Journal of Robotics, Networking and Artificial Life
Vol. 8(1); June (2021), pp. 66–71
DOI: https://doi.org/10.2991/jrnal.k.210521.015; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Verification of Robustness against Noise and Moving Speedin Self-localization Method for Soccer Robot
Yuehang Ma1,*, Kaori Watanabe1, Hitoshi Kono2, Hidekazu Suzuki2
Graduate School of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan
Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan

12ARTICLE INFOABSTRACT

Article History

The main focus of the RoboCup competitions is the game of football (soccer), where the research goals concern cooperative
multi-robot and multi-agent systems in dynamic environments. In RoboCup, self-localization techniques are crucial for robots
to estimate their own position as well as the position of the goal and other robots before deciding on a strategy. This paper
presents a self-localization technique using an omnidirectional camera for an autonomous soccer robot. We propose a selflocalization method that uses the information on the white lines of the soccer field to recognize the robot position by optimizing
the fitness function using a genetic algorithm. Through experiments we verify the robustness of the proposed method against
noise and moving speed.

Received 13 November 2020
Accepted 22 April 2021
Keywords
Robustness against noise
robustness against moving speed
self-localization
RoboCup Middle Size League
soccer robot
genetic algorithm© 2021 The Authors. Published by Atlantis Press B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
The landmark project RoboCup is a well-known international
robotics challenge [1]. This project promotes research on Artificial
Intelligence (AI) and robotics with the goal of “creating a team ofautonomous humanoid robots that can beat the world-championsoccer team” by 2050 [2]. Various technical challenges must be
overcome to achieve this goal. Self-localization—which enables
a robot to recognize the real-world environment in real time—isa key function that determines the outcome in any competition
because it serves as the starting point for the strategic coordinatedmovements that follow.
Figure 1 shows the RoboCup Middle Size League (MSL) soccer
field, which has the widest field size (12 × 18 m). Many selflocalization methods using the shape of the white lines drawn
on the field have been proposed, including a method using theHough transform [3] and a method based on error minimization [4]. However, the Hough transform-based method requires
a long processing time because calculations other than estimating the self-localization are generated. Error minimization is alocal search method, so the initial position needs to be assumed.
As a result of these shortcomings, a method using Monte Carlolocalization (MCL) [5,6] with a particle filter has been proposed
and implemented by many RoboCup teams. However, improving localization accuracy leads to increased processing time.

Corresponding author. Email: yuehang.ma@gmail.com*Furthermore, MCL is known to be relatively vulnerable to thekidnapping problem [7].
In this paper, we describe a real-time self-localization methodthat applies a Genetic Algorithm (GA) [8] for the RoboCup MSL.
We estimate the self-position using information on the image,­environment, and field. A search space is formed by model-basedmatching [9], which collates a search model based on the shape of
the white lines (obtained from omnidirectional images) and known
field data. A GA is then used for optimization processing. Our proposed method enables the robot’s self-position to be identified inreal time.
The competition environment is a dynamic system where multiple robots move at high speed, and image noise is generated
due to the mutual occlusion of multiple robots. Moreover,the image changes caused by the movements of the robot also
affect self-localization, so the robustness of the self-localization
method must be evaluated as well. Thus, we conducted experiments to verify the robustness of our method against noise andmoving speed.

2. SELF-LOCALIZATION USING­OMNIDIRECTIONAL CAMERA
We use a white line on the MSL field for self-localization. Our
proposed self-localization method generates the search space­based on a model-based matching using white line information.
The method recognizes the robot position by optimizing the f­ itness

Y. Ma et al. / Journal of Robotics, Networking and Artificial Life 8(1) 66–71

Figure 1 | RoboCup MSL soccer field.

function, which is the maximum value at the correct robot ­position.
A GA is utilized to optimize the fitness function.

