensed by AHRS, a fusion of 2D-LiDAR ranging and acceleration (FLRA) was proposed. The algorithm accuracy verification platform and method were developed to verify the effectiveness of the algorithm. The indoor test results show that the mean value of the ranging error of the profiling distance before the algorithm processing was 36.53 mm, and the standard deviation was 23.21 mm. After the algorithm processing, the mean value of the profiling distance estimation error was 8.56 mm, and the standard deviation was 6.31 mm, which improved the accuracy and real-time performance of profiling distance ranging. Field tests show that the harvesting efficiency reached 180-210 kg·h-1. The average picking rate of young shoots on the canopy covered by cutter was 92.38%. The integrity rate of bud and leaf was 85.34% and the impurity rate was 4.93%. The young shoots better than one bud and three leaves accounted for 90.72%, which meets the technical standards of bulk tea machine picking and the requirements of subsequent processing technology. Compared with the traditional ultrasonic sensing automatic profiling tea picker, the harvesting effect of bulk tea fresh leaves was improved.
WU Min
,
HUAN Xiaolong
,
CHEN Jianneng
,
DONG Chunwang
,
SHAO Bokai
,
BIAN Xianbing
,
FAN Guoshuai
. Research and Experiment on Profiling Method of Tea Picker Based on Fusion of 2D-LiDAR and Attitude and Heading Reference System[J]. Journal of Tea Science, 2023
, 43(1)
: 135
-145
.
DOI: 10.13305/j.cnki.jts.2023.01.008
[1] 刘仲华. 中国茶叶深加工产业发展历程与趋势[J]. 茶叶科学, 2019, 39(2): 115-122.
Liu Z H.The development process and trend of Chinese tea comprehensive processing industry[J]. Journal of Tea Science, 2019, 39(2): 115-122.
[2] 易文裕, 程方平, 邱云桥, 等. 单人采茶机研究现状与发展趋势[J]. 中国农机化学报, 2020, 41(11): 33-38.
Yi W Y, Cheng F P, Qiu Y Q, et al.Research status and development trend of single-person tea picking machines[J]. Chinese Journal of Agricultural Machinery, 2020, 41(11): 33-38.
[3] 韩余, 肖宏儒, 秦广明, 等. 国内外采茶机械发展状况研究[J]. 中国农机化学报, 2014, 35(2): 20-24.
Han Y, Xiao H R, Qin G M, et al.Research on the development status of tea picking machinery at home and abroad[J]. Chinese Journal of Agricultural Machinery, 2014, 35(2): 20-24.
[4] Shirai K. Traveling type tea leaf plucking machine: JP2006166710(A) [P].2006-06-29.
[5] 闫晶晶. 仿形采茶机的优化设计及与茶园管理的协调性研究[D]. 合肥: 安徽农业大学, 2019.
Yan J J.Research on the optimal design of the profiling tea picking machine and its coordination with tea garden management [D]. Hefei: Anhui Agricultural University, 2019.
[6] 汤一平, 韩旺明, 胡安国, 等. 基于机器视觉的乘用式智能采茶机设计与试验[J]. 农业机械学报, 2016, 47(7): 15-20.
Tang Y P, Hang W M, Hu A G, et al.Design and experiment of intelligentized tea-plucking machine for human riding based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(7): 15-20.
[7] 赵润茂, 卞贤炳, 陈建能, 等. 分布控制的乘坐式仿形采茶原型机研制与试验[J]. 茶叶科学, 2022, 42(2): 263-276.
Zhao R M, Bian X B, Chen J N, et al.Development and test of a ride-on profiling tea picking prototype machine with distributed control[J]. Journal of Tea Science, 2022, 42(2): 263-276.
[8] 李秋洁, 袁鹏成, 邓贤, 等. 基于移动激光扫描的靶标叶面积计算方法[J]. 农业机械学报, 2020, 51(5): 192-198.
Li Q J, Yuan P C, Deng X, et al.Calculation method of target leaf area based on mobile laser scanning[J]. Journal of Agricultural Machinery, 2020, 51(5): 192-198.
[9] 苗艳龙, 彭程, 高阳, 等. 基于地基激光雷达的玉米株高与茎粗自动测量研究[J]. 农业机械学报, 2021, 52(s1): 43-50.
Miao Y L, Peng C, Gao Y, et al.Research on automatic measurement of maize plant height and stem diameter based on ground-based lidar[J]. Journal of Agricultural Machinery, 2021, 52(s1): 43-50.
[10] 王庆, 车荧璞, 柴宏红, 等. 基于无人机可见光与激光雷达的甜菜株高定量评估[J]. 农业机械学报, 2021, 52(3): 178-184.
Wang Q, Che Y P, Chai H H, et al.Quantitative assessment of sugar beet plant height based on UAV visible light and lidar[J]. Journal of Agricultural Machinery, 2021, 52(3): 178-184.
[11] 陈日强, 李长春, 杨贵军, 等. 无人机机载激光雷达提取果树单木树冠信息[J]. 农业工程学报, 2020, 36(22): 50-59.
Chen R Q, Li C C, Yang G J, et al.Extraction of canopy information of fruit trees by UAV airborne lidar[J]. Chinese Journal of Agricultural Engineering, 2020, 36(22): 50-59.
[12] Kolb A, Barth E, Koch R, et al.Time-of-Flight cameras in computer graphics[J]. Computer Graphics Forum, 2010, 29(1): 141-159.
[13] 吴先坤, 李兵, 王小勇, 等. 单人背负式采茶机的设计分析[J]. 农机化研究, 2017, 39(8): 92-96, 101.
Wu X K, Li B, Wang X Y, et al.Design and analysis of single knapsack tea plucking machine[J]. Journal of Agricultural Mechanization Research, 2017, 39(8): 92-96, 101.
[14] Zhao R M, Hu L, Luo X W, et al.Method for estimating vertical kinematic states of working implements based on laser receivers and accelerometers[J]. Biosystems Engineering, 2021, 203: 9-21.
[15] 韩京清, 王伟. 非线性跟踪-微分器[J]. 系统科学与数学, 1994, 14(2): 177-183.
Han J Q, Wang W.Nonlinear tracking differentiator[J]. System Science and Mathematics, 1994, 14(2): 177-183.
[16] 韩京清, 袁露林. 跟踪-微分器的离散形式[J]. 系统科学与数学, 1997, 19(3): 268-273.
Han J Q, Yuan L L.The discrete tracking differentiator[J]. System Science and Mathematics, 1997, 19(3): 268-273.
[17] 农业部农业机械化管理司. 采茶机作业质量:NY/T 2614—2014 [S]. 北京: [出版者不详], 2014.
Department of Agricultural Mechanization Management of the Ministry of Agriculture. Operation quality of tea picker: NY/T 2614—2014 [S]. Beijing: [n.s.], 2014.