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DreamWing3D3.0 Soccer Simulation Team Description Paper

Weixiang Wang

Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education College of Computer Science and Technology Anhui University Hefei, Anhui, China

Abstract We continue to carry on the further studied about the edition of DreamWing3D3.0 on the base of DreamWing3D2.0. DreamWing3D has won the 4th in Robocup ChinaOpen 2010. In this paper, we mainly present the structure and the techniques of our team.

Introduction

The DreamWing RoboCup Team, starting with only the 2D soccer simulation team, was established in 2007, and then The DreamWing RoboCup Team with 3D soccer simulation team was established in 2008. Now we have focused on Robocup 3D Simulation after two years development. Finally, it had won the third place in Robocup ChinaOpen 2009 ,the 4th place in that of 2010 and then won the top8 in Singapore .

Team Structure

In 2009 we have reconstructed architecture of our agent code and improved the reusability and scalability of it. We divide program into several functional modules: the network connection, message parsing, world model, agent model, mathematical calculations, advanced strategies, advanced behavior, visual model, basic movements, modeling calculations.

From the perspective of artificial intelligence, the most important module is highlevel strategy.

From the perspective of robot, the most important module is the basic movements and modeling calculations

High-level strategy reflects the response of the agent body under different circumstances. Basic movements mainly package a large number of the module robot's basic actions, such as stand up, play, etc.

Modeling calculation module can obtain the optimal value of walking and other movements primarily by way of kinematic model calculation.

The relationship of the various modules of the agents process has shown a clear hierarchical structure, which is shown in Figure 1

Fig. 1. Module Structure
Fig. 1. Module Structure

Application Techniques

(This is a section header with subsections below)

Look for the Ball

When the agent is standing, it always keeps facing the ball. If the visual information can not receive the ball information in the six cycles then the ball is not within visual range, so joint rotation to the head until regaining the ball visual information. And turn the body makes the ball in the visual center.

Fig. 2. Agent Vision
Fig. 2. Agent Vision

Agent's Position

In the case of a person's height is unknown, to calculate one's position needs to see three same height of flags not on the same side at least. It means that must be able to get the three corner's flags, or three goal's flags of the polar coordinates.

Fig. 3. Agent Position
Fig. 3. Agent Position

Posture

The judgment of standing and downing base on ZMP and gyroscope: It is difficult to calculate relative changes in the world coordinate system matrix ( ) of Torse in the local visual model. So ZMP is used here to determine whether the Agent is hovering, and judge the Agent fall direction through the previous accumulated directions of gyroscope.

3-D Inverted Pendulum

The walking movements of DreamWing2.0 version are manual debugging, whose shortcomings are walking movements instability and slowly. This version uses a dynamic walking algorithm based on the three-dimensional inverted pendulum.

The robot simplified is shown in Figure 4: three-dimensional inverted pendulum model.

Fig.4. 3-D Inverted Pendulum
Fig.4. 3-D Inverted Pendulum

3D Gait Pattern

Traditional gait planning is generally considered two-dimensional space, which are the forward and backward. But in many cases, the Robocup 3D Simulation needs to walk in any direction, like the left post, diagonal etc. In DreamWing3D3.0 we used 3D gait planning algorithm, which makes agent to move in any direction without turn around, so it greatly improves the efficiency of the movement.

Multi-Agents

In the bottom, to obtain other agent body positions through the propaganda and visual. In the high-level strategy module, to collaborate with multi-agents by the location information, which we have previously obtained .

Conclusion

As is shown above, we spent a lot of time in improving the low level implementation details and high level decision making models. We also used a lot of experiments to evaluate our improvements and observed that they are efficient and useful and the results of them are good. All of features above enhance the strength of our team.

We will try to make more progress along the line that we have described in this paper.

References

  1. Shuuji Kajita, Hirohisa Hirukawa, et al, Humanoid Robots. Tsinghua University Press. Vol.1 Mar 2007.
  2. Saeed B. Niku. Introduction to Robotics Analysis, Systems, Applications. Publishing House of Electronics Industry. Vol.2 Nov 2006.
  3. Tan Min, Xu De, et al, Advanced Robot Control. Higher Education Press. Vol.1 May 2007.