Abstract-Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. This paper presents the CS Freiburg team, the winner in the middle size league at RoboCup 1998, 2000 and 2001. The paper focuses on multi-agent coordination for both perception and action. The contributions of this work are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment, a rich set of basic skills allowing to respond to large range of situations in an appropriate way, an action selection method based on behavior networks as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multi-robot group, which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multi-robot field.

Self-localization is important in almost all robotic tasks. For playing an aesthetic and effective game of robotic soccer, self-localization is a necessary prerequisite. When we designed our robotic soccer team for participating in robotic soccer competitions, it turned out that all existing approaches did not meet our requirements of being fast, accurate, and robust. For this reason, we developed a new method, which is presented and analyzed in this paper. This method is one of the key components and is probably one of the explanations for the success of our team in national and international competitions. We present also experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.

No abstract

No abstract

Abstract. The success of CS Freiburg at RoboCup 2000 can be attributed to an effective cooperation between players based on sophisticated soccer skills and a robust and accurate self-localization method. In this paper, we present our multiagent coordination approach for both, action and perception, and our rich set of basic skills which allow to respond to a large range of situations in an appropriate way. Furthermore our action selection method based on an extension to behavior networks is described. Results including statistics from CS Freiburg final games at RoboCup 2000 are presented.