Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano

Abstract

The use of robots continues to increase in various fields in society today. Current developments require robots that are more effective and efficient in its application, especially in terms of its movement. This research is intended to design robots that move omni-directionally, making it easier to move in all directions by using the omniwheels, and the robots can detect simple object around them. The robot using 3 DC motors with encoder as feedback. System movement is controlled using Raspberry Pi 4, to move the robot to destination postition from user’s input. For robot to be able to detect certain object, the robot is equipped with infrared sensor for measure the distance and a camera for image processing purpose with jetson nano as a controller. By using inverse kinematics and odometry calculations for robot movement, it has an error of 9.51% on the x-axis and 8.12% on the y-axis at the robot's final position. The robot can detect objects using infrared sensors with error rate 0.87% and measure object sizes using a camera and image processing with error rate of 30.02% for object’s width readings and 41.8% for object’s height readings.

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JIRAE, Vol. 4, No. 2, October 2019, 57-62 DOI: 10.9744/jirae.4.2.57-62
e-ISSN 2407-7259
Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano
Evert Oneil1,a, Indar Sugiarto2,b
1,2 Electrical Engineering, Petra Christian University, Surabaya, Indonesia
aevertoneil1@gmail.com, bindar@ieee.org
Abstract. This paper describes a process of designing a low-cost robot that moves omni-directionally that capable
of detecting simple objects around it while moving. The robot uses three DC motors with encoder as feedback. The
overall movement control is done by a Raspberry Pi that is embedded into the robot. It also uses ROS (Robotic
Operating System) as the framework. To detect certain objects, the robot is equipped with an infrared sensor for
measuring the object distance, and a camera for capturing the image of the object that will be processed by a Nvidia
Jetson Nano. By using inverse kinematics and odometry calculations, the robot can move smoothly with error at
about 9.5% on the x-axis and 8.1% on the y-axis, measured at the robot's final position. The robot can detect objects
using infrared sensors reliably with error at about 0.87%, however, it cannot measure the object size precisely due
to various factors.
Keywords: Robot, omnidirectional, omniwheel, object detection, kinematics.
1. Introduction
Robot design and programming is a core subject in
engineering that has rich aspects to be explored, especially for
educational purposes. By self-developing robust but low-cost
robots, students and researchers can sharpen their knowledges
while exploring various novelty aspects in robotics. This is in
line with one of the opinions of the Team ABI Research:
"Flexibility and efficiency have become the main differen-
tiators in a system, in order to cope with volatile product
demand, seasonal peaks, and rising consumer shipping expec-
tations" [1].
Seeing that flexibility and efficiency are important compo-
nents in mobile robotics, a robot needs to be designed with
capability of moving easily to specific locations with wider
scope of space. For this reason, the robot needs to have good
mobility and maneuverability, which depends on its wheel
configuration.
One disadvantage of conventional wheels is that they are
limited in motion; thus, the robot cannot move sideways
without preliminary maneuvers. This problem can be over-
come with special omni-directional type wheels that allow the
robot to be driven in all directions.
In this paper, the process of building such a mobile robot is
described. In the future, this robot can also be equipped with a
robotic arm to create a mobile manipulator. For this purpose,
the robot needs to know the distance of the object from the
robot, along with the height and width information of the object
so that the manipulator arm can grip object correctly.
This paper is organized as follows. In section 2, the design
of the omni-directional mobile robot is described. In section 3,
we evaluate the performance of the robot. Finally, in section 4,
we conclude our work.
2. Research Methods
The following figure shows the logical connection between
core components of our mobile robot.
As shown in Figure 1, the Raspberry Pi acts as a controller
that will receive input from the user to determine the desti-
nation position and direction of motion of the robot.
Figure 1. Block diagram of an omni-directional robot system
The Raspberry Pi will send data to drive a DC motor, and
the encoder will read the wheel’s speed and make it as feedback
so that the Raspberry Pi can determine the robot's mileage.
