Petri Net based Role Arrangement Mechanism for the Soccer Robot System
Ming-Yuan Shieh*, Ching-Chih Wen, Chung-Chieh Lien
Department of Electrical Engineering
Southern Taiwan University
Tainan, Taiwan
E-mail: myshieh@mail.stut.edu.tw
Abstract: This paper proposes a Petri net based role
arrangement mechanism for the soccer robot system. Soccer
robot games consist of the challenges of path planning, obstacle
avoidance, image processing, real-time control and decisionmaking strategy. As well known, effective strategies dominate a
successful soccer game, moreover, the key point is based on
real-time role and task arrangements. Since the Petri net
algorithms benefit the real-time role switching and formation
controls for facing dynamic and uncertain situations. In this
paper, the Petri net based control scheme is designed to
arrange robotic roles and to perform respective obstacle
avoidance decision for real-time optimal controls. In which, the
role arrangement supervisor is designed by Petri net and then
it is used to modeling what a robot should do in an assigned
role of attacking, defending and goalkeeping. Besides, the
optimal path of robotic movements could be determined and
modified by A* algorithm. The simulation and experimental
results show that the proposed system has obtained
satisfactory role arrangement efficiency.
Keywords-role arrangement mechanism; robot soccer system;
AndroSot; Petri net.
I. INTRODUCTION
Following rapid advances in human life, to develop
suitable robots and related products becomes more essential
and emerging. Generally speaking, humanoid/android robots
are the most conspicuous of autonomous robots. Since
android robots have similar features as human, they can be
regarded as the most suitable to live with people in the
human living environment. During these years, although
there were many real android robots developed but most of
them didn’t satisfy human’s expectations. For example, the
robot “Asimo”[1], which is called as the most advanced
humanoid robot nowadays, only can perform several simple
preset operations, such as communication with humans by
limited conversation and stiff emotional expression. Such
robots, which always are designed in automatic for
movements and semi-automatic for human-robot interaction,
have their cute appearance, can follow, guide, perceive,
recognize, avoid obstacles, and interact with people or
environment.
From many approaches [2-3], one can see to develop an
appropriate humanoid robot system is really difficult. It is
still as an open question based on the developed techniques
for humanoid robot soccer games. Hence, in order to simplify the problems existed in a humanoid robot soccer game,
the FIRA (Federation of International Robot-soccer
Association) [4] promote the AndroSot game focusing on
visual servo control strategies instead of all in one design for
an android robot since 2007. The AndroSot game aims to let
the contestants design their humanoid soccer robot system
based on a global visual system, a decision-making PC,
strategy programs, wireless communication system, and
autonomous humanoid robots. These android robots execute
the strategic commands received from the PC in which all
strategies are concluded based on visual processing by using
the real-time field sequence images captured by a global
camera hanged on the top of the game field.
In the AndroSot game, the images consisted of the ball,
the robots and the field can be obtained from the top-view
camera. By analyzing these images, the control system can
identify the locations of robots and the ball, and the relative
velocities of robots and ball. As well known, effective
strategies dominate a successful soccer game, in which, the
key point is the real-time role and task arrangements. Petri
nets were invented in August 1939 by Carl Adam Petri for
the purpose of describing chemical processes. It is a
graphical tool for the description and analysis of concurrent
processes which arise in systems with many distributed
components. Since the Petri net algorithms benefit the realtime role switching and formation controls for facing
dynamic and uncertain situations[5-7]. In this paper, the Petri
net based control scheme is designed to arrange robotic roles
and to perform respective obstacle avoidance decision for
real-time optimal controls.
Besides, how to determine the optimal path of robotic
movements is also an important problem in robot soccer
games. A* algorithm is a searching algorithm that is widely
used in pathfinding because it’s fairly flexible and can be
used in a wide range of contexts. It is an extension of Edsger
Dijkstra's algorithm[8]. A* algorithm achieves better
performance than Dijkstra's algorithm with respect to time
by using heuristic estimation [9-10]. It’s like Greedy BestFirst-Search in that it can use a heuristic to guide itself. In
common cases, it is as fast as Best-First-Search.
For FIRA AndroSot game, since all computational and
decision-making data in an computation system based on the
image data captured from the camera hanged upper on the
game field. It reveals good procedures of image processing
will provide not only real-time but also accurate performance
to the decision system of optimal motion controls for a real
robot soccer game. In this paper, a real system implemented
for 3-on-3 robot soccer games is proposed to verify its feasibility.
responding to appropriate game actions such as tracking ball,
kicking ball, moving for defense or attack.
However, before the hold of a competition, the field
image should be calibrated for exact controls. As shown in
Fig. 3(a), there are a few black points spaced in equidistance
as the datum points. To determine the interval between every
two of these points on image plane, one can transfer it by a
fixed formula to get a proper correction. Besides, since the
illumination in the whole field is always not uniform (as
shown in Fig. 3(b)), it will cause that a color has two distinct
RGB values just because of different locations. As the
situation is worse, the accuracy of image discrimination will
be all gone. Immediate color discrimination is proposed by
using a wider range of RGB definition of a color. It will
reduce the errors on image processing.
