Journal of Robotics, Networking and Artificial Life
Vol. 7(2); September (2020), pp. 98–102
DOI: https://doi.org/10.2991/jrnal.k.200528.005; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Real-time Self-localization using Model-based Matching forAutonomous Robot of RoboCup MSL
Kaori Watanabe1,*, Yuehang Ma1, 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, Japan12

ARTICLE INFOABSTRACT

Article History

This paper presents a self-localization technique using an omni-directional camera for an autonomous soccer robot. The position
information of the robot is important for strategic behavior and cooperative operation. Therefore, we have proposed the selflocalization method which generates the searching space based on a model-based matching with white line information of soccer
field, and which recognizes the robot position by optimizing the fitness function using genetic algorithm.

Received 03 December 2019
Accepted 20 May 2020
Keywords
Self-localization
RoboCup middle size league
soccer robot
genetic algorithm© 2020 The Authors. Published by Atlantis Press SARL.
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 main focus of the RoboCup competitions is the game offootball/soccer, where the research goals concern cooperative multirobot and multi-agent systems in dynamic adversarial environments[1]. In the field of RoboCup, self-localization technique is important toestimate own position including goal and other robot positions and todecide strategy. Basically, we estimate the self-position with the image
information, the environment information and the field information.
In this paper, we describe a real-time self-localization method thatapplies a genetic algorithm (GA) for the RoboCup middle size league(MSL, Figure 1), which has the widest field size (12 × 18 m). In Section 2
is hardware structure, which is overview of our past robot, driving
module, ball handling module and kicking module. In Section 3,vision systems, which are omni-directional camera module andself-localization method are described. And we verify the effectivenessof the proposed self-localization method using GA in Section 4.

2. HARDWARE STRUCTURE
We have restructured the most part of hardware structure every year
since our team was founded in 2008. We developed the robot based onrecent MSL concepts that are high torque driving module, ball handling module, electrical kicking module using solenoid and USB3.0
camera system. We have called the platform as “Mugen” series [2],and “Mugen III (M-III)” shown in Figure 2, which is improved andbased on “Mugen” model, participated in RoboCup Japan Open 2017.
This high torque driving module equips 4-wheels and each wheel isomni-directional wheel. We use ball handling module for rotating
Corresponding author. Email: watanabe.kougei.karakuri@gmail.com*a ball with natural direction. This kicking module can shoot a ballby solenoid.

3. VISION SYSTEM
3.1. Hardware of Vision System
The omni-directional vision system of our robot is consisted of thecamera (FLIR® Systems, Inc. Flea3 [3]), a varifocal lens (Vstone) and a
hyperboloidal mirror (Vstone). We developed vision system shownin Figure 2 for RoboCup MSL robot by combining with aboveelements. The image captured by this vision system is shown inFigure 3a, and the image size and frame rate are 512 × 512 (pixels)
and 30 (fps) respectively.

3.2. Self-localization
We use a white line of MSL field for self-localization. We have proposed the self-localization method which generates the searching
space based on a model-based matching using white line information, and which recognizes the robot position by optimizing thefitness function which has the correct robot position as the maximum value of the function. And this proposed self-localization
method uses Genetic Algorithm (GA) [4] for optimization of thefitness function.

3.2.1. Searching model
Figure 3 shows the process of making the searching model of the
proposed method. At first, we need the detection image of theK. Watanabe et al. / Journal of Robotics, Networking and Artificial Life 7(2) 98–102

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on white line like Figure 3d. Therefore we use thinned model assearching model for the self-localization.

3.2.2. Model-based matching

Figure 1 | RoboCup MSL.

The proposed self-localization method generates the searching
space by model-based matching between geometric informationof the white line in the MSL field and above-mentioned searchingmodel. Here, the matching area Ω is MSL field, and white line in
the field is expressed as set of pixels like Figure 4. Therefore, thematching area Ω is defined by following Equation (1).
W = {r = (x , y )|0 £ x £ x max , 0 £ y £ ymax } (1)Here, xmax and ymax are positive constant values corresponding to
the image plane boundaries in x and y directions. Now let us consider the set of pixels with above-mentioned searching model shapeinside Ω as moving model Sf like Figure 5. The position r = (x , y )and orientation q of moving model is represented as f = [ x , y , q]T .
Then the extent of the existence of the moving model is given by:{}

G = f = (x , y , q) Î R 3|r Î W, - p < q £ p (2)The movement of the moving model Sf in the matching area isexpressed as S f (f). And, if the pixel value of field image corresponding to the area of the moving model is expressed as p(r),r Î S f (f), then the evaluation function F(f) of the moving modelis given as follows.
Figure 2 | “Mugen III’ mechanical layout.

abcd
Figure 4 | Matching area.

Figure 3 | Process of making search model. (a) Original image fromcamera. (b) White detection image. (c) Orthogonalized image. (d) Searchmodel.

white line for making the searching model based on the white line.
We obtain the white detection image by employing the converting
method of color space from RGB to HSV and to YUV like Figure 3b.
And we generate the field information by orthogonalizing thewhite line information like Figure 3c. Moreover, we determine thesearching model by thinning down the field information based

Figure 5 | Moving model.

