Journal of Robotics
Volume 2015 (2015), Article ID 696301, 9 pages
http://dx.doi.org/10.1155/2015/696301

Research Article

Study and Experiment on a Wheat Precision Seeding Robot

Lin Haibo,¹ Dong Shuliang,² Liu Zunmin,¹ and Yi Chuijie³

¹College of Mechanical Engineering, Qingdao Technological University, Qingdao 266520, China
²School of Mechanical Engineering, Harbin Institute of Technology, Harbin 150001, China
³Environmental Protection Bureau of Shandong Province, Jinan 250101, China

Received 29 April 2015; Accepted 1 November 2015

Academic Editor: Yangmin Li

Copyright © 2015 Lin Haibo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The wheat precision seeding technology provided an advanced agricultural support for the high yield of wheat. But the lack of effective agricultural machine made this technique difficult to apply widely. In this paper a wheel mobile robot to achieve the wheat precision seeding technology was designed. The kinematic model of the robot was built and simulated. And experimental study was taken under different operating conditions. Because of multiple effort factors, a quadratic orthogonal rotation combination design method was applied in the experiments, identifying the main factors by analysis. Then the field test was carried out according to the main factors. The experiment results showed that the qualified rates of seeding exceed 93% in different sowing speed. That reached the agronomic requirements of wheat precision seeding.

1. Introduction

With the development of agriculture in China, the traditional intensive and meticulous farming had been unable to meet the requirements of current agriculture and agronomy development. Using agricultural machinery instead of the traditional manpower had become a major trend in the world [1, 2]. And the agricultural robot had become an important target in research and development of agricultural machinery engineering [3, 4]. In agricultural robotics research, Japan, the United States, and other countries were in the lead [5, 6]. In China it started in these years, and there was a certain gap in technology comparing with the developed countries [7, 8].

Wheat is one of major food crops in China. China’s wheat acreage was 24 million hectares in 2013. Increase of wheat production was of great significance for China’s food security. Academician Songlie from Shandong Agricultural University proposed the wheat precision seeding agronomic techniques to improve wheat yield. Using Lumai-5 as experimental wheat seed, the experiments results showed that the yield of the precision seeding techniques was greater by 7.5% to 22.3% than that of the traditional seeding techniques [9]. However, due to the special characteristics of the wheat seed grain geometry, now the mechanization for the wheat precision seeding techniques was still in research stage. Automatic agricultural vehicles had received attentions from the 1920s [10]. Especially in the recent 20 years, with the development of computer and sensor technologies, numerous researches on this subject, particularly agricultural robots, had been reported [11–15]. In this paper, a wheel mobile robot was designed and developed for the wheat precision seeding. A kinematic model was built for the four-wheel drive robot, and some experiments were taken using this machine. It provided a reference for the design and product of wheat precision seeding robot.

2. Design of the Precision Seeding Robot

According to the working environment of the robot and the agronomic requirements of the wheat precision seeding techniques, this robot was designed with four-wheel drive, the drive system using servo motor, and the steering system using stepper motor [16]. To ensure the robot’s load-bearing capacity and robustness, the frame was welded by rectangular steel together, seen in Figure 1. Figure 2 was the system block diagram of the wheat precision seeding robot. The robot was mainly divided into three parts: the robot mobile body, the control system, and the sensor system. And, the robot mobile body was divided into the mobile platform and the precision seeding mechanism. The control system was the core of the robot, seen in Figure 2. It could process the sensor signal and the information received in the working time. By adjusting the coherent drive motors, the control system could precisely control the movement of the robot, the picking up and seed of the wheat, and the adjustment of the pressure of the vacuum chamber, realizing the agronomic requirements of the wheat precision seeding.

Figure 1: Wheat precision seeding robot.

Figure 2: System block diagram of the robot.

3. Kinematic Model and Simulation

For the mobile robot, the model reflected the relationship between the state variables and control parameters of the robot. So, It was essential to build the model to design an effective tracing control law for small error or no error robot trajectory tracking. In this paper, the precision seeding robot’s platform was designed with 4-wheel drive and 4-wheel steering. Rotation and steering of the four wheels are controlled independently, there were four servomotors to control each wheel for rotation, and there were also four step motors to steer. A central controller coordinated the eight motors to work well together. Theoretically, the four-wheel steering scheme has all the degrees of freedom in the plane [17]. But, there were many forms that were not practical in actual application. Figure 3 shows some basic operation mode. Considering the characteristics and the actual situation of the working in the field, mode (c) was selected in the design of the wheat precision seeding robot.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Figure 3: Some basic operation mode. (a) Four-wheel same direction steering, (b) front wheel steering, (c) four-wheel opposite steering, (d) differential speed mode, (e) slide steering mode, (f) zero radius steering mode, and (g) braking mode.