Figure. 1 The system configuration of an Andro-Sot game
II. THE SYSTEM CONFIGURATION
The Andro-Sot game in the FIRA RoboCup can be
illustrated simply from Fig. 1. In such competition, each
team could mount their camera on the top of the field; the
image sequences will be sent to the host computer through
transfer lines, then the PC will determine the control
commands for android robot soccer players through careful
strategies. These commands sent to the android robots in
wireless communication results in a real-time android robotsoccer game. Thus, one can divide the Andro-Sot system by
three parts at least such as the visual subsystem, the strategy
making subsystem, and the motion control subsystem.
The proposed data flowchart as shown in Fig. 2 can
illustrate the signal processing existed in an android robot
soccer game. While the game starts, the top view image
sequences of the field captured by the global CCD camera
firstly, then the objective features of the robots, the ball and
the environmental objects such as the door will be
determined after digital image processing procedures. These
objective features will offer the strategy making subsystem
decision making data for path planning, game strategy
decision and movement control command determination.
The android robots follow the commands received from
Zigbee modules then execute determined movements for
III. PETRI NET BASED ROBOT ROLE ARRANGEMENT
MECHANISM
The strategy of 3-on-3 robot soccer competition in this
study was divided as two parts: the principal strategy and the
immediate strategy. When the competition is taking place,
the control system firstly concludes the principal strategy of
every robot in accordance with what region the ball stays.
Then, the immediate strategy based on Petri net provides the
integrated control to every robot. Take consideration of
robotic mobility, the role of each robot depends on the
relationship between the ball, the robots and their locations
in the field. The Petri net mechanism performs robotic roles
arrangement to make the robots move in order and obey a
clear but harmonious strategy.
The supervisor presented in Fig. 2 receives the
information of robots and ball after image analyses in PC,
and decides the role for each robot. In other words, the
supervisor does not fix attacking and defending robots, but
their roles are decided and allocated according to the
situation. With the role of a robot fixed as goalkeeper, the
supervisor, according to the game situation, assigns of
attacking or defending to each of the other two robots. To
read the game situation, the supervisor continually receives
feedback infoemation on the robots postures and ball
position.
Figure. 2 The flowchart of the signal processing in an android robot soccer game
T5 : Robot2 is presently defending, but in a good
position to attack, and it takes the attacking role.
T6 : Robot2 is presently attacking, but in a good
position to defend, and it takes the defending role.
(a) Calibration of field image
(a) initial status (1)
(b) Partition for immediate color discrimination
Figure. 3 The calibration and partition of the image screen
The following places and transitions are defined for
modeling the supervisor in Petri net:
Structure: CS= ( P, T , I , O,W )
Places: P  P1 , P2 , P3 ,..., Pm  ,m=8.
Transitions: T  T1 , T2 , T3 ,..., Tn  , n=6.
(b) Transition firing and redistributions (1)
Input function: I(T1)={P1}, I(T2)={P2}, I(T3)={P3},
I(T4)={P3 , P5}, I(T5)={P7, P8}, I(T6)={P2 , P6}.
Output function: O(T1)={P2}, O(T2)={P1}, O(T3)={P8},
O(T4)={P4 , P6}, O(T5)={P6}, O(T6)={P5 , P7}.
Weightings: W=1.
The following places are defined to model the supervisor:
P1 : Robot1 defending, Robot2 attacking,
P2 : Robot1 attacking, Robot2 defending,
P3 : Robot1 attacking,
P4 : Robot1 defending,
P5 : both Robot1 and Robot2 defending,
P6 : Robot2 attacking,
P7 : Robot2 defending,
P8 : both Robot1 and Robot2 attacking.
The transitions are defined as:
T1 : Robot1 is in a good position to attack.
T2 : Robot2 is in a good position to attack.
T3 : Robot1 is presently defending, but in a good
position to attack, and it takes the attacking role.
T4 : Robot1 is presently attacking, but in a good
position to defend, and it takes the defending role.
(c) initial status (2)
(d) Transition firing and redistributions (2)
Figure. 4 Petri net models for the supervisor
Figure. 5 An obstacle case (the robot intends to be moved from the purple
point to the green one.)
Figure. 7 The simulation of path planning by A* algorithms
(a) a real-time topview image of field
(b)the interface screen
Figure. 8 The interface in the proposed system
Figure. 6 The optimal path of successful obstacle avoidance by A*.
The models of the Petri net supervisor can be
illustrated by the graphs in Fig. 4(a) to Fig. 4(d). As shown
in Fig. 4(a), assume that there are three tokens in P2, P3 and
P7, that is, the robot 1 is attacking and robot 2 is defending
at start moment. In the following instant, if transition T2 is
fired, token in P2 moves to P1 as shown in Fig. 4(b). It
means that the robot 2 becomes the attacker and the robot 1
becomes as the defender after this transition.