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Figure 2 | Hardware of vision system.

abcd2.1. Hardware of Vision System
The omnidirectional vision system of our robot consists of a camera(FLIR, Flea3), a varifocal lens (Vstone), and a hyperboloidal mirror(Vstone). We developed the vision system, shown in Figure 2, for
the RoboCup MSL robot by combining the above elements. The
image captured by this vision system is shown in Figure 3a. The
image size and frame rate are 512 × 512 pixels and 30 frame per
millisecond (fps), respectively.

2.2. Searching Model
Figure 3 shows the process of creating the search model for theproposed method. First, the detection image of the white line isneeded to make the searching model. We obtain the white detection image by applying the converting method of color space from
RGB to HSV and to YUV as in Figure 3b. Then we generate thefield information by orthogonalizing the white line information,
shown in Figure 3c. We determine the searching model by thinning down the field information based on the white lines as inFigure 3d. Finally, we use the thinned model as the searching model
for self-localization.

Figure 3 | Process of creating search model. (a) Original image
from camera. (b) White detection image. (c) Orthogonalized image.
(d) Search model.

2.3. Model-based Matching
The proposed self-localization method generates the search spaceby model-based matching between geometric informationof the white lines on the MSL field and the above-mentioned
searching model shown in Figure 4. The evaluation function
F (e ) e =  x, y ,q of the model-based matching is given asfollows:{}F (e ) = å p(r) (1)		  Figure 4 | Model-based matching.

the task of detecting robot position is converted to a search task ofe such that F(e ) is maximized.

rÎS (e )Here, the movement of the searching model S in the matchingarea is expressed as S(e ). And, if the pixel value of field imagecorresponding to the area of the moving model is expressed asp(r) r = (x, y ), r ∈ S(e ) . The fitness function F(e ) obtains themaximum value when the position of the searching model corresponds to the correct position of the robot on the MSL field. Then,{}

2.4. Genetic Algorithm
In the proposed self-localization method, we implement a GA todetermine the maximum value of the fitness function F(e ) . A GA
is an example of an artificial intelligence program and is well known
as a parallel search and optimization process that mimics natural

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Y. Ma et al. / Journal of Robotics, Networking and Artificial Life 8(1) 66–71

selection and evolution. The proposed method utilizes an elitist
model of a GA that preserves the best individual in the population
at every generation. The method also utilizes genetic coding using
gray code, roulette selection, and one-point crossover. The parameters of the GA process were determined in previous experiments.

2.5. Verification Experiment
We performed a self-localization experiment to determine theerror between the correct position and detected position on theMSL field to verify the effectiveness of the proposed method. In
the verification experiment, the resulting maximum error was
36.1 cm and the average error was 12.8 cm. These errors are small
compared to the field size, indicating that the self-localization
could be detected with sufficiently high accuracy.
We conducted an additional verification experiment to confirm thesmoothness of the recognized position in an actual environment,
in which the robot moved along a test path on the field shownon the left side of Figure 5. The results of the self-localization are
shown on the right side of the figure, where the black dots represent
the detected position at each camera frame. We also recorded thex, y-coordinates of the detected position at each frame number on
the test path, shown in Figure 6. The results show that the selflocalization result did not deviate significantly, and the robot’s
coordinates of its detected position are continuous.

conducted additional experiments to verify the robustness of theproposed method against varying amounts of noise.

3.1. Experiment
In a real environment, a variety of noises may appear in the image,so qualitative experiments cannot be performed. Here we perform
verification experiments by adding artificial noise.
We conducted the experiments at the seven locations on the MSL
field indicated in Figure 7. These seven locations are characteristic
points on the field. Taking the center of the panoramic image as thecenter of the circle, we set the fan-shaped area with a center angleof 30° as noise, shown in Figure 8. By changing the position andamount of noise, we can simulate different noise conditions andverify the results of self-localization. In the field of research using
omnidirectional cameras, the verification method using fan shapenoise as described above is often used [10].
Figure 8 shows the changes in the amount of noise at the position Din Figure 7, and the changes in angle are shown in Figure 9. The
image in the left of Figure 8 is the original image without any noise.
We verified the error in the self-localization results measured at theangle and number of various noise at each coordinate location onthe MSL field. The results of each measurement position are summarized in Table 1. For the position deviation caused by the noise

3. VERIFICATION OF ROBUSTNESSAGAINST NOISE
The accuracy measured thus far is the result of an ideal environment
in which there are no other robots on the soccer field. However, ina real game environment, up to 10 robots may exist on the samesoccer field. In addition to the robots, people such as the referee
and the line referee are on the field at the same time. The robot
may not be able to recognize its own position correctly because
the people and the other robots become occlusions. Therefore, weFigure 7 | Experiment location.

