SHU Strive Legends Team Description 2009
Zhang Shunxin, Lin Xiaoyuan, Fan Haiting, Xiang Zongjie, Chen Wanmi
Shanghai University, China
Abstract This paper describes the hardware and software of the Middle Size League team "SHU Strive Legends" for RoboCup 2009. We will discuss the hardware design, motion control, robot's vision system and AI software in this paper. Some improvements such as the new pneumatic kicker system and software will also be proposed.
1. Introduction
The Middle Size League team of the "SHU Strive Legends" was founded in 2004. It was developed in the base of RoboCup Small Size League which had won the champion in RoboCup Iran Open 2007 & 2008. In RoboCup world championship 2008, our team proceeded to the last 8 teams. In RoboCup China Open 2008, our Middle-Size robots had 45 goals and lost only 3 goals. We won the champion of the Technical Challenge and the 3rd place in the Middle-Size competition.
The basis for our success was the robust and reliable robot body, the powerful kicker device, efficient algorithms and team members' great passion. Our main research interests are well-mechanical structure for middle-size soccer robots, the development of improved sensor fusion and the development of learning robots.
In the following parts, we will describe the general hard-and software design of the "Legends3.0" and the recent developments of our robots. Finally, we will introduce our current research focuses.
2. Hardware
In order to solve many problems such as the architecture and the wheels, we designed our new robots (Legends3.0) by using the software UGnx5 in 2008. Each function of the robot is modularization for easily assembling and maintenance. We use homogeneous robot hardware architecture for all the robots in the team, based on an omnidirectional mobile platform. The mobile platform is built in a triangular configuration. This is one of many possible ways to arrange omnidirctional wheels to achieve an omni-directional behavior. The advantage of this configuration was analyzed in our TDP2008. Each wheel is driven by a Maxon DC motor (24V,150W).This allow our robot to move at high speeds up to 2.91m/s, and maximum acceleration up to 3.66 m/s².The mechanical assembly of our mobile base was developed to be easy and robust.
Pneumatic kicking device will be used as before. The robots are equipped with a pneumatic kicking device. The device consists of a pneumatic cylinder, a valve and a 2L Coca-Cola plastic bottle as pressure tank. The device is able to kick the ball with a velocity of 8m/s. Recently, we have designed a new pneumatic soccer kicker system. The fluidic muscle is used to drive a linkage, which can kick the ball with a velocity of 10m/s and lift it about 2m above the ground. The fluidic muscle is controlled by an electric-proportional valve instead of electric valve .This device can reinforce the kick power and use less pressured air. We believe that our robots will have more goals in 2009 by means of our new soccer kicker system.
3. Motion Control
The motion control system is based on Cyclone EP1C12 FPGA chip board. The velocity feedback is done by using 512 PPR digital incremental encoders. The velocity of the wheels is controlled by a microprocessor based DC motor controller which has a RS232 communication link with the host laptop. The controller reads the pulse gains from the motor encoders and produces amplified PWM output voltages for the motors based on a PID algorithm.
In 2009, our RS232 communication method will be removed. A more efficient and convenient method such as USB will be used. We can transfer an 8-bit data bidirectionally in the rate of 1M byte/second. Thus we can share the information between control board and laptop immediately.
A more robust PID algorithm for motor control will be discussed in our farther research. Sensor fusion is also our research focus. In 2008, the robot's self-localization is based on panoramic mirror vision system only. The problem of use one sensor (camera) only is that we may get wrong position in self-localization and the position will heavily affected by noise. In 2009, other kinds of sensors such as speedometer and digital compass will be equipped on our robot. Thus the robots are able to determine their position much more quickly and accurately. Sensor fusion will be done in our control board.
When the robot collides with other robots or slips on the green carpet, the actual velocity of the robot differs a lot from the desired velocity as well as from the velocity measured by the wheel encoders. To overcome that problem we also use a new kind of speedometer and developed an algorithm to estimate the motion of the robot.
4. Vision System
Our vision subsystem is composed of two parts, one is an omni-directional mirror based on a digital camera, the other is algorithm for image processing.
The omni-directional mirror is designed by our team members, as shown in figure 1. The mirror design has been split in two parts: the inner part is a curve which has established the mapping between scene points and pixels. Its capabilities to map scene distances, in any direction, in proportional image distances within the whole range covered by the mirror (our designed range is 6m); the outer part is constant curvature curve. Considering the inner mirror observes an angle which heads too low, in such a way that it cannot observe the higher part of the scene, this part can observe at a quite high height to distinguish the top markers in our future works.
5. Self-localization
Matrix [3] is mainly our self-localization method, which is highly accurate and robust against outliers. The algorithm is based on detected white lines. However, other sensors like odometry and digital compass help the localization process. Our robot is used compass to get the direction instead of detecting the door color. And considering odometry accumulative total errors, Odometry which speedup the self-localization is assistant. In sum, our self-localization shows sensors fusion, as shown is Figure 3
6. Robot Behavior and Cooperation
Our path-planner is relatively simple yet efficient. We use the famous artificial potential field algorithm. In brief, our robot is influenced by two forces. One is the attraction force generated by ball, the other is the repulsion force generated by the nearest obstacle. We will improve our path-planning so that it could learn by some degrees. It will significantly reduce people's debugging time if it could set parameters automatically.
Multi-robot cooperation is also simple. When one of our robots having the ball, only one robot could move and others keep silent at the same time so that our robots will not steal ball each other. And the communication among robots is necessary. Our robots only share their ball information. If none find the ball, some of robots will run cover the whole ground to detect the ball. Its practicality had proved in RoboCup 2008. We will add some cooperation between robots in free kick.
References
- Zongjie Xiang: Automatic Calibration of Camera to World Mapping in omnidirectional vision system using error descent algorithm, International Conference on Advanced Intelligence 2008.
- Felix von Hundelshausen, and Raul Rojas: Tracking Regions, in Daniel Polanietal.(editors): RoboCup-2003: Robot Soccer World Cup VII(Lecture Notes in Computer Science), Springer, 2004.
- Felix von Hundelshausen, Michael Schreiber, Fabian Wiesel, AchimLiers and Raul Rojas: MATRIX: A force field pattern matching method for mobile robots, Technical Report B-08-03, Freie Universität Berlin, Institute of Computer Science, Takustr. 9, 14195 Berlin, Germany.