Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (8): 2410-2420.doi: 10.13229/j.cnki.jdxbgxb.20211121

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Design and experiment of obstacle avoidance weeding machine for fruit trees

Yong-shuo WANG1(),Jian-ming KANG1(),Qiang-ji PENG1,Ying-kai CHEN2,Hui-min FANG1,Meng-meng NIU1,Shao-wei WANG1   

  1. 1.Shandong Academy of Agricultural Machinery Science,Jinan 250110,China
    2.Shandong Academy of Agricultural Sciences,Jinan 250110,China
  • Received:2021-10-28 Online:2023-08-01 Published:2023-08-21
  • Contact: Jian-ming KANG E-mail:1005736785@qq.com;kjm531@sina.com

Abstract:

Aiming at the problems of complicated weeding operation among fruit trees and low weed removal rate, based on the idea of reciprocating telescopic motion of mechanism, an automatic obstacle avoidance weeding machine for clearing weeds among fruit trees was designed. The structure and parameters of key components such as working width adjustment mechanism, signal acquisition mechanism, automatic obstacle avoidance mechanism and weeding knife are determined by theoretical analysis of each component of the weeding machine. Using the synergy of the arc-shaped contact rod and the hydraulic system, an automatic obstacle avoidance control system is constructed, which can dynamically control the weeding knife to avoid fruit trees according to the different positions of the contact rod. A virtual prototype model was established in ADAMS, and a single factor simulation experiment was carried out to determine the key factors affecting the weeding and obstacle avoidance between plants. Taking the speed of the obstacle avoidance hydraulic cylinder, the forward speed of the machine and the elastic coefficient of the return spring as the main influencing factors, and the weed removal ratio as the evaluation index, according to the Box-Benhnken experimental design principle, using the three-factor and three-level response surface analysis method, a two The regression model of the weed removal ratio was established through the second regression combination test, and the optimal parameters were obtained as follows: the speed of the obstacle avoidance hydraulic cylinder is 126 mm/s, the forward speed of the machine is 1.57 km/h, and the elastic coefficient is 21. The field verification test was carried out, and the results showed that the average weed removal rate of the designed weeding machine was 92.65%, which met the agronomic requirements of orchard weeding. The research results provide a reference for the further optimization of the interplant weeder in orchards and other crops.

Key words: agricultural engineering, weeding machine, weeds between plants, automatic obstacle avoidance, ADAMS

CLC Number: 

  • S224.1

Fig.1

Schematic diagram of the weeder"

Fig.2

Schematic diagram of working process of weeder"

Fig.3

Schematic diagram of signal acquisition mechanism"

Fig.4

Schematic diagram of weeding structure"

Fig.5

Kinematics analysis of weeding mechanism"

Fig.6

Obstacle avoidance hydraulic system"

Fig.7

Schematic diagram of weeding knife combination"

Fig.8

Change graph of net removal ratio in a single factor test"

Fig.9

Field experiment site and prototype"

Table 1

Level of test factors"

编码

前进速度

X1/(km·h-1

液压缸伸缩速度

X2/(mm·s-1

弹性系数

X3

-11.412020
01.515030
11.618040

Table 2

Arrangement and results of test"

序号X1X2X3除草比/%
1-1.000-1.0000.00088.23
21.000-1.0000.00091.54
3-1.0001.0000.00087.58
41.0001.0000.00089.45
5-1.0000.000-1.00087.93
61.0000.000-1.00091.45
7-1.0000.0001.00089.34
81.0000.0001.00091.35
90.000-1.000-1.00091.95
100.0001.000-1.00087.58
110.000-1.0001.00090.44
120.0001.0001.00090.64
130.0000.0000.00089.10
140.0000.0000.00088.89
150.0000.0000.00089.45
160.0000.0000.00089.52
170.0000.0000.00089.64

Table 3

Analysis of variance of regression equation"

方差来源平方和自由度均方F显著性水平
模型Model30.5693.4036.04<0.0001
X114.34114.34152.15<0.0001
X25.9915.9963.56<0.0001
X31.0211.0210.880.0132
X1X20.5210.525.550.0506
X1X30.5710.576.030.0438
X2X35.2215.2255.440.0001
X120.06910.0690.730.4200
X220.000410.00040.0040.9486
X322.8612.8630.320.0009
残差Residual0.6670.094
失拟Lack of Fit0.2730.0910.940.4990
误差Pure Error0.3940.097
总和Cor Total31.2216

Fig.10

Impact of interaction factors on the net removal rate"

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