BabyTigers 2001: Osaka Legged Robot Team
Noriaki Mitsunaga, Yukie Nagai, Tomohiro Ishida,Taku Izumi, and Minoru Asada
Emergent Robotics Area, Dept. of Adaptive Machine Systems,
Graduate School of Engineering Osaka University, Suita, Osaka, 565-0871, Japan

1

Introduction

We have developed all kinds of motions such as walking, standing-up, and kicking
towards Seattle. From a human designer’s point of view, it is easier to write
strategies based on the geometric, global position data but seems less attractive.
Then, we are attacking learning issues such as action selection [1] [2], observation
strategy without 3D-reconstruction.

2Color detection

In the RoboCup Legged Robot League field [3], seven colors (aqua-blue, yellow,orange, blue, red, pink, green) are used and robots need to detect and discriminate them. The SONY’s legged robot has the color detection facility in hardwarethat can handle up to eight colors at frame rate. To detect these colors with thisfacility, we need to specify each color in terms of subspace in YUV color space.
YUV subspace is expressed in a table called Color Detection Table(CDT). In
this table, Y are equally quantized into 32 levels and at each Y level we specify
one rectangle (umin i , vmin i ),(umax i , vmax i ) (i = 1, ..., 32).
In order to make CDTs, we used the same procedure as in previous years [4][5] [6], which consist of the followings;
1. take an image which includes the object to be detected,
2. specify appropriate pixels to be detected with GUI program and make a listof YUV components,
3. classify each pixel according to the Y level as they are classified in CDT andmake a bounding box of UV in each level,
4. check if detection is OK, else return 1) .
We iterate these procedure for each color with several images.
We improved the GUI tool for making CDTs (Fig.1). The main point is tomake the CDT checking by the operator smoother. We placed the file selector,original / filtered images, the color which mouse cursor is on, the list of the color
specified, UV rectangle list (the CDT) and UV color box with a rectangle. We
have found that the dynamic range of the on-board camera’s color saturation
was not so broad.

It seemed that the lightning conditions of this year (2001) were the bestin RoboCup and discrimination of colors were much easier. However, there weresituations where we specified all the shaded colors to be included in the CDT, therectangle included the gray color which cause mis-detection of colors. We show
a such an example in Fig.2. The figure shows the specified points by the humanoperator which consists of both shaded and non-shaded colors of the aqua-bluegoal and landmark poles. We see that the bounding box of these points willincludes the points (127, 128) in UV space, which means achromatic color. Also
there were reflexions from the field (green) to the goal colors which may makethis problem worse.

Fig. 1. Our new tool for constructing CDTs.

3Behaviors

We show the state transition map of the goal keeper in Fig.3. The state changes
when the size of the goal or the ball exceeds the thresholds (GOAL S,M,L,
BALL S,M,L), or timeouts (*Counts). The basic behavior is; 1) to find and go
to own goal, 2) to stay in front of the goal watching the opponent goal while
try to find the ball, 3) if the ball size becomes high, try to clear the ball to thedirection of the opponent goal.
One of our attacker is a modified version of the goal keeper. It does not wait
for the ball to become large. It attempts to reach the ball if it finds. And it does
not go inside the penalty area when it does not find the ball.
Our another attacker’s basic behavior is, 1) to try to find the ball, 2) to gonear the ball if the robot finds it, 3) to kick the ball according to the estimatedaquablue in U-V planeV

140

130

127

110

120127

UFig. 2. Aquablue points in UV plane (96 ≤ Y ≤ 133) specified by the operator.

direction of the opponent goal. It estimates the direction by the dead-reckoning
in the robot centered coordinate.

4Actions and walking

We developed walking based on trot, standing-up, kicking, and diving by ourselves. We adopted three kinds of methods for walking development. One wasto design by human inspiration, the other two were to extract walking throughrobot-environment interaction. That is, we prepared basic walking motion withvariable parameters and tuned the parameters with a real robot in the field. We
used the steepest descent method for parameter tunings in RoboCup 2000 andhand-tuned in RoboCup 2001 based on that. And we also used genetic algorithm
for tunings and we hand-tuned genetically acquired parameters. Other motions
were developed by the human designer.

5Conclusion

We implemented all strategies in the robot centered coordinates and our goal
keeper showed good performance during the competition. Our keeper saved thegoal well even from the champion team UNSW. Unfortunately the simple estimation of the goal direction did not seem to work well in the competitions. Inaddition to this problem, team works and learnings are our future issues.

References
1. N. Mitsunaga and M. Asada. Observation strategy for decision making based on
information criterion. In P. Stone, T. Balch, and G. K. Kraetzschmar, editors,

SEARCH_BALL1
BALL_SBALL lost

TRACK_BALL

goal.x < ball.x

CIRCLE_BALL_L
BALL_M
BALL_M

SEARCH_BALL2

BALL_L*0.3
GOAL_SREACH_BALL
BALL lost

BALL_S

CircleCount > 5

BALL_LCIRCLE_BALL_R
BALL_L

CLEAR_BALL

goal.x > ball.x

ClearCount > 0

SEARCH_BALL3
BALL_S
BALL_S

CameraCount > thld

BACK_GOAL
BackCount > 3

WalkCount < 0

MyGoalCount > 30

SEARCH_MYGOAL
GOAL_M

GOAL lost

REACH_GOAL
GOAL_L
WalkCount < 0

GOAL_L

OpGoalCount > 10

SEARCH_OPGOAL
GOAL_SFig. 3. State transition map of the goal keeper.

RoboCup 2000: Robot Soccer World Cup IV, pages 189–198. Springer, Lecture Notein Artificail Intelligence (2019), 2001.
2. N. Mitsunaga and M. Asada. Visual attention control by sensor space segmentation
for a small quadruped robot based on information criterion. In The RoboCup 2001
International Symposium, 2001.
3. M. Veloso, W. Uther, M. Fujita, M. Asada, and H. Kitano. Playing soccer withlegged robots. In Proceedings of the 1998 IEEE/RSJ International Conference onIntelligent Robots and Systems, volume 1, pages 437–442, 1998.
4. N. Mitsunaga, M. Asada, and C. Mishima. BabyTigers-98: Osaka legged robot team.
In M. Asada and H. Kitano, editors, RoboCup-98: Robot Soccer World Cup II, pages
498–506. Springer, Lecture Note in Artificail Intelligence (1604), 1999.
5. N. Mitsunaga and M. Asada. BabyTigers-99: Osaka legged robot team. In M. Veloso,E. Pagello, and H. Kitano, editors, RoboCup-99: Robot Soccer World Cup III, pages
762–765. Springer, Lecture Note in Artificail Intelligence (1856), 2000.
6. N. Mitsunaga, Y. Nagai, and M. Asada. BabyTigers: Osaka legged robot team. In
P. Stone, T. Balch, and G. K. Kraetzschmar, editors, RoboCup 2000: Robot Soccer World Cup IV, pages 631–634. Springer, Lecture Note in Artificail Intelligence(2019), 2001.