KIKS Extended Team Description
for RoboCup 2023
Daichi Miyajima, Kosei Naito, Hayato Mitsuda, Kazuaki Harada,
Mizuki Nonoyama, Ryo Shirai, Futa Sato, Ryuto Tanaka, Yota Dori, andToko Sugiura1
National Institute of Technology, Toyota College, 2-1 Eisei-cho, Toyota, Aichi
471-8525, Japansugiura.toko@toyota.kosen-ac.jp,
URL: www.ee.toyota-ct.ac.jp/~sugi/RoboCup.html

Abstract. This paper presents the robot and software system of SSL
team KIKS, which is planned to participate in the RoboCup 2023 Bordeaux. In this ETDP, we mainly present our study on the improvementof the dribbling bar, dribbling mechanism and kicker circuit, and prediction of the opponent robot’s behavior using machine learning. We
describe the weaknesses of the previous generation robot and the improvements to overcome them, including software upgrades. In addition,
we overview the study of improving robot position control with on-board
cameras and encoders to correspond to Vision-Blackout.
Keywords: RoboCup, small size league, autonomous robot, global vision, engineering education

1

Introduction

Team KIKS has continued to work toward developing higher performance hardware and smarter AI systems. This year, we studied the improvement and extension of ball control performance on hardware, especially on the improvementof the dribbling system. We also experimented with motion control based on the
robot’s own judgment, using a local vision system and encoders corresponding to
Vision Blackout, based on the Jetson Nano introduced last year. In the software,
we tried to predict the opponent robot’s behavior using machine learning. The
results of the experiments are described below.

2

Mechanical system

In RoboCup Small Size League, the dribbling system is very important to realize
the strategy of AI, recently. This is because enhanced ball possession and therealization of curved shots lead to an expansion of the strategy. In 2022, we
improved the traditional dribbling bar to increase ball keeping ability by simply
splitting in the center of the bar[1]. The improvement was very simple and could

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Daichi Miyajima et al.

be done in a very short time. As a result, the ball keeping ability of the robot was
improved, but its performance is still not suﬃcient and several problems remain
to be solved. In this section, we describe the trial experiments we performed tosolve those problems and the results of the veriﬁcation.
2.1

Dribbling bar

The dribble bar [1] proposed in the ETDP 2022 is made by cutting a rubber
pipe of uniform thickness. Due to the relatively hard material, when the ball
was caught at the end of the bar, it bounced and could not hold the ball. In
addition, the ball could not be moved to the center of the bar, and suﬃcientrotational and holding force could not be given to the ball. If the ball could
be moved to the center of the dribbling bar in a short time by improving theshape and material of the dribbling mechanism and bar, it would be eﬀectivein enhancing the ball holding force and expanding the variety of strategies. A
typical dribbler that moves the ball to the center is a spiral structure [2], [3]. We
introduced it in 2020, but its eﬀectiveness was not suﬃcient due to the individual
robot diﬀerences and spiral accuracy of the robots, and it was not applied to all
robots because of its high fabrication diﬃculty. Therefore, we aim to develop a
dribble bar that is easier to fabricate than the spiral structure and more eﬀective
in moving the dribble to the center. In this section, we focus on the material andshape of the dribble bar, and make prototypes of diﬀerent types of dribble bars.
The purpose of this section is to experimentally evaluate the lateral movement
performance and ball possession of the ball in contact with the dribble bar, andto provide guidelines for the development of more eﬀective dribble bars.

(a) Bar1(b) Bar2(c) Bar3(d) Bar4(e) Bar5(f) Bar6(g) Bar7(h) Bar8 (Super soft gel)Fig. 1. Dribbling bar used in the experiment

Figure.1(a)-(h) show the newly designed and fabricated dribble bars. (a)-(f)show a metal bar as a core around which a string of rubber or plastic is wrapped,