Infrared sensor input is used to detect the distance from the
robot and the destination objects. The output from the camera
will be processed by Jetson Nano. Camera image processing is
carried out at Jetson Nano to read the height and width of the
detected object, then the reading data will be sent to the
Raspberry Pi. Both microcontrollers (Raspberry Pi and Jetson
Nano) are connected on the same network and use ROS for
communication between controllers.
Figure 2. Three wheeled robot's kinematics

Evert O. et al. / Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano / JIRAE, Vol. 4, No. 2, October 2019, pp. 57–62
58
Figure 2 shows the basic inverse kinematics calculation of
an omni-directional 3-wheel robot system [2]. And the final
equation can be written as follows:
(1)
With the description of several variables according to this
study:
α1 is the angle between the x-axis and wheel 1, 270O
α2 is the angle between the x axis and wheel 2, 30O
α3 is the angle between the x axis and wheel 3 ,150O
R is the distance of the wheel to the center point, which is 0.12
m.
r is the radius of the wheel, which is 0.0246 m
θ is the initial angle of the robot, which is 0
Figure 3. Odometry calculation
[ϕ1
ϕ2
ϕ3] = [2/3 0 1/3
0 1/√3 1/3
−1/3 −1/√3 1/3] [ x/r
y/r
θ.3L/r]
(2)
This equation is implemented in the program to determine
each rotation angle needed to input the intended x, y, θ position
[3]. The units x and y are in meters, while θ is in radians. Units
ϕ1, ϕ2, ϕ3 in radians.
Figure 4. Motor's speed control diagram
Figure 4 is a motor speed control design where the motor
speed’s value as results of inverse kinematics calculation will
be controlled with proportional control, using an encoder as
feedback that reads current speed.
Figure 5. Robot's movement diagam
To regulate the movement of the robot, the encoder acts as
feedback to estimate the movement of the robot. So once the
robot has reached the target position, then the motor will
immediately stop.
To detect the distance between the robot and the object , it
is used an infrared sensor. The SHARP GP2Y0A21 sensor
used can read a range of 10 - 80 cm. Calibration is performed
to determine the conversion equation from the sensor, the
calibration process is done using a black rectangular image
measuring 12 cm x 12 cm. The regression equation is:
y = 12320 x-1,073
(3)
Where ‘x’ is the reading number from the sensor and ‘y’ is the
reading value of the distance of the sensor to the object in
centimeters.
To detect the size of the object in front of the camera, an
object reading program is required by the camera. The camera
used is the Raspberry v2 camera module which will be
processed using openCV.
Figure 6. The process of object sizes measurements
To calculate the size of an object that is read, it needs to
determine the ratio between the pixels read in the image and
the distance of the camera to the object [4]. First it should be
'calibrated' with a reference object first. The reference object
must be known for its original size.The calibration result is:
y = 661.84x-1,002
(4)
Where ‘x’ is the distance that is read and ‘y’ is a dividing
constant to calculate the size of objects. Next for the
implementation of the program are: Actual size = pixel
distance / constant.
3. Result and Discussion
3.1 Motor’s characteristic test
Figure 7. Motor's velocity readings with encoder.
-10
10
30
50
70
90
110
130
150
-10 40 90 140 190
RPM
PWM
Motor's Velocity
A_load B_load C_load
A B C

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The graph shown in Figure 7 shows the difference in
motor speed at no load and under load conditions. After
obtaining characteristic data from each motor, a regression
equation can be made to convert the desired speed numbers to
the PWM numbers needed for each motor. Through the table
data above, the regression equation is obtained using Microsoft
Excel:
PWM_a = -4E-10y6 + 2E-07y5 - 4E-05y4 +
0.0043y3 - 0.2179y2 + 6.0869y - 42.92
y = 7E-06v4 - 0.0016v3 + 0.1341v2 - 4.0937v +
52.613
(5)
PWM_b = 9E-08y5 - 3E-05y4 + 0.0032y3 -
0.1748y2 + 4.2613y - 12.657
y = -3E-10v6 + 2E-07v5 - 4E-05v4 + 0.0049v3 -
0.2711v2 + 7.9921v - 65.117
(6)
PWM_c = 1E-07y5 - 3E-05y4 + 0.0036y3 -
0.1794y2 + 3.9339y - 5.0599
y = -3E-10v6 + 2E-07v5 - 5E-05v4 + 0.0052v3 -
0.2882v2 + 8.468v - 69.855
(7)
Equations (5), (6), (7) are regression equations where v is
the value of velocity required for each motor and PWM_a,
PWM_b, PWM_c is the PWM value given to the motor.