Two assumptions were made in this paper for the kinematic model of the robot: () the platform of the robot was considered as a rigid body and the wheel was considered as a rigid wheel; it was that the elastic deformation of the wheel and the ground was ignored; () the robot did not consider the vertical and pitching motion, excluding the impact of roll.

In this paper, we discussed the kinematic model of mobile robot when front and rear wheels are steering in the opposite direction, showing in Figure 3(c). Through analyzing the four-wheel opposite steering mode, there were some certain relationships in the steering angle between the rear wheels and the front wheels and the speed between the left wheels and the right wheels.

Figure 4 was the schematic diagram of four-wheel steering mode. was the instantaneous center of the mobile robot platform; was the coordinate system of the mobile robot; is the rear wheels’ axis center; was an intersection point of the line through the point perpendicular to the body axis and the extended line of the wheels axis; is the reference point for precision seeding meter. was the steering angle of the front-left wheel; was the steering angle of the rear-left wheel; was the speed of the front-left wheel; was the velocity of the rear-left wheel; was the velocity of the reference point ; was the velocity of ; was the distance between and the front wheels axis.

Figure 4: Schematic diagram of four-wheel steering mode.

Given , the steering angle of front-left wheel, and , the steering angle of rear-left wheel, the following relationship could be obtained from the triangle:

By the velocity of rear-left wheel, the robot’s angular velocity around the instantaneous center could be obtained as

As , the velocity of was

The kinematic model of the system could be established with for reference points as

From the relationship of points and , (5) could be derived:

By taking derivation on both sides of (5), the kinematics model with for reference point was

It was

In (7), , , and could be obtained from and . And the relationship of the left wheels and the right wheels was

From these equations, when , the front wheel steering kinematic model of the robot could be obtained.

Based on the kinematic model, a trajectory tracking controller was built and simulation was taken on the control law. The error differential equation of the wheat precision seeding robot was

First, straight line trajectory tracking was simulated. Set the linear equation as follows:

In straight line motion, , the velocity was constant, and set , . So the differential equation of the robot was as follows:

The initial location of the robot was , the initial location of the desired trajectory was , and Figure 5 was the error curve of straight line tracking. Figure 6 was the actual movement of the robot tracking a straight line.

Figure 5: Diagram of error curve of straight line tracking.

Figure 6: Diagram of the movement of straight line tracking.

From the simulation, the errors of and and the angle became 0 after 3.5 s. The actual path and expected path were overlapped after 3 s.

Then, circular trajectory tracking was simulated. The circle equation was

The robot’s initial position was and the initial position of expected path was . The expected linear velocity and angular velocity were . The controller parameter was . The differential equation for the location of the robot was as follows:

Figures 7 and 8 showed the result of simulation.

Figure 7: Diagram of error curve of circular tracking.

Figure 8: Diagram of the movement of circular tracking.

From the diagram, the errors of and and angle became 0 after 3 s. In the movement diagram, the path turned 2 circles before the actual curve to coincide with expected path, so the error in -axis should better be improved.

4. Design of Seeding Experiments

During the working, for the robot, the main factors affecting the quality of seeding were three: the pore size of planting tray, the pressure (negative) of vacuum chamber, and the speed of the planting tray [18]. The pressure of vacuum chamber and the pore size of the planting tray had a direct impact on the leakage rate and reseeding rate. The smaller size needed the bigger pressure, and if the pore size was too small, it would cause the absorbing force which was too weak to pick up the seed, resulting in leakage phenomenon. If the pressure was too big, it would cause the increase of the reseeding. Meanwhile in the seeding process, if the speed of planting tray exceeded a certain limit, there was no adequate filling time, likely to cause serious leakage phenomenon [19–21].

Due to the fact that the impact of these factors on the qualified rate was not a simple linear relationship, the quadratic orthogonal rotation combination design method was taken in this paper to arrange the experiments. The regression analysis was used to analyze the connection between the factors and determine the relation of primary and secondary between them.