KIKS Extended Team Description for RoboCup 2023

3(g) shows a PLA cone formed by a 3D printer and covered with a balloon rubber
to enhance friction, and (h) shows a conical mold created by a 3D printer andthen molded with a ﬂexible material called ”human skin gel”. (h) is a conicalmold made by a 3D printer and molded with a ﬂexible material called HITOHADA gel. HITOHADA gel is the super soft urethane resins for molding manufactured by EXSEAL co. ltd[5]. Its hardness is ASKER C/15 just like humanskin. As a result of the actual experiment using a ball, only three types of horizontal movement on the dribble bar could be conﬁrmed. Therefore, to evaluate
the eﬀectiveness of the three types of dribble bars (f)-(h), we measured the velocity of the ball moving horizontally along the dribble bar. Three types of carpets
were used in the experiment. These pictures are shown in Fig. 2. The materialof all carpets is 100% polypropylene. Carpet1 shown in Fig.2(a) is used in our
playing-ﬁeld. The surface structures of (a) Carpet1 and (b) Carpet2 shown in
Fig.2 are similar and relatively soft. (c) Carpet3, on the other hand, has rougher
and harder. There is no signiﬁcant diﬀerence in terms of friction. The results are
summarized in Table 1. The results show that there is no signiﬁcant diﬀerencein the speed of ball movement by the dribble bars. However, the durability ofthe balloon rubber of Bar7 is poor, indicating that it is not suitable for practicaluse. In addition, the direction of horizontal movement of the ball was opposite
for both Bar7 and Bar8, even though they have the same cone shape. That is, inFig.3, where the dribbling bar and the ball are viewed from above the robot, (A)Case of Bar7, the ball moved with a slight bounce toward the top of the cone,and (B) Case of Bar8, the ball moved smoothly in the opposite direction. The
reason for this can be attributed to the diﬀerence in the contact area between theball and the dribble bar due to the diﬀerence in the surface hardness (viscosity)of the materials. That is, the surface of Bar 7 is relatively hard, and because theball makes contact at a point, it bounces and moves gradually to the left. On
the other hand, Bar 8 is made of a softer material, which is thought to increase
the size of the contact area as the ball dips into the dribble bar, pulling theball toward the right with a larger cone radius (higher rotation speed). It wasreconﬁrmed that the material (hardness) of the dribble bar is one of the mostimportant factors in choosing an appropriate dribble bar.

(a) Carpet1(b) Carpet2(c) Carpet3

Fig. 2. Carpets used in the experiment for a ball-holding performance

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Daichi Miyajima et al.

(a) in case of Bar7(b) in case of Bar8

Fig. 3. Direction of ball movement for diﬀerent bar materials
Table 1. Ball speed moving horizontally along dribble bar[m/s]Dribble bar\Materials
Bar6
Bar7
Bar8

2.2

Carpet1

Carpet2

Carpet3

0.1
0.09
0.1

0.09
0.11
0.1

0.11
0.12
0.11

Tentative dribbling device

One of the problems with our dribblers is their low maintenance. Current
dribblers consist of a ball sensor, dribbling mechanism, and tip kick bar in asingle component, and improving any of them would require redesigning all parts.
In this section, we evaluate the performance of a tentative dribbling device withimproved maintainability by dividing the dribbling peripheral mechanism into a
dribbling mechanism and a ball sensor & tip kick mechanism.

(a)Present(b)Tentative without damper
Fig. 4. Dribbling device

Figure 4(a) shows the current dribbling device, and Figure4(b) shows the dribbling device fabricated tentatively, respectively. The dribbling motor is a maxon