For the status shown in Fig. 4(c), the initial tokens are
occurred in P1, P4 and P6. It reveals that the robot 1 is
defending and robot 2 is attacking at this moment. In the
following instant, if transition T1 is fired, token in P2 moves
to P2; and then transition T6 is also fired, token in P2, P6
moves to P5, P7 as shown in Fig. 4(d). It results in the robot
2 becomes as another defender because the robot 1 is still as
the defender after these transitions.
(a) a sequence of frames
IV. PATH PLANNING BY A* AND EXPERIMENTAL RESULTS
During the strategy making procedures, the decision
making scheme always calculates the direction of the way
only but ignores to whether there are the obstacles on the
way. In such common situation as shown in Fig. 5, there is
an obstacle (the red object) existed on the way between the
departure and the destination. Here, the authors intend to
execute path planning by adopting A* algorithm which is
used for optimal shortest problem. Fig. 6 show the results of
path planning by A* algorithm. Form the simulation results
as shown in Fig. 7, one can see that the path is really the
collision-free and optimal shortest one.
(b) the topview screen
Figure. 9 The experimental results in a real competition
For real-time observation, the proposed system consists
of an interface which is capable to monitor the real-time
images of the field (Fig. 8(a)) and to display the estimated
results after strategy making procedures (Fig. 8(b).) Fig. 9(a)
and Fig. 9(b) show a sequence of frames that illustrate the
movements in a real competition by the proposed system. It
reveals that the Petri net really makes an appropriate role
arrangement because the robot 2 has became an attacker
even though it is farther from the ball than the robot 1. The
experimental result in such case indicates that the
mechanism really can arrange the robots and successfully
make them reach the correct destinations.
V. CONCLUSION
In this paper, a Petri net based role arrangement
mechanism for android robot soccer system is designed and
implemented in actual FIRA AndroSot game. In which, the
role arrangement supervisor is designed by Petri net and
then it is used to modeling what a robot should do in an
assigned role of attacking, defending and goalkeeping.
Besides, the optimal path of robotic movements are
determined and modified by A* algorithm. The simulation
and experimental results show that the proposed system has
obtained satisfactory role arrangement efficiency.
ACKNOWLEDGMENT
This work was supported by the National Science
Council, Taiwan, under grant number NSC99-2221-E-218021.
REFERENCES
[1]
Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, K.
Fujimura, “The intelligent ASIMO: system overview and
integration,” Proc. IEEE/RSJ International Conf. Intelligent Robots
and System, Vol. 3, Sept. 2002, pp. 2478-2483, doi:
10.1109/IRDS.2002.1041641.
[2] K. Noritake, S. Kato, T. Yamakita, H. Itoh, “A motion generation
system for humanoid robots - Tai Chi motion,” Proc. IEEE Symp.
Micromechatronics and Human Science, Oct. 2003, pp.265-269, doi:
10.1109/MHS.2003.1249946.
[3] M. Bowling, M. Veloso, “Motion control in dynamic multi-robot
environments,” Proc. IEEE Symp. Robotics and Automation, Nov.
1999, pp.168-173, doi: 10.1109/CIRA.1999.810044.
[4] FIRA, http://www.fira.net/
[5] J.-H. Kim, H.-S. Shim, H.-S. Kim, M.-J. Jung, 1.-H. Choi, J.-O.
Kim, “A Cooperative Multi-Agent System and Its Real Time
Application to Robot Soccer,” Proc. IEEE International Conf.
Robotics and Automation, Apr. 1997, vol. 1, pp.638-643, doi:
10.1109/ROBOT.1997.620108.
[6] E. W. Dijkstra,. “A note on two problems in connexion with graphs,”
Numerische Mathematik, vol. 1, no. 1, 1959, pp. 269–271, doi:
10.1007/BF01386390.
[7] T. Murata, “Petri nets: Properties, analysis and applications,”
Proceedings of the IEEE, vol. 77, no. 4, Apr. 1998, pp.541-580, doi:
10.1109/5.24143.
[8] M.C. Zhou and D. T. Wang, “Design of Petri net objects and their
application in command and control systems,” Proc. IEEE Int. Conf.
System Man Cybern., vol. 4, Oct. 1995, pp. 3463-3468, doi:
10.1109/ICSMC.1995.538322.
[9] I. Chabini and S. Lan, “Adaptations of the A* algorithm for the
computation of fastest paths in deterministic discrete-time dynamic
networks,” IEEE Trans. Intelligent Transportation Systems, vol. 3,
no. 1, Mar. 2002, pp. 60-74, doi: 10.1109/6979.994796.
[10] M. Fu and B. Xue, “A path planning algorithm based on dynamic
networks and restricted searching area,” Proc. IEEE Int. Conf. on
Automation and Logistics, Aug. 2007, pp. 1193-1198, doi:
10.1109/ICAL.2007.4338750.