SEU-RedSun 2010 Soccer Simulation Team Description

Zhao Bolu, Guo Xiaxia, Shen Qian, Yi Yimin, Tan Yingzi

RoboCup Lab, Southeast University, Nanjing 210096, China

Abstract The purpose of SEU-RedSun is to develop a re-usable agent which can behave in a human-like way. This paper describes the main features of SEU-RedSun soccer 3D simulation team. SEU-RedSun improves itself to adapt the changes in RoboCup2010. This year our most work is making the location information more accurate, optimizing the basic skills such as walking and kicking, strengthen the cooperation between agents. At last, it describes the future work we will do.

Introduction

To get the ultimate goal of the RoboCup project, the RoboCup competition improved itself in different fields from the simulation platform to competition rules. And the influence of competition is increasing which is good for the research of robot.

As simspark is becoming more stable and real, it is easier for us to experiment on methods and algorithms for humanoid soccer behaviors. To close the reality, restricted vision preceptor is installed on NAO. On the other hand, to make developing more convenient, Accelerometer sensor which gives the information of linear acceleration was added, and sync mode was added.

There are two rules of the competition changed. One is that both length and width of playground expand to 1.5 size of original. The other is each team could send 6 agents instead of 3 agents. This means that the focus of the competition will turn to the aspect of strategy and cooperation from base skills. And this asks us to improve the autonomy and interactive of the agents. With the stability platform and base skills, the research of artificial intelligence will become the focus in the 3d simulation league.

SEU- RedSun has been established in 2005, and successfully attended several competitions. After changing into the humanoid simulation, most of the team's work has been spent on developing a powerful and developmental base code, primary skills, development tool and adapt strategy[1]. Our team got the champion in both RoboCup Iran Open 2009 and RoboCup 2009 Graz.

This paper describes the main features and implementation of SEU-RedSun. Section 2 describes our agent architecture. Section 3 discusses the humanoid behavior generation in dynamic and adversarial environment. Section 4 shows our future work.

Agent Architecture

Considering of that the simspark is still under development, and many new features will be added in the future, we try to make our team enough flexible to adapt new changes. So we use the plug-in architecture based on on-strict layered agent architecture with singleton modules [2]. With plug-in architecture, we could just change some modules to adapt the changing of sever (Fig. 1).

The modules in the green box are core modules, which are implemented as singleton. The blue and yellow modules around the green box are add-ons. The yellow ones will be described in this paper. The core modules are relatively stable. If the simspark and sever change, the add-ons can be replaced easily.

Humanoid Behavior Generation

Humanoid behavior generation is a key problem in humanoid robot research. A competitive team needs both personal ability and teamwork. An Agent's Personal ability mainly refers to robust basic skills including biped walking, running, kicking, getting-up, etc. The teamwork needs good cooperative strategy and communication. And the basic skills which we want to design are that agent should behave in a human-like way and that it could transplant to real robot easily.

Agent makes decision base on its WorldModel. So the agent must get accurate information about the playground, the ball, itself and teammates. As the restrict vision, we use Noise Filtration and Zoning to make localization work.

At strategy part, we use Rational Passing Decision based on Region (RPDR ) method which was used by our 3D sphere agent simulation. And we improve the communication model to support the strategy.

Localization with Restricted Vision

Noise Filtration

For the expanding of playground, the noise's influence increases. We use Kalman filter to solve this problem.

The Kalman filter is an efficient recursive filter that estimates the internal state of a linear dynamic system from a series of noisy measurements. [3]This means that only the estimated state from the previous time step and the current measurement are needed to compute the estimate for the current state. [3]

We impalement the Kalman filter to filter out the noise of the ball information, but it is impossible to figure out an accurate motion model of agent. Unlike the simulation of spherical agent, the driving force acted on agent is not controllable. Thus, Kalman filter is not useful for filtering the noise in self-localization, not to mention the noise in other agent's position.

Localization

According to our team's locating algorithm, vision perception of three flags is required for self-localization. Theoretically, only flags are enough, but agent needs to solve a quadratic equation in twelve unknowns [4]. As vision restrict, agent may not see enough flags to locate. They need to rotate their head to find enough flags. But rotating head costs much time. Therefore, we divide the playground in different regions. And in different region, the agent turns its head to right or left direction. So it can search the flags and the ball more quickly and do other motions smoothly.

Keeping Object in View

When a tank is running fast on a bumpy ground, Fire Control System can keep it aiming at target. A similar controller is implemented to keep some object in view when robot is moving, such as football and goal.

Communication

As the competition change to 6V6, the communication between agents becomes more important.

Say and Hear model were used send and receive message. Fig.2 shows our communication model. As messages are restricted to a maximal length (currently 20 bytes)[5], we design a message class to deal different sentence.

Rational Passing Decision based on Region

According paper [4],we divide playground as 11 region(Fig. 3). Three edges are left(L),center(C),right(R). Three rows are front(F),middle(M),back(B);and special region:Shoot(S) and Dangerous(D).

The calculate process as follow:

• (1)calculate different kinds of region;
• (2)calculate the intersection of the regions
• (3)select the target region.

Passing ball to a region instead of a point could decrease the calculation and increase the success.

Future Work

The results achieved by SEU-RedSun team are very hopeful. But there is much work to do in the future. Both basic skills and decision making will be the primary work of SEU-RedSun. On the one hand we should improve the stability of walking, decrease the time of adapting kicking, on the other hand, we should design a good formation and strategy.

As the new accelerometer sensor was added, we will try to combine gyro rate perceptor and accelerometer sensor to calculate the posture of the agent, so that the agent could work better.

Considering that the agent often fall after shooting, we should improve the accurate of vision and optimize the kicking.

As the number of teammates increased, we will adapt the position of agents and use the communication between agents to keep the balance of offensive and defensive.

At the same time, we are keeping on developing the development tool and improving the server in the future.

References

1. Chen Si, Zhou Yanjun, Yi Yimin and Tan Yingzi, SEU-RedSun 2009 Soccer Simulation Team Description, .In RoboCup 2009: RoboCup IranOpen 2009, Iran, April,2009.
2. Xu Yuan, Jiang Chunlu, and Tan Yingzi. Seu-3d 2006 soccer simulation team description. In RoboCup 2006: Robot Soccer World Cup X, Bremen, Germany, June 2006.
3. WIKIPEDIA , http://en.wikipedia.org/wiki/Kalman_filter
4. Xu Yuan. Research of robocup humanoid soccer simulation robot the design and development of seu-redsun soccer simulation team. Master's thesis, Southeast University, Nanjing, China, 2008. (in Chinese).
5. Joschka Boedecker, Klaus Dorer, Markus Rollmann, Yuan Xu, Feng Xue, Marian Buchta, Hedayat Vatankhah, SimSpark User's Manual, Version 1.2, Page 17-18.
6. Xu Yuan, Shen Hui, Qian Cheng, Chen Si, and Tan Yingzi, SEU-RedSun 2008 Soccer Simulation Team Description.In RoboCup 2008: RoboCup IranOpen 2008, Iran, April,2008.