KIKS Extended Team Description for RoboCup 2023

5#283860 with a gear ratio of 1.4. The height from the carpet to the metal shaft
with 4mm diameter of the dribbler is about 36 mm. While the current dribbler
has all elements ﬁxed on the base plate, in trial dribbler, it is divided into thecomponents as units. Yellow part in Fig. 4(b) shows the dribbling unit, red partshows the ball sensor & tip kick unit, respectively. On the other hand, insteadof improving maintainability, this dribbler has removed the damper unit fromthe current dribbler. If the dribbling bar made of super soft gel shown in Fig.
1h can suﬃciently play the role of a damper unit, the dribbling mechanism canbe simpliﬁed. We veriﬁed the shock absorption and ball retention performanceof the dribbler with and without the bumper in the experiments.
Veriﬁcation of Shock Absorption Performance of Tentative dribbler
Conventionally, it is quite common to mount a damper on a dribbler. It is interesting to note that the ETDP2022 from Tigers Mannheim introduces a dribbler
equipped with a 2 degree of freedom damper[6]. On the other hand, our recent
damper unit has a problem that the dribbling bar bounces the ball during dribbling, so we removed the damper unit for trial and compared its behavior with
two types of dribbling bars. One is made of conventional polyurethane rubber
and the other is made of newly fabricated super soft gel. By comparing these
two types, the possibility of simplifying the dribbling unit is investigated.
The following experiment was carried out to compare the shock-absorbing performance of two dribblers. A ball moving at a constant speed (about 2.3 ms−1 )was impacted head-on with a dribbler that was not rotating. The distance r between the robot and the bouncing ball was measured 50 times, the average wascalculated, and compared for each dribbler. The ball was ejected using the slope
shown in Fig.5(a), and the robot was ﬁxed on the ﬁeld. The measured distances
are shown in Table 2.
Table 2 shows that the bounce distance of SUPER SOFT GEL is longer than thatof the current dribble bar, with or without damper. This result indicates thatthe shock-absorbing ability of the super soft gel only is insuﬃcient. As a result,it was shown that the tentative dribbling device has a lower shock absorption
ability than the present device due to its structure without a damper. Namely,
it was again conﬁrmed that dampers are important to improve the performanceof shock absorption.

(b) Measured r(a) Environment
Fig. 5. Experimental condition

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Daichi Miyajima et al.
Table 2. Evaluation of shock absorption performance for dribbler

Dribbling bar
Distance between robot and stopped ball [mm]Current rubber with Damper
379
Current rubber without Damper
600
Super soft gel with Damper
463
Super soft gel without Damper
819

Veriﬁcation of ball keeping performance In real games, performance in possession of the ball (e.g., ball placement, preliminary movements before shooting,etc.) is critical. If fast and stable movement could be achieved while keeping
possession of the ball, AI’s tactics would be greatly expanded. The following
experiment was carried out to compare the ball keeping performance of thedribbler described in the previous section. While the robot was keeping the ball,the robot was allowed to rotate until it released the ball at a constant angularvelocity. The ball keeping time against the angular velocity of rotation was measured and compared between the present dribbler and the proposed bar usedwith super soft gel shown in Fig.6. The speed of the dribbling motor was about
100s−1 (6000rpm).

Fig. 6. Dribbling bar
made from super soft gel

Fig. 7. Ball keeping performance

The results are shown in Fig.7. The horizontal axis represents the angularvelocity of the robot’s rotation, and the vertical axis represents the average
keeping time. The results of Fig.7 show that the current dribbler cannot keep
the ball stably when the robot’s angular velocity is 200 deg/s or higher. On
the other hand, the dribbler with super soft gel could keep the ball stably formore than 10 seconds if the angular velocity was less than 300 deg/s. However,contrary to our prediction, the super soft gel dribblers kept the ball at the endof the bar. This indicates that the keeping force of the super soft gel is due toKIKS Extended Team Description for RoboCup 2023

7

the softness of the material. That is, the contact area with the ball is larger,
which may be the result of suﬃcient frictional force transferring the dribbler’srotational force to the ball. In this section, we describe the shock absorption
and ball keeping performance of a prototype dribbler. The results showed theimportance of the damper and the usefulness of the new material (super softgel). In the future, the durability of the super soft gel should be investigated indetail.

3

Electrical system

The main circuit was modiﬁed signiﬁcantly last year. This year, we are continuing
to use that circuit. In this section, especially, we describe a new circuit board
for kicker device.
3.1

Introducing a New Kicker Circuit Using the Two-Step Booster
MethodKIKS has conventionally used a one-stage voltage booster circuit as shown in
Fig.8 as a kicker circuit to operate the solenoid. In this circuit, a reverse voltage
equal to the voltage of the capacitor for the solenoid operation is instantiallyapplied to the rectiﬁer diode during the operation.
Recently, in SSL, the power of the kick and continuous operation have been required, and it has been necessary to increase the capacitor voltage when charging
the device. Since our previous charging method applied a large load to the device, there were concerns about malfunctions during a game and a decrease in
the circuit life. Therefore, we designed and introduced a new kicker circuit using
a two-step boost method. It can be applied to the new main board introducedin ETDP[1] in 2022. The eﬀectiveness of the circuit is described below.