According to the quadratic orthogonal rotation combination design method, the test points were composed of 3 types of combination [22, 23]; it was

And was the test number of factor point, was the test number of star point, and was the test number of zero level center point. The number of factors was 3, so all the test numbers were 23.

According to the test and experience, the best combination of factors was selected as the zero level of the experiments, the pore size of the planting tray was 2.4 mm, the speed of the planting tray was 30 r/min, and the pressure of the vacuum chamber was −2.3 kPa. The values and code table of each level of the 3 factors were determined according to the zero level, as shown in Tables 1 and 2.

Table 1: Code variable design level and the value.

Level

Value

Table 2: Level code table of tests factors.

Level  

Pore size  
/mm
Speed  
/(r/min)
Pressure 
/kPa

Table 3 was the orthogonal rotation design structure matrix and calculation table of the seeding qualified rate.

Table 3: Orthogonal rotation design structure matrix and calculation.

Number

1
1
1
1
1
1
1
1
0.406
0.406
0.406
85.780

2
1
1
1
−1
1
−1
−1
0.406
0.406
0.406
82.370

3
1
1
−1
1
−1
1
−1
0.406
0.406
0.406
94.650

4
1
1
−1
−1
−1
−1
1
0.406
0.406
0.406
93.270

5
1
−1
1
1
−1
−1
1
0.406
0.406
0.406
87.560

6
1
−1
1
−1
−1
1
−1
0.406
0.406
0.406
80.350

7
1
−1
−1
1
1
−1
−1
0.406
0.406
0.406
93.420

8
1
−1
−1
−1
1
1
1
0.406
0.406
0.406
83.380

9
1
1.682
0
0
0
0
0
2.234
−0.594
−0.594
92.830

10
1
−1.682
0
0
0
0
0
2.234
−0.594
−0.594
84.620

11
1
0
1.682
0
0
0
0
−0.594
2.234
−0.594
85.220

12
1
0
−1.682
0
0
0
0
−0.594
2.234
−0.594
95.360

13
1
0
0
1.682
0
0
0
−0.594
−0.594
2.234
93.830

14
1
0
0
−1.682
0
0
0
−0.594
−0.594
2.234
90.430

15
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
94.600

16
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
92.140

17
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
90.230

18
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
92.600

19
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
95.120

20
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
92.460

21
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
94.340

22
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
93.280

23
1
0
0
0
0
0
0
−0.594
−0.594
−0.594
95.640

2083.48
25.169
−45.715
27.759
−10.880
−12.460
−0.800
−34.979
−26.127
−15.720

23.000
13.660
13.660
13.660
8.000
8.000
8.000
15.890
15.890
15.890

90.586
1.843
−3.347
2.032
−1.360
−1.558
−0.100
−2.201
−1.644
−0.989

46.376
152.995
56.409
14.797
19.406
0.080
76.998
42.959
15.552

Because the design factors were the function of the norms variable confirmed by a dimensionless linear coding, these among the first term regression coefficients and and the interaction terms were not relevant. Therefore, the affection to seeding qualified rate was directly judged by comparing the absolute values of the norms variable regression coefficient . So the influence order within a certain range was . Among them, the speed was the biggest effect on the seeding performance, the pressure was less, and the pore size was the smallest.

Based on the above analysis, the conditions for the seeding experiments of the robot were listed in Table 4. The experiments were taken under different speeds to verify the performance of the robot.

Table 4: Experiment parameters list.

Item
Value
Unit

Pore size
2.5
mm

Pressure
−2.4
kPa

Seed space
6
cm

Speed
0.7, 0.9, 1.1
m/s

5. Result Analysis

For the three speeds, 100 adjacent seed spaces as a sample were selected from each speed experiment. Tables 5–7 were the selected data at the speeds of 0.7 m/s, 0.9 m/s, and 1.1 m/s. Figures 9–11 were the statistical chart of the measured seed space under each speed.

Table 5: Measured data at speed of 0.7 m/s.