Figure 5 | Result of self-localization on test path.

Figure 6 | Coordinates of detected position at each frame number.

Figure 8 | Varying amounts of noise.

Figure 9 | Noise at different angles.

Y. Ma et al. / Journal of Robotics, Networking and Artificial Life 8(1) 66–71

69

Table 1 | Comparison of detection errorError [cm]Point

Noise numRate12

34

5678

910A

28.3
0.00%
14.1
0.00%
10.0
0.00%
20.0
0.00%
22.4
0.00%
30.0
0.00%
10.0
0.00%

28.3
0.00%
14.1
0.00%
14.1
0.00%
14.1
0.00%
22.4
0.00%
30.0
0.00%
10.0
0.00%

28.3
0.00%
14.1
0.00%
0.0
0.00%
10.0
0.00%
22.4
0.00%
41.2
0.00%
40.0
0.00%

28.3
0.00%
14.1
0.00%
10.0
0.00%
20.0
0.00%
58.3
33.33%
14.1
0.00%
120.0
33.33%

150.3
8.33%
31.6
0.00%
14.1
0.00%
20.0
0.00%
1650.5
8.33%
20.0
0.00%
40.0
0.00%

22.4
0.00%
14.1
0.00%
10.0
0.00%
10.0
0.00%
22.4
0.00%
40.0
0.00%
10.0
0.00%

72.8
8.33%
41.2
0.00%
14.1
0.00%
1310.3
8.33%
22.4
0.00%
20.0
0.00%
820.1
33.33%

1510.066.67%
22.4
0.00%
10.0
0.00%
429.5
66.67%
31.6
0.00%
40.0
0.00%
900.1
66.67%

28.3
0.00%
648.5
50.00%
14.1
0.00%
676.8
75.00%
31.6
0.00%
41.2
0.00%
890.1
75.00%

131.5
16.67%
1164.8100.00%10.0
0.00%
1207.8100.00%
31.6
0.00%
370.1
66.67%
900.1
66.67%B
CD
E
F
G

at different angles, we took the average value combined with thedeviation value caused by the amount of noise. The bold text in thetable indicates that the error value of the self-localization exceeds
the width of a robot (50 cm), meaning that the robot has lost its
exact position.

3.2. Experiment Results
As shown in the results in Table 1, when the amount of noise
increases, the self-localization error also increases. Generally,
when the number of noises is greater than 7, it will cause large
errors except for special locations, i.e., where there are more than
two features in the image obtained by the robot (Locations Band F) and when the robot is in the center circle (Location C),
shown in Figures 10 and 11, respectively. Locations E and G also
have errors when the noise is low. This may be because the robot
was on the vertical white line, and the noise obscured all of thewhite line information, shown in Figure 12. Overall, the results
show that when the number of noise is less than 3, the selflocalization error is very small, and when the noise increases to 4,
the maximum self-localization error is 120 cm. In a real game environment, the probability of more than four noises existing at thesame time is very low.

4. VERIFICATION OF ROBUSTNESSAGAINST MOVING SPEED
Genetic algorithm searching obtains the optimum value of fitness function by changing and converging the generation continuously against a fixed search space such as a static image.
However, when processing a dynamic image, the target positionchanges at every moment. The target position ϕp in the imagechanges and is represented by ϕp(t) as the time function. Realtime processing, which calculates within 33 ms per frame is very
important for recognizing target position ϕp(t) without delay. In
this research, the proposed method outputs the target position
obtained by the maximum value of the genetic fitness function
at every frame change.