Fig. 8. previous kicker circuit for voltage Fig. 9. LTSpice circuit for previous voltage
boosterbooster

Figure 9 and 10 show the simulator schematics of the previous kicker circuit andthe voltage booster part of the new kicker circuit by LTspice XVII, respectively.
The results of the transient analysis are shown in Fig. 11 and 12, respectively. In

8Daichi Miyajima et al.

the previous kicker circuit in Fig. 11, a maximum reverse voltage of about 70 V
is applied to rectiﬁer diode D1 300 ms after the start of charging. On the other
hand, in the new kicker circuit in Fig. 12, the reverse voltage applied to rectiﬁer
diode D4 is suppressed to about 52 V (Max 70 V - Min 18 V). This meansthat the voltage applied to the device is reduced by 1/4 of that in the previous
kicker circuit. Therefore, the improvement to the 2-stage voltage booster circuit
is considered to contribute to the improvement of the durability of the circuit.

Fig. 10. LTSpice circuit for new voltage booster

Fig. 11. Waveforms of C1 terminal voltage Fig. 12. Waveforms of C1 terminal voltage(green) and D1 anode voltage (blue) in pre- (green) and D4 anode voltage (blue) in new
vious kicker circuit
kicker circuit

4

Software system

4.1

Prediction of actions using machine learning

Predicting the actions of the opposing robot is one of the major factors that
dominate soccer tactics, as it is useful in getting the ball and determining effective positioning. Our team, in particular, has the problem of low ball control
during the game. To solve this problem, we tried to predict which robot the
opponent robot will pass to next based on information from the ﬁeld. In thissection, we consider this problem as a classiﬁcation problem of the opponent’s
behavior and analyze it using machine learning. We deﬁned a total of 12 classes:

KIKS Extended Team Description for RoboCup 2023

9

10 candidate classes for passing, and 2 classes for not passing (dribbling) andshooting.
Models and Data Structures In order to eﬀectively learn information on aﬁeld, image classiﬁcation methods are considered suitable. Therefore, we used
one of them, ResNet (Residual Neural Networks)[7].
The input to ResNet requires a matrix representation of the ﬁeld information.
Since in the ﬁeld of image classiﬁcation, convolution is performed by superimposing two-dimensional images of each RGB and convolving them as threedimensional, we divided the ﬁeld information into multiple dimensions and performed convolution. Here, the ball and each team’s robot are represented asanother 120×120 matrix, each corresponding to a position on the ﬁeld. For theteam being trained, the value of the robot’s angle and speed were similarly placedin the corresponding elements of the matrix, resulting in ﬁve matrices of ﬁeld
information.
ResNet is one of the CNNs (Convolutional Neural Networks), but it is generally
considered unable to learn the positional relationships in an image. Therefore,
to solve this problem, we added matrices representing x-axis and y-axis as ﬁeld
information in addition to the ﬁve matrices mentioned above, as proposed by
Rosanne Liu et al.[8]. That is, a 7×120×120 matrix was used as input information to the model. The IDs used as output were assigned in x-axis in ascendingorder to ensure uniqueness of IDs across data. The above dataset was used as
the input to the model when the robot of the team being trained kept the ball.
Then, the ID of the robot with the ball at the end of one play was used as theoutput of the model to create the teacher data. We played our AIs against eachother on the GRSim simulator and generated data for 149 pass plays. Using
those data as training data, training and inference were performed on the training and evaluation data, respectively.
The results are shown in Table 3. The results show that inference was not suﬃcient for the evaluation data. On the other hand, for the training data, it showsan accuracy of 80%, suggesting that the task is classiﬁable. Table 4 also shows

Fig. 13. Method for making dataset

the results of inference on the same training data under diﬀerent conditions.
Data column indicates the data deleted from the input. From the table, it can

10Daichi Miyajima et al.
Table 3. Probability predicted by ResNet(149 samples)
Data \
Learned data
Untrained data

