基于并联机器人的蒜瓣种芽朝向调整系统的设计与优化

doi:10.12398/j.issn.2096-7217.2023.04.003

智能化农业装备学报（中英文） ›› 2023, Vol. 4 ›› Issue (4): 20-25.DOI: 10.12398/j.issn.2096-7217.2023.04.003

基于并联机器人的蒜瓣种芽朝向调整系统的设计与优化

朱正波(), 曹昕, 张瑞宏()

扬州大学机械工程学院/江苏省现代农机农艺融合技术工程研究中心，江苏扬州，225127

收稿日期:2022-05-25 修回日期:2023-09-25 出版日期:2023-11-15 发布日期:2023-11-15
通讯作者: 张瑞宏，教授。E-mail： zhang-rh@163.com
作者简介:ZHU Zhengbo， Doctor， Lecturer， research interests： agricultural seeding technology and equipment. E-mail： zhuzb@yzu.edu.cn

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

ZHU Zhengbo(), CAO Xin, ZHANG Ruihong()

School of Mechanical Engineering/Jiangsu Engineering Center for Modern Agricultural Machinery and Agronomy Technology，Yangzhou University，Yangzhou 225127，China

Received:2022-05-25 Revised:2023-09-25 Online:2023-11-15 Published:2023-11-15
Contact: ZHANG Ruihong， Doctor， Professor， research interests： technology and equipment of planter. E-mail： zhang-rh@163.com
About author:ZHU Zhengbo， Doctor， Lecturer， research interests： agricultural seeding technology and equipment. E-mail： zhuzb@yzu.edu.cn
Supported by:
National Natural Science Foundation of China(52005309);Jiangsu Postdoctoral Research Funding Program(2021K134B);Shandong Natural Science Foundation(ZR2019QEE035)

摘要/Abstract

摘要：

蒜瓣种芽朝上播种的大蒜具有出苗早，蒜苗强壮的特点，是符合中国农艺要求的播种方法。为了提高大蒜机械化播种水平，针对目前大蒜机械化播种种芽朝上率低的问题，研究设计了一种基于并联机器人的蒜瓣种芽朝向调整系统，该系统主要包括四个模块：图像采集、图像预处理、种芽特征提取和指令驱动。首先，蒜瓣运至指定位置后，并联机器人发出的光触发信号，视觉系统接收该信号并立即采集图像。其次，通过对图像进行中值滤波、二值化和形态学处理，填充孔洞，滤除背景颗粒，得到理想的二值图像，并根据蒜瓣的大小范围对二值图像的连通域面积进行筛选，得到大小合适的蒜瓣二值图像。第三，提取并计算图像的中心轮廓和轮廓的曲率，将曲率最大的部分视为蒜瓣的种芽，从而确定蒜瓣的中心位置及朝向，该朝向信息也会被量化为角度值。最后，并联机器人根据蒜瓣的位置信息、速度矢量信息和朝向信息控制机械臂进行蒜瓣抓取和定向摆放的操作，实现蒜瓣种芽朝向的调整。最后本文对调整系统的关键参数进行了试验测试，建立了调整成功率的多元回归模型，结果表明：（1）蒜瓣重量、输送速度和抓取时间对调整成功率有重要影响。（2）影响因素的主次顺序依次为抓取时间、蒜瓣重量、输送速度。（3）当蒜瓣质量为4.54 g，输送速度为0.1 m/s，并且抓取时间为2.445 s时，模型预测的调整成功率最高，达到97.41%。本研究为蒜瓣种芽调整系统的应用提供了技术指导，有助于解决利用嵌入式工控机调整蒜瓣朝向的问题，有利于促进国内大蒜机械化播种。

关键词: 并联机器人, 种芽朝上播种, 控制系统, 朝向调整, 视觉系统

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

中图分类号:

朱正波, 曹昕, 张瑞宏. 基于并联机器人的蒜瓣种芽朝向调整系统的设计与优化[J]. 智能化农业装备学报（中英文）, 2023, 4(4): 20-25.

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.

图/表 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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基于并联机器人的蒜瓣种芽朝向调整系统的设计与优化

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

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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

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

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

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

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

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