Optimal design of garlic clove’s orientation adjustment system based on parallel robot

doi:10.12398/j.issn.2096-7217.2023.04.003

Abstract

Abstract:

The upward seeding of garlic cloves has the characteristics of emergence early and strong， which is the common planting agronomy of garlic in China.Based on the parallel robot， a control system of garlic clove’s orientation was designed to improve the upward seeding rate， and to promote the garlic planting mechanization in China. This system mainly included four modules： image acquisition， image preprocessing， garlic clove feature extraction and instruction drive. First， when the garlic cloves were transported to the established position， the visual system received the photo trigger signal emitted by the parallel robot and collected the image immediately. Second， the ideal binary image was obtained by median filtering， binarization， and morphological processing of the image to fill holes and filter out background particles， and the area of the connected domain of the binary image was screened according to the size range of garlic species to obtain the garlic binary image of the right size. Third， the center contour of the image and the curvature of the contour were extracted and calculated， and the part with the maximum curvature was regarded as the bud of the garlic clove and this part was used as the clove orientation， while this orientation would be quantified as the angle value and sent to the robot. Finally， according to the position information， velocity vector information and orientation information of the garlic cloves， the parallel robot controlled the grasper to grasp the garlic clove， and the garlic clove was adjusted to a unified direction and put into the established position， so as to adjust the orientation of the garlic clove. The key parameters of the adjusting system were tested and the multiple regression model of the adjusted success rate was established. The results indicated that：（1）the indicators of adjusted success rate were impacted by weight of garlic clove， conveying speed and grasping period significantly. （2）the order of priority of influencing factors is grasping time， weight of garlic cloves， and conveying speed. （3） the combination of the weight of garlic seed （4.54 g）， the conveying speed （0.1 m/s）， and the grasping time（2.445 s） led to a relatively high adjusted success rate （97.41%）. This study provided a technological guidance for the application of the intelligent garlic clove’s orientation control system， which will be helpful for solving the problem of using an embedded industrial computer to adjust garlic clove’s orientation， and promoting the garlic planting mechanization in China.

Key words: parallel robot, upward seeding, control system, orientation adjustment, visual system

CLC Number:

ZHU Zhengbo, CAO Xin, ZHANG Ruihong. Optimal design of garlic clove’s orientation adjustment system based on parallel robot[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(4): 20-25.

Figures/Tables 6

Figure 1 Structural representation of garlic clove’s orientation adjustment system1.Frame 2.Robot body and manipulator 3.Speed control motor4.Grasper 5.First conveyor 6.Second conveyor 7.Hopper8.Control cabinet 9.Hoist 10.Vibration conveyor 11.LED belt12.Camera 13.Black box

Figure 2 Three main working steps of visual program

Table 1 Parameters and coded results of X1， X2， and X3

Levels

Weight of garlic clove

X₁/g

Conveying speed

X₂/(m·s^-1)

Grasping time

X₃/s

Figure 3 Adjustment experiments of garlic clove orientation1. Visual system 2. Vibration conveyor 3. Grasper 4.Hoist5. Garlic clove before adjustment 6. Garlic clove after adjustment7. First conveyor 8. Second conveyor 9. Hopper

Table 2 Experiment design and results

No.	X₁	X₂	X₃	Y₁/%
1	-1	-1	0	88
2	1	-1	0	87
3	-1	1	0	85
4	1	1	0	78
5	-1	0	-1	93
6	1	0	-1	89
7	-1	0	1	83
8	1	0	1	76
9	0	-1	-1	98
10	0	1	-1	94
11	0	-1	1	84

Table 3 Variance analysis of regression models

Source	Y₁
Source	Sum of squares	df	Mean square	F value	p-value
Model	591.979 4	9	65.775 49	13.723 65	0.001 1**
X₁	84.5	1	84.5	17.630 4	0.004 0**
X₂	45.125	1	45.125	9.415 052	0.018 1*
X₃	210.125	1	210.125	43.841 28	0.000 3**
X₁X₂	-1.1E-13	1	-1.1E-13	-2.4E-14	1.000 0
X₁X₃	-1.1E-13	1	-1.1E-13	-2.4E-14	1.000 0
X₂X₃	0.25	1	0.25	0.052 161	0.825 9
X $12$	204.844 7	1	204.844 7	42.739 59	0.000 3**
X $22$	2.213 158	1	2.213 158	0.461 762	0.518 6
X $32$	31.265 79	1	31.265 79	6.523 414	0.037 9*
Residual	33.55	7	4.792 857
Lack of fit	16.75	3	5.583 333	1.329 365	0.382 3
Pure error	16.8	4	4.2
Cor total	625.5294	16

Table 3 Variance analysis of regression models

Source	Y₁
Source	Sum of squares	df	Mean square	F value	p-value
Model	591.979 4	9	65.775 49	13.723 65	0.001 1**
X₁	84.5	1	84.5	17.630 4	0.004 0**
X₂	45.125	1	45.125	9.415 052	0.018 1*
X₃	210.125	1	210.125	43.841 28	0.000 3**
X₁X₂	-1.1E-13	1	-1.1E-13	-2.4E-14	1.000 0
X₁X₃	-1.1E-13	1	-1.1E-13	-2.4E-14	1.000 0
X₂X₃	0.25	1	0.25	0.052 161	0.825 9
X $12$	204.844 7	1	204.844 7	42.739 59	0.000 3**
X $22$	2.213 158	1	2.213 158	0.461 762	0.518 6
X $32$	31.265 79	1	31.265 79	6.523 414	0.037 9*
Residual	33.55	7	4.792 857
Lack of fit	16.75	3	5.583 333	1.329 365	0.382 3
Pure error	16.8	4	4.2
Cor total	625.5294	16

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