Accuracy
0.80550.2564

Avg.Precision
0.74040.0751

Avg.Recall
0.6251
0.1138

Avg.F-measure0.65270.0895be seen that the amplitude of velocity has a signiﬁcant eﬀect on the accuracyof inference, while the angle parameter contributes very little to inference. Thismeans that it is almost meaningless in the data due to the fact that most robots
are facing the ball direction. On the other hand, it can be seen that it works
eﬀectively with regard to the location matrix. However, when we experimented
with increasing the number of data, we observed a decrease in accuracy. As aproposal for improvement to increase accuracy, we will consider using images
or time-series data as input data. Md Amirul Islam et al.[9] demonstrate thatthe parameters of the Convolution layer can also be used to store location information, and we would like to try this out. In addition, in this section, weused inputs that represent coordinates with reference to CoordConv, but xyaxis should originally be incorporated into the Convolution layer, and we willconsider its implementation. After the completion of this model, we will apply
it to actual matches and consider applications such as countermeasures for each
opponent, or diversion of the opponent’s passing algorithm to our team likeimitation learning.
Table 4. Probability predicted by ResNet under the other condition
Data \

default

Accuracy 0.8055

4.2

speed

angle

speed, position matrix
angle
0.2592 0.8148 0.3611 0.6944

Veriﬁcation of the eﬀectiveness of position control performedby the robot itself

In this section, we try to improve the motion performance of the robot by controlling the position of the robot itself.
Problems with Global Vision Only Control At present, we are using global
vision for position control based only on information from the server side. However, this method has the following problems.

KIKS Extended Team Description for RoboCup 2023

11

– If the communication delay from the server to the robot is large, the robot
oscillates back and forth due to the diﬀerence in the control cycles between
the server and the robot.
– The robot cannot be controlled if there is a vision problem (e.g., the ball isin the robot’s shadow and precise position information is not obtained) or if
the communication is interrupted.
We tried to mount local vision on a robot in 2022 and reported on the utility of
tracking the ball with local vision(Fig. 14) only and the control to support global
vision[1]. In this section, we investigate the eﬀectiveness of using an encoder
attached to the motor in addition to local vision. In the following experiments,
we try to control the robot’s position using only an encoder.

Fig. 14. Local camera mounted on the robot

System overview Present system and trial system built on tentatively are
shown in Fig.15. The current system uses only information from the server sideto generate velocity to the target position, but in this experiment, control is performed only with the robot’s encoder. If the precision of the position informationfrom the local vision and encoder improves, it is expected that the robot’s position control can be improved by adding it to the information from SSL vision.

(a) Present system(b) Trial system

Fig. 15. Motion contol system

12Daichi Miyajima et al.

Experimental As shown in Fig.16, the robot moved 1000 mm in a straight line
and rotated 90°at the same time, as commanded by the server, and measured thetime from the start to the stop of the action. The time was compared between thecase with an Ethernet connection (corresponding to an environment with goodcommunication conditions) and the case with a Wii connection (corresponding
to an environment with poor communication conditions). Table 5 summarizes

Fig. 16. Experimental method for motion control performancethe experimental results. In the case of the Ethernet connection to the server,
there was almost no diﬀerence in the process time between the current sytem
and the trial system. On the other hand, when connected to the server via
Wiﬁ, the current system did not ﬁnish its operation for more than 10 seconds,
whereas the trial system completed its operation in about 2 seconds, as in thecase of the Ethernet connection. This result suggests that there is a problem
with the stability of wireless communication in the current system and conﬁrms
the eﬀectiveness of the trial system.

Table 5. Veriﬁcation results (10 times average)
Process time for \ Connection
Ping between server and robot
Ping between server and vision
Present system
Trial system

4.3

Ethernet [ms]
6.7
2.2
2159
2060

Wiﬁ [ms]
11.5
6.1>100001987

Trial experimetal in path planning based on Informed RRT*At present, KIKS uses the Human-Like algorithm as the robot’s path planning
method. While the Human-Like algorithm is simple and fast, it sometimes fails
to generate an optimal path when the path is blocked by obstacles or undersome conditions.。Therefore, in this section, we aim to introduce path planning
based on Informed RRT*[10] and verify its eﬀectiveness.

KIKS Extended Team Description for RoboCup 2023

13

Experimental method In this experiment, the random place to place a point
to generate the ﬁrst path is somewhere in the range 14000 mm×11000 mm witha margin of 1000 mm around a ﬁeld of 12000 mm×9000 mm, as shown in Fig.
17a.