To be able to reach the speed of each motor more
accurately, a proportional controller is used, so the determi-
nation of the motor rotational speed is closer to the target. In
this system, using proportional controller of 0.7, obtained from
testing with trial and error methods.
3.2 Kinematics and odometry calculation test
Table 1. Final Position test
Input[cm]
End [cm]
Error[cm)
X
Y
Time[s]
X
Y
x
y
0
40
4
8
40
8
0
0
80
4
14
78
14
2
0
120
4
7
125
7
5
40
0
4
38
16
2
16
80
0
4
86
3
6
3
120
0
4
135
5
15
5
0
-40
4
-7
-35
7
5
0
-80
4
-20
-77
20
3
0
-120
4
-15
-116
15
4
-40
0
4
-38
-15
2
15
-80
0
4
-82
3
2
3
-120
0
4
-131
-20
11
20
Average Error
9.08
6.75
For diagonal robot movements, there is an average error of
9.51% on the x-axis and 8.12% on the y coordinate. All robots
are set to reach the target position within 4 seconds, except the
target position x = 120, y = 120 and x = -120, y = -120 where
the robot's movement time is set at 5 seconds because the motor
3’s speed has exceeded the maximum (for 4 seconds). Looking
at the data shown in Table 2, if the final position is small (in
this test x = ± 40, y = ± 40), it causes an error that tends to be
greater, because the required motor speed is below 20 rpm so
that proportional control has difficulty reaching that point.
Table 2. Final Position test (diagonal)
Input[cm]
End [cm]
Error [%]
X
Y
T [s]
X
Y
x
y
Avrg
40
40
4
35
38
12.5
5
8.7
80
80
4
73
65
8.7
18.7
13.7
120
120
5
125
102
4.1
15
9.5
0
0
0
40
-40
4
38
-37
5
7.5
6.2
80
-80
4
72
-75
10
6.2
8.1
120
-120
4
115
-110
4.1
8.3
6.2
0
0
0
-40
40
4
-33
37
17.5
7.5
12.5
-80
80
4
-70
74
12.5
7.5
10
-120
120
4
-110
111
8.3
7.5
7.91
0
0
0
-40
-40
4
-35
-36
12.5
10
11.2
-80
-80
4
-73
-78
8.75
2.5
5.6
-120
-120
6
-108
-118
10
1.67
5.83
Average error
9.51
8.12
8.81
Figure 8. Motor A Poropotional responses
Figure 9. Motor B Poropotional responses
Figure 10. Motor C Poropotional responses
0
50
100
150
12345678910 11 12 13 14 15 16 17 18 19
RPM
Motor A Proportional Responses
SP A1 A2 A3 A4
-10
40
90
140
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
RPM
Motor B Proportional Responses
SP B1 B2 B3 B4
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14
RPM
Motor C Proportional Responses
SP1 C1 C2 C3 C4

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60
Looking at the responses of the three motors used, several
points can be observed as follows:
1. All three motors find it difficult to achieve low speed
numbers, especially for speeds below 25 rpm, indicated by
the error number from the proportional control response
that is of high value.
2. The current control parameter setting, which is 0.7, is more
suitable and tends to be more stable at high speeds,
especially at speeds above 90 rpm. This is indicated by the
error number of the proportional control response which is
smaller.
3.3 Distance measurement with Infrared sensor
This test is carried out to determine the use of infrared
sensors used in reading the distance of an object with a robot.
The object used for testing is a rectangular radio with a height
of 8.6 cm and a width of 13.4 cm.