100 adjacent seed spaces at speed of 0.7 m/s (cm)

6.1
7.7
7.9
6.8
5.9
5.8
3.9
7.2
6.0
7.0

6.5
6.1
14.8
5.9
5.6
4.8
5.0
6.4
5.6
4.8

5.0
5.7
7.4
5.6
5.6
3.0
6.8
8.0
0.5
3.0

4.9
5.8
6.1
7.2
4.8
6.2
15.9
8.7
6.8
8.0

6.5
6.2
5.5
6.0
6.4
7.3
7.6
8.9
5.4
6.8

6.7
7.0
5.9
8.1
6.5
8.5
8.4
6.4
9.0
3.9

5.0
6.4
7.4
9.2
6.0
6.3
8.0
5.8
8.4
6.5

4.4
6.0
4.6
8.4
7.2
6.4
7.4
6.0
8.9
5.4

6.8
9.3
5.2
6.0
8.1
6.0
4.6
6.5
7.2
6.0

6.0
9.0
3.9
5.4
5.4
9.3
5.2
7.5
8.6
5.4

Table 6: Measured data at speed of 0.9 m/s.

100 adjacent seed spaces at speed of 0.9 m/s (cm)

6.4
8.4
6.5
6.6
7.6
6.8
5.8
7.9
5.0
6.6

6.2
8.9
5.4
6.8
5.7
6.0
6.4
6.5
4.0
6.8

6.2
7.2
6.0
8.0
3.8
7.4
9.2
7.2
7.0
6.0

6.3
5.9
5.8
7.5
6.5
4.6
8.4
5.9
7.3
8.1

7.0
6.0
7.6
7.3
7.1
16.7
6.5
6.0
4.5
7.9

7.3
1.0
5.1
8.4
8.4
9.0
7.6
8.5
8.1
5.6

4.5
7.9
6.3
9.1
6.3
8.0
5.1
7.9
5.1
8.4

8.1
5.6
4.8
7.0
5.1
5.5
6.3
8.1
6.3
9.0

7.5
6.1
2.0
6.8
3.5
5.9
4.8
5.9
6.7
7.8

6.5
8.0
5.9
8.4
6.1
7.4
6.0
6.9
6.1
6.8

Table 7: Measured data at speed of 1.1 m/s.

100 adjacent seed spaces at speed of 1.1 m/s (cm)

6.0
5.7
6.9
6.9
8.9
8.3
7.5
8.4
7.2
6.0

6.0
6.0
8.0
7.9
6.0
9.0
5.8
6.0
8.1
7.4

5.4
6.8
9.2
9.4
7.0
8.0
4.6
5.4
5.4
5.4

4.9
6.3
5.4
5.6
7.5
6.1
6.7
6.5
7.0
6.6

7.3
5.1
5.0
6.0
6.8
6.4
5.0
3.4
8.4
6.8

6.5
8.0
5.8
4.7
6.0
8.1
6.0
4.6
6.5
7.6

7.8
7.4
6.0
9.8
5.4
5.4
16.3
5.2
7.5
7.6

7.2
4.6
6.5
1.2
6.6
7.6
6.8
5.8
15.5
5.2

5.2
5.2
7.5
6.0
6.9
8.6
6.0
8.0
5.4
6.8

6.2
5.8
8.1
5.4
5.4
5.4
5.6
6.3
6.2
7.0

Figure 9: Statistical chart at 0.7 m/s.

Figure 10: Statistical chart at 0.9 m/s.

Figure 11: Statistical chart under 1.1 m/s.

According to the standard GB/T6973-2005 (testing methods of single seed drills (precision drills)), the leakage was that the seed space was greater than 1.5 times the set distance. And the reseed was that the seed space was smaller than 0.5 times the set distance. So, the seed space was divided into several ranges: , , , , and ; was the set seed space. Consider

Define the leakage number , the qualified number , the reseed number , and the interval number as follows:

The following were the formulas to calculate the reseed rate , leakage rate , and qualified rate :

Table 8 was the calculation result of the rates under different speed.

Table 8: Results under different speed.

Speed (m/s)
Qualified rate (%)
Reseed rate (%)
Leakage rate (%)

0.7
94.81
0.96
4.80

0.9
95.15
0.97
3.88

1.1
93.33
0.95
5.71

From Table 8, it could be concluded that the qualified rates under different speed were all greater than 93%.

6. Summary

Through the above experiments, the results could verify the performance of the wheat precision seeding robot in the actual work environment. But there were differences under the different speed. And the speed was the main factor in the wheat precision seeding.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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