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KIKS 2015 Team Description Paper

Tetsuya Sano, Soya Okuda, Kosuke Matsuoka, Yu Yamauchi,

Hayato Yokota, Tatsuro Sakaguchi,

Masato Watanabe

and Toko Sugiura

National Institute of Technology, Toyota College

Department of Electrical and Electronic engineering,

2-1 Eisei-cho, Toyota Aichi, 471-8525, Japan

sugi@toyota-ct.ac.jp

URL: http://www.ee.toyota-ct.ac.jp/~sugi/RoboCup.html

Abstract

. This paper is used to qualify as participation to the RoboCup 2015

small size league. Our team's robots and systems are designed under the RoboCup
2015 rules. The major points of improvement in this year are improvements about
the performance of driving wheels, electrical circuit and AI system. The over-
views of them are described.

Keywords

: RoboCup, small size, autonomous robot, global vision, engineering education

Introduction

We have made soccer robots since 2002. We still have problems with a strategy, an

electrical circuit, and their kinematic performance. One of the serious problems is that
the success rate of their passing a soccer ball is low. Our robots can pass the ball
worse than those of other teams, so that robots which receive the ball often can’t catch
it successfully. As a result, our team is late for making a smart strategy. In order to
solve the problem above, we attempted to improve our robots in 2014 so that they can
pass perfectly.

First, we attempted to improve the ability

and receive the ball precisely by

to pass

making robot’s driving wheel and the ball sensor outskirts better. Second, we made
our robots catch the ball more precisely by enhancing the ability to predict where the
ball reaches. The main improved points are as follows:

Improvement of the driving wheels

Improvement of the electrical circuit

Improvement of the AI system

Hardware of the robot

2.1.

Performance of our robots

Specifications of our robot made in 2015 are shown as Table 1. Its weight was light-

er than 2014 model. Furthermore, its height and center of gravity are lower by using
of flat type solenoid. The other mechanical elements are the same as the previous
year.

2.2.

Travelling performance for the wheel

The wheel used in 2014 has a superior performance in its durability, but the width

between each small ring-tire had a bad influence on its kinematic performance in that
it caused the vertical vibration. It also caused a malfunction of a circuit and induced
the noise of its acceleration sensor, which negatively affected the durability of the
robot.

Therefore, as shown in Fig. 1, the wheel was improved by increasing the number

of small ring-tire to make a shape of the wheel close to a perfect circle. Moreover, the
grip power of wheel was enhanced by using of X ring as small ring-tire of wheel.

Table 1. Specification of the robot (comparison with 2014 model)

2015 version

2014 version

Weight

1.9kg

2.3kg

Material

Aluminum alloy

Driving motor

maxon EC45 flat (30watt)

maxon EC45flat (30watt)

Driving gear ratio

4.0 : 1

3.6 : 1

Wheel diameter

50mm

56mm

Number of solenoids

Straight kick: 1
Chip kick: 1

Straight kick: 1
Chip kick: 2

Straight kick power

Ball speed of 8m/s

Ball speed of 8[m/s]

Chip kick power

3.0m away from robot

Max 3.0m away from robot under
the condition of initial angle 40°

Dribbling motor

maxon EC-max22

Dribble-roller diame-
ter

15mm

Dribbling gear ratio

1.2 : 1

Fig. 1

Driving wheel (a) 2014 model and (b) 2015 model

2.3.

For kick device

When we used straight kick last year, sometimes the ball bounced and we couldn’t

kick the ball at the designed speed. A close investigation of the cause reveals that it is
attributed to the fact that straight bar kicked the ball at higher a center position of the
ball.

(a) The height of straight-kick bar (previous)

(b) The height of straight-kick bar (present)

Fig. 2 Improvement of the height of straight-kick bar

The kick device we made last year is shown as Fig. 2(a). The solenoid for straight

kick bar is set on the solenoid for chip kick bar in our robots. In Fig. 2(a), the straight
kick bar kicks the ball, which induces a bound of the ball. In order to make this situa-

Solenoid for straight kick

Solenoid for
chip kick

Straight-kick bar

(a)

(b)

tion better, we reduced the number of turns of solenoid for chip kick and set straight
kick  bar  at  a  lower  position  by  lowering  the  height  of  the  whole  solenoid.  The  im-
proved  device  is  shown  in  Fig.  2(b).  It  is  confirmed  that  the  ball  that  is  kicked  out
from  the  device  doesn’t  bounce  and  moves  in  stable  orbit.  Furthermore,  the  experi-
ment we made reveals that the reduction of the number of turns has little influence on
the  distance  of  the  chip  kick.

The result for this is the chip kicks the ball at a little

lower position, which can touch the bar to the ball more extensively and conduct force
efficiently.

Electrical design

3.1.

Development of new ball-detecting system using ranging sensor

Existing ball sensor is arranged on the both sides of the catching part in front of the

robot as shown in Fig. 3. When the ball enters between an infrared-emitting diode and
a photodetector, the sensor reacts. But a sensor’s convex portion structure may pre-
vent reacting on ball. That is, there are cases that robot can’t catch the ball when the
ball coming from robot’s slant, left and right hits a sensor’s convex portion structure.
So we used infrared distance measuring sensor unit to improve performance of catch-
ing the ball.