Table 3. Infrared sensor to measure distance
Test
Real Distance [cm]
Sensor reading [cm]
Error [%]
1
10
9.99
0.1
2
15
15.15
1
3
20
20.45
2.25
4
25
25.01
0.04
5
30
30.51
1.7
6
35
35.23
0.65
7
40
40.65
1.62
8
45
45.15
0.33
9
50
50.5
1
10
55
55.12
0.21
11
60
60.52
0.86
12
65
63.8
1.84
13
70
70.08
0.11
14
75
76.8
2.4
15
80
82.6
3.25
Average Error
0.87
In previous experiments, objects with a flat surface were
used. Then an experiment was carried out using a tubular object
whose surface was not flat, dan change the position of the
object. The test method remained the same as before. This is to
determine the effect of the object’s surface dan position on
distance reading with an infrared sensor.
Table 4. Distance measurement on different object’s surfaces
Distance
[cm]
Flat Surfaces
Curved surface
Distance
[cm]
Error
[%]
Distance
[cm]
Error
[%]
20
20.45
2.25
18.12
9.40
30
30.51
1.70
28.16
6.13
40
38.45
3.87
36.4
9.00
50
49.55
0.90
52.5
5.00
60
61.35
2.25
62.8
4.67
70
63.8
8.86
78.1
11.57
Table 5. Distance measurement on different object’s position
Distance
[cm]
Parallel
Tilted 15°
Distance
[cm]
Error
[%]
Distance
[cm]
Error
[%]
30
30.58
1.9
31.73
5.77
40
41.48
3.7
30.42
23.95
50
50.85
1.70
39.1
21.80
60
61.12
1.87
42.8
28.67
70
70.15
0.21
34.5
50.71
As the result, the curved surface and the tilted object do affect
the reading by infrared sensor, the curved or tilted object
potentially has a greater error number.
3.4 Object size measurement using pi camera and Jetson
Nano
Following the test of distance measurement, object
dimension calculation is also performed. For this, the Nvidia
Jetson Nano is used for the image processing. Due to the
physical constraints, the system cannot detect more than one
object and the object being measured must have height above
7.5 cm (height of the sensor to the ground surface).
For testing, we used various mundane objects such as a
radio, a food box, a flashlight, a DVD box, a bottle, and a lump
of sewing threads (see Figure 11).
Figure 11. Various common objects for the object size
measurement experiment.
Here are the measurement results.
Table 6. Radio’s size measurement
Distance
[cm]
Real size [cm]
Camera reading [cm]
Error[%]
width
height
width
length
width
Height
20
13.4
8.6
13.63
8.57
1.72
0.35
25
13.6
8.66
1.49
0.70
30
13.77
8.65
2.76
0.58
35
13.51
8.4
0.82
2.33
40
13.98
8.61
4.33
0.12
45
14.33
8.57
6.94
0.35
50
13.88
8.42
3.58
2.09
55
14.08
8.37
5.07
2.67
60
13.2
8.3
1.49
3.49
65
13.38
8.47
0.15
1.51
70
13.7
8.4
2.24
2.33
Rata-Rata
13.73
8.49
2.78
1.50
Table 7. Food box ‘s size measurement
Distance
[cm]
Real size [cm]
Camera reading [cm]
Error[%]
width
height
width
height
width
height
20
11
16
13.2
17.22
20.00
7.62
30
12.55
16.66
14.09
4.13
40
12.32
15.55
12.00
2.81
50
11.7
15.48
6.36
3.25
60
11.64
16.65
5.82
4.06
70
13.1
15.88
19.09
0.75
Rata-Rata
12.42
16.24
12.89
3.77

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61
As a note, the food box measurements have an average
error of 12.89% because, at distance of 20cm, the object covers
all the camera's reading screens so the size reading is invalid.
The average error for the height is 3.77%.
Table 8. Flashlight’s size measurement
Distance
[cm]
Real size
[cm]
Camera reading
[cm]
Error
[%]
width
height
width
height
width
height
20
12.55
24.88
14.4
12.1
14.74
51.37
30
6.08
4.97
51.55
80.02
40
12.77
24.44
1.75
1.77
50
12.31
23.52
1.91
5.47
60
10.83
19.67
13.71
20.94
70
11.67
21.44
7.01
13.83
Rata-Rata
11.34
17.69
15.11
28.90
For reading the size of a round flashlight, the average
number of errors is 15.11% and 28.9%, because at distance of
20 cm and 30 cm, the size of the object is too large for the
camera reading.