(a) Area of points placed randomly
at the beginning(b) Limited area of points placed randomly

Fig. 17. Area where random points are placed

After the ﬁrst path is generated, as shown in Fig. 17b, a square is createdcentered at the midpoint between the distances of the robot and the ball, andrandom points are placed within this range. After the ﬁrst path is generated,
as shown in Fig. b, a square is created centered at the midpoint between the
distances of the robot and the ball, and random points are placed within thisrange. Note that the length of one side of this square in the ﬁrst stage should be
double the length of the x- and y-components of the distance between the robot
and the ball added together. If the path can be generated again by placing a
number of random points, the coeﬃcient applied to the length of the square isslightly reduced to narrow the range, and then a random point is placed in thesame way. The coeﬃcient in this case is written as the expand coef f icient, andthe change in this value is given by eq. (1).
expand coef f icient = e−0.05x + 1(1)

Note that x is the number of times the path is generated. The length of one
side of the square in the range where the ﬁnal random point is placed converges
to the sum of the lengths of the x and y components of the distance between
the robot and the ball, as in the notation in Fig. 17b. In this case, after 100
random points are placed, the path with the shortest distance, generated by
the range reduction described above, is taken as the optimal path. The reason
why we did not specify the range with an ellipse, which is typically used inInformed RRT*, is that we are concerned about a decrease in process speed
when operating path planning in real-time at high speed. In this experiment,
the minimum length of one side of the square for range limitation is 2000 mm.

14Daichi Miyajima et al.

Thus, the distribution range of random points is reduced to a minimum of about
2.60%(=(2000)2 /(11000×14000)) of the starting point for the search.

Results and discusson Place the ally robot (yellow), the enemy robot (blue),and the ball (orange) as shown in Fig. 18 on the simulator. One of the three
enemy robots is placed in the corner of the penalty area, which is a prohibited
area, and the other enemy robots are placed at a distance of 100 mm fromthe other robots. This is to verify that the robot does not try to force its way
through a space smaller than the size of the robot and collide with an enemy
robot. The ball and the allly robot should be placed 900 mm from the row of
enemy robots, and the ball and the allly robot should be placed so that thestraight line connecting them perpendicularly crosses the row of enemy robots.

Fig. 18. Experimental to evaluate performance of path planning

KIKS Extended Team Description for RoboCup 2023

15(a) Typical motion for RRT* (Forced passes through between robots)(b) Typical motion for RRT* (Bypass success)(c) Typical motion for Informed RRT* (Bypass success)Fig. 19. Typical motion of RRT* and Informed RRT* on path planning

The behavior of each of the three methods, Human-Like, RRT* (unrestricted
range), and Informed RRT*, was conﬁrmed. Considering the possibility thatslight errors in the actual ball and robot placement could aﬀect the results, morethan 10 experiments were performed. In particular, since the path generation of
the RRT* and Informed RRT* methods is characterized by randomness, 50
experiments were carried out. An example of the results is shown in Fig. 19.
Table 6 also tabulates the success rates. In all experiments in Human-Like, theallly robot interrupted its action by touching the enemy robot. The success rateof bypassing RRT* was about 40%, but it sometimes tried to forcefully passbetween enemy robots, and the rate of stopping when it touched an enemy
robot was as high as 50%. On the other hand, Informed RRT* did not passbetween enemy robots, and its success rate exceeded 90%. These results indicate
the usefulness of Informed RRT*, although only under the conditions of thisexperiment. The average process time under the present experimental conditions,however, was the fastest for Human-like at about 18.0 ms and the slowest for
Informed RRT* at about 36.0 ms (RRT* was about 29.5 ms). Therefore, it isnecessary to consider faster process time for future introduction.

16

Daichi Miyajima et al.
Table 6. Experimental results

Path generation method \ Success rate Rate of forced passes
Rate offor bypass(%) through between robots(%) Stoppage(%)
Human-like (10times)
00100
RRT* (50times)
38
12
50
Informed RRT* (50times) 92
0
8

Acknowledgments
This work was supported in part by JSPS KAKENHI Grant Number 20K03263,
Grant-in-Aid from the Chuden Foundation for Education and The Nitto Foundation in Japan, respectively.

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KIKS Extended Team Description for RoboCup 2023

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