Fig. 3 Ball-detecting sensor (conventional)

Shown as a situation that robot gets a sensor in Fig. 4(a), and infrared distance

measuring  sensor  unit in Fig. 4(b). This device outputs the voltage corresponding to
the  detection  distance  and  digital  I2C  data.  We  added  IC2  host  module  to  existing
circuit board, and put this sensors on two points of the robot’s front. As a result, we
checked  the  robot  could  catch  the  ball  with  more  huge  area  than  the  previous  situa-
tion. Now, we put these sensors into some robots, but we can’t use data of distance. In
the future, we expect to get data of position and data of distance between robots and
the ball from two sensors, therefore we expect to use data for single robot’s attacking
and defending without server’s orders.

Fig. 4 (a) new ball-sensor mounted on a robot and (b) distance measuring sensor unit [SHARP
GP2Y0E03]

3.2.

Introduction of bidirectional communication

The previous communication between AI server and robots is simplex to send or-

ders from AI server to robots. We use only Xbee module to send data as shown  Fig.
5(a), but as discussed above, we have to incarnate duplex to send data from distance
measuring  sensor  to  a  server.  So  we  made  the  environment  for  the  reception  in  AI
server and add 2 Xbee to robots modules for sending and receiving to use duplex. The
picture  of  situation  that  robot  has  2  Xbee  is  shown  as  Fig.  5(b).  So  far,  a  Xbee  re-
ceives  orders  from  server,  and  another  sends  situation  of  ball  sensor’s  reacting.  We
use  information  sent  from  robots  to  check  robot  kicked  or  didn’t.  We  can  check  re-
ceiving data in server. The previous situation, there was only way to research distance
between robots and balls from SSL vision if we want to know state of robot’s catch-
ing the ball. So if robots don’t have the ball, robot may blank shot because of sensor
misrecognition.  If  robots  send  situation  of  catching  the  ball  to  AI  server,  we  can  re-
duce blank kick the ball. Therefore, if we know where the ball is existing on a catch-
ing devices, we can use the data efficiently to expect where the ball is going.

Fig. 5 Communication module (a) previous system (one XBee module), (b) bidirectional com-
munication system (two XBee modules)

Size 16.7 × 11.0 × 5.2mm

(a)

(b)

(a)

(b)

Improvement of the program for receiving a pass

In the recent SSL League, with the extension of the soccer filed, more accurate and

frequent passes are required for scoring a goal. Our previous robots had a low success
rate of receiving a pass owing to  their  mechanical accuracy and structure. The robot
making a pass was unable to kick straight through to the robot receiving it. Also, the
latter  was unable to receive it because of the projection portion of the ball sensor or
the  chip  kick  bar  as  well  as  the  insufficient  prediction  of  the  orbit  estimation  of  the
ball from the former.

This year, we used Kalman filter in order to improve this prediction and calcu-

lated the angle for the direction of a ball from the difference of the position be-
tween before and after it moved. It is shown in Fig. 6. The angle of the blue arc
shown in Fig. 6 is defined as angle

θ.

Fig. 6 An angle of moving ball

The estimated result of the direction of a ball is shown in Fig. 7. The red line in Fig.

7  shows  the  result  after  Kalman  Filter  was  used.  Before  the  filtering,  we  can  detect
vibrational component which hinders the calculation of the direction of the ball, while
after the filtering, the vibration amplitude falls inside the range within 0.1 [rad].  Fig-
ure  8  shows  the  prediction  result  of  the  target  position  in  each  frame  based  on  the
result  after  the  filtering.  The  black  line  in  Fig.  8  shows  the  ball  is  kicked  from  the
lower right point A to upper left point B near goalpost, which proves that the predic-
tion  of  the  orbit  estimation  of  the  ball  is  more  accurate  than  before.  The  solid  lines
which deviated from the target significantly were drawn at the initiation stage of the
experiment  when the ball a robot started to kick bounded. Under the existing condi-
tions, a robot can only make a pass when it is in a resting state. The development of
the robot making a pass while moving remains to be solved in the future.

Fig. 7 Variability of traveling direction estimated by using of Kalman filter for the ball

Fig. 8

Prediction results (black lines) based on Kalman filter for the target position

5. Conclusions

Continuous improvement has been made to our robots every year. We hope they will

perform better in this coming competition.

References

[1] Ryu Goto et al.: KIKS 2013 Extended Team Description, 2013
[2]K. Fukunaga: Introduction to Statistical Pattern Recognition, Academic Press, 1972.

[3]

James Bruce and Manuela Veloso: Real-Time Randomized Path Planning for Robot Navi-

gation, Intelligent Robots and Systems, 2002. IEEE/RSJ Int. Conf. Intelligent Robots and Sys-
tems, pp.2383-2388, 2002.

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