Table 9. DVD box’s size measurement
Distance
[cm]
Real size
[cm]
Camera reading
[cm]
Error
[%]
width
height
width
height
width
height
20
38.85
24.85
4.75
2.2
87.77
91.15
30
4.56
2.15
88.26
91.35
40
5.88
3.55
84.86
85.71
50
42.12
25.12
8.42
1.09
60
39.85
24.88
2.57
0.12
70
39.25
23.76
1.03
4.39
Rata-Rata
22.74
13.61
45.49
45.63
Measurement using a DVD box is only effective at
distances greater than 50 cm, because at closer distances, the
camera screen is not enough to capture the entire object.
Table 10. The balm bottle’s size measurement
Distance
[cm]
Real size [cm]
Camera reading
[cm]
Error[%]
width
height
width
length
width
Height
20
5.1
3.6
11.8
12.3
131.37
241.6
30
8.44
10.01
65.49
178.
40
7.22
8.02
41.57
122.7
50
5.41
6.78
6.08
88.33
60
6.35
9.5
24.51
163.8
70
21.7
10.18
325.4
182.7
Rata-Rata
10.15
9.47
99.08
162.9
Measurement of the size of the balsam bottle object cannot
be carried out, because the height of the object is only 3.6 cm,
while the placement of the sensor is 7 cm from the ground
surface. So it cannot be detected by an infrared sensor, causing
the absence of distance readings and calculations for
measurements with the camera cannot be processed correctly.
Table 11. A lump of sewing thread ‘s size measurement
Distance
[cm]
Real size
[cm]
Camera reading
[cm]
Error
[%]
width
height
width
height
width
height
20
7.5
11.5
6.40
10.66
14.67
7.30
30
7.53
11.2
0.40
2.61
40
7.45
10.88
0.67
5.39
50
7.21
10.34
3.87
10.09
60
6.85
9.44
8.67
17.91
70
7.47
10.87
0.40
5.48
Rata-Rata
7.15
10.57
4.78
8.13
From the above experiments, we observe the following:
• The system cannot detect objects with the same back-
ground color as the object.
• The shape of objects that can be measured is square,
rectangular, round, tube. Beyond this form, the reading
becomes less accurate.
• To be able to read its size, objects must all enter the camera
reading frame. If it is too close to the camera, it might pro-
duce invalid readings.
Figure 12 shows the sketch and the physical appearance of
our robot.
Figure 12. The sketch and the real robot.
4. Conclusion
This paper describes the process of designing a 3-wheel
omni-directional mobile robot that is controlled using a
Raspberry Pi for its direction and can detect simple objects in
front of it. Inverse kinematics was used to determine the
direction and speed of each motor, and the odometry was used
to estimate the movement of the robot's position. From these
two calculations, the robot can move and stop at the position
inputted by the user with an error rate of 9.51% on the x-axis
and 8.12% on the y-axis for the robot's final position. This is
influenced by the speed of the robot to reach that point, the
proportional parameters used, and the distance between the
final position and the initial position. Reading the distance of
an object using an infrared sensor, we got an average error of
0.87%. The factors that influence this reading accuracy are the
angle of placement of the object to the sensor and the surface
of the object being measured. For the object detection, the
OpenCV library was used that utilizes the color contrast of

Evert O. et al. / Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano / JIRAE, Vol. 4, No. 2, October 2019, pp. 57–62
62
objects against the background to detect an object. From the
results of testing the size of objects using 6 objects of different
sizes, this method has the ability to read object sizes in front of
the robot with an average error of 30.02% for length readings
and 41.8% for object height readings. This detection accuracy
was influenced by object size, object distance from the camera,
and object detection methods used. For future work, we plan to
combine this omni-directional robot platform with a mani-
pulator to create an adaptive mobile manipulator.
Acknowledgements
This work is supported partially by Petra Christian Univer-
sity through the Special Grant (No.009/SP2H/LT-MULTI/
LPPM-UKP/III/2020 and No.016/SP2H/LT-MULTI/LPPM-
UKP/III/2020).
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