Figure 10 | Eight noise patterns at position B.

Figure 11 | 10 noise patterns at position C.

Figure 12 | Five noise patterns at position E.

4.1. Inheritance of Genetic Information
An example of the output fitness value from the general GA searchis shown in Figure 13, where the vertical line represents the timingof frame-to-frame switching. In this figure, the fitness value drops
significantly because the best individual is reset with every framechange and the search restarts from a very low initial fitness value.
The proposed method uses the genetic information of the previous frame as the initial genetic information of the next frame atevery frame change. The resulting output of the proposed method
is shown in Figure 14. As seen in the figure, a high fitness value ismaintained by inheriting the individual information.

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70

4.2. Experiment Results
To verify the method’s robustness to moving speed, we conducted
experiments in which we measured the robot’s fitness value andself-localization accuracy at different robot speeds. The robot
moves forward on test paths A–C, shown in Figure 15, at a speedof 1–3 m/s.
The result of test path A is shown in Figures 16 and 17. In
Figure 16, the vertical axis represents the fitness value and thehorizontal axis represents the distance from the starting point. To
verify the relationship between accuracy and moving speed, wepicked three areas surrounded by rectangles in Figure 16, which
are rearranged in Figure 17. In Figure 17, I–III are the respectivepositions and frames at each speed.
Here, the position error is represented with a bar graph. As shownin Figure 17, the self-localization error does not change greatly as
moving speed changes. The maximum error is about 13 cm, which

Figure 16 | Results of fitness value at each moving speed (test path A).

Figure 13 | Output of fitness value from general GA search.

Figure 14 | Output of fitness value using proposed method.

Figure 17 | Self-localization error at each moving speed (test path A).

Figure 15 | Test paths A–C.

is small enough compared to the size of the MSL field. Test paths
B and C yielded similar results to test path A; the maximum errors
were 9.9 and 10.12 cm for B and C, respectively. This shows that theproposed method is robust against changes in the robot’s moving
speed within 1–3 m/s.

Y. Ma et al. / Journal of Robotics, Networking and Artificial Life 8(1) 66–71

5. CONCLUSION
We proposed a self-localization method that generates thesearch space based on a model-based matching with the whiteline information of the RoboCup MSL soccer field. The method
recognizes the robot position by optimizing the fitness function
using a genetic algorithm. We verified the effectiveness and accuracy of the proposed self-localization method using the GA inan actual environment. Furthermore, we experimentally evaluated the robustness of the proposed method against noise andmoving speed and confirmed that it is sufficient for RoboCup
soccer games.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

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AUTHORS INTRODUCTION
Mr. Yuehang Ma

Ms. Kaori Watanabe
He received his B.S. and M.S. degree inEngineering in 2018 and 2020 from theFaculty of Engineering, Tokyo Polytechnic
University in Japan. He is acquiring thePhD in Tokyo Polytechnic University.

She received her Master’s degree from theDepartment of System Electronics andInformation Science, Tokyo Polytechnic
University, Japan in 2013. She is currently a Doctoral course student in Tokyo
Polytechnic University, Japan.

Dr. Hitoshi Kono
He received his B.S., M.S. and PhD degrees
from Tokyo Denki University, Japan, in2008, 2010 and 2015 respectively. He joined
Fujitsu Limited from 2010 to 2011. In 2015,
he joined the Department of Precision
Engineering, The University of Tokyo as a
Project Researcher. Since 2017, he has beenan Assistant Professor in the Departmentof Electronics and Mechatronics, Tokyo Polytechnic University.
His research interests include disaster response robotics, transfer
reinforcement learning and it’s applications. He is a member ofRSJ, JSAI, and IEEE R&A.

Dr. Hidekazu Suzuki
He is an Associate Professor of Facultyof Engineering at Tokyo Polytechnic
University in Japan. He graduatedfrom the Department of MechanicalEngineering, Fukui University, in 2000.
He received his D. Eng. degree in System
Design Engineering from Fukui University
in 2005. His research interest is Robotics.