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F (f) =å p(r) (3)rÎS f (f)The fitness function F(f) obtains the maximum value when theposition and orientation of the moving model corresponds to thecorrect position and orientation that robot exist in the MSL field.
Then, the problem of detection of robot position and orientation ischanged to the problem of searching f that maximizes F(f) [5,6],
and can be expressed as:
find f to maximize F(f) subject to f Î G (4)

The calculation result of whole matching area shown in Figure 6
shows Figure 7. In this Figure 7, the vertical axis represents the fitness value, and the horizontal axes represent the field plane. Here,
we select only one depending on the value of an electric compass,because two maximum value exist in the function value caused byrevolution symmetry of geometric characters of the MSL field.

Figure 8 | Error of the self-localization using whole searching.

3.2.3. Verification of model-based matching
We performed an experiment to verify the effectiveness of the proposed model-based matching. Figure 8 shows the error between
the correct position and the detected position in the quarter area ofthe MSL field at an interval of 1 m. The severity of the error amountFigure 9 | GA’s process.

is indicated by the brightness of the gray scale. The average error
was 12.7 (cm), so the self-localization method is accurate enoughfor playing soccer.
However, the processing time of whole searching for matching areaΩ is 3000 (ms). So, we have needed the efficient searching method
for real-time self-localization.
Figure 6 | Model matching.

4. REAL-TIME SELF-LOCALIZATIONMETHOD4.1. Genetic Algorithm

Figure 7 | Calculation result of the fitness function.

In the proposed self-localization method, we use GA for searching
the maximum value of the fitness function F(f). A GA is an example of an artificial intelligence program and is well known as a parallel search and optimization process that mimics natural selectionand evolution (Figure 9). In the proposed method, an elitist modelof a GA that preserves the best individual in the population at every
generation is utilized and genetic coding using gray code, rouletteselection and one-point crossover are used. And, the parameters of
the GA process are determined by previous experiments. Figure 10
shows the result of the convergence of GA in case of self-localization
experiment using actual image that the robot captured at voluntary
position. In this figure, the vertical axis represents fitness value of

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fitness function, and the horizontal axis represents the generation
number of GA. The GA converged the maximum value, whichmeans current position of robot in the MSL field, at about 60thgeneration in real-time.

4.2. Verification Experiment using GA
We performed the self-localization experiment to verify theeffectiveness of the proposed method using GA. Figure 11 shows
the result of the verification experiment that checked the selflocalization error using GA as same as Figure 8. Average errorof this experiment was 12.9 (cm), and the accuracy of the selflocalization by the proposed method is roughly the same as theabove-mentioned whole searching.
Moreover, we performed the experiment using five players shownin Figure 12 on the assumption of MSL game. Laptop PC displays
the position sent by each robot in real-time, and the positions offive players described in the result image corresponded with theactual position of the robots.

Figure 12 | Verification experiment in the real world.

5. CONCLUSION
In this paper, we have proposed the self-localization method which
generates the searching space based on a model-based matching with white line information of RoboCup MSL soccer field,and which recognizes the robot position by optimizing the fitness function using genetic algorithm. Moreover, we verified theeffectiveness of the proposed self-localization method using GA.
Furthermore, we confirmed that the accuracy of the self-localizationby the proposed method is enough to play soccer.

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

ACKNOWLEDGMENT
This work was supported by “FY2016 MEXT Private University
Research Branding Project”.
Figure 10 | Result of the convergence of GA.

Figure 11 | Error of the self-localization using GA.

REFERENCES[1] RoboCup Federation official website, Available from: https://www.robocup.org/.
[2] Y. Yasohara, H. Suzuki, Development of omni-directional mobile
mechanism for RoboCup MSL, Proceedings of 31st Fuzzy System
Symposium, Japan Society for Fuzzy Theory and Intelligent
Informatics, 2015, pp. 151–152 (in Japanese).
[3] FLIR, Flea3(USB3 Vision Camera), Available from: https://www.
flir.com/products/flea3-usb3/.
[4] D.E. Goldberg, Genetic algorithms in search, optimization andmachine learning, Addison-Wesley, Reading, MA, USA, 1989.
[5] T. Yoshida, H. Suzuki, Real-time self-localization for RoboCup
middle-size-league, Proceedings of 32nd Fuzzy SystemSymposium, Japan Society for Fuzzy Theory and Intelligent
Informatics, 2016, pp. 397–398 (in Japanese).
[6] H. Suzuki, M. Minami, Visual servoing to catch fish using
global/local GA search, IEEE/ASME Trans. Mechatron. 10(2005), 352–357.

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AUTHORS INTRODUCTION
Ms. Kaori Watanabe
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.

Mr. Yuehang Ma
He received his B.S. degree in Engineeringin 2018 from the Faculty of Engineering,Tokyo Polytechnic University in Japan. He
is acquiring the M.E. in Tokyo Polytechnic
University.

Dr. Hidekazu Suzuki
He is an Associate Professor of Facultyof Engineering at Tokyo Polytechnic
University in Japan. He graduated from theDepartment of Mechanical Engineering,
Fukui University, in 2000. He received his
D. Eng. degree in System Design Engineeringfrom Fukui University in 2005. His researchinterest is Robotics.