Thilo Weigel

CS Freiburg: coordinating robots for successful soccer playing

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. The paper presents the CS Freiburg team, the winner in the middle-size league at RoboCup 1998, 2000, and 2001. The paper focuses on multiagent coordination for both perception and action. The contributions of the paper 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 robots to respond to a large range of situations in an appropriate way, and 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 multirobot group which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multirobot field.

A fast, accurate and robust method for self-localization in polygonal environments using laser range finders

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 none of the existing approaches met 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 also present experimental evidence that our method outperforms other self-localization methods in the RoboCup environment.

The CS Freiburg Robotic Soccer Team: Reliable Self-Localization, Multirobot Sensor Integration, and Basic Soccer Skills

Robotic soccer is a challenging research domain because problems in robotics, artificial intelligence, multi-agent systems and real-time reasoning have to be solved in order to create a successful team of robotic soccer players. In this paper, we describe the key components of the CS Freiburg team. We focus on the self-localization and object recognition method based on using laser range finders and the integration of all this information into a global world model. Using the explicit model of the environment built by these components, we have implemented path planning, simple ball handling skills and basic multi-agent cooperation. The resulting system is a very successful robotic soccer team, which has not lost any game yet.

KiRo – An Autonomous Table Soccer Player

This paper presents KiRo – a system capable of playing table soccer on a competitive level and in a fully autonomous way. It can serve a human both as a teammate and an opponent but also allows for matches between two artificial players. KiRo introduces the table soccer game as a new domain for the research in the fields of robotics and artificial intelligence.

The CS Freiburg Team Playing Robotic Soccer Based on an Explicit World Model

Robotic soccer is an ideal task to demonstrate new techniques and explore new problems. Moreover, problems and solutions can easily be communicated because soccer is a well-known game. Our intention in building a robotic soccer team and participating in RoboCup-98 was, first, to demonstrate the usefulness of the self-localization methods we have developed. Second, we wanted to show that playing soccer based on an explicit world model is much more effective than other methods. Third, we intended to explore the problem of building and maintaining a global team world model. As has been demonstrated by the performance of our team, we were successful with the first two points. Moreover, robotic soccer gave us the opportunity to study problems in distributed, cooperative sensing.

CS Freiburg 2001

The CS Freiburg team has become F2000 champion the third time in the history of RoboCup. The success of our team can probably be attributed to its robust sensor interpretation and its team play. In this paper, we will focus on new developments in our vision system, in our path planner, and in the cooperation component.

Decision-Theoretic Planning for Playing Table Soccer

Table soccer (also called “foosball”) is much simpler than real soccer. Nevertheless, one faces the same challenges as in all other robotics domains. Sensors are noisy, actions must be selected under time pressure and the execution of actions is often less than perfect. One approach to solve the action selection problem in such a context is decision-theoretic planning, i.e., identifying the action that gives the maximum expected utility. In this paper we present a decision-theoretic planning system suited for controlling the behavior of a table soccer robot. The system employs forward-simulation for estimating the expected utility of alternative action sequences. As demonstrated in experiments, this system outperforms a purely reactive approach in simulation. However, this superiority of the approach did not extend to the real soccer table.

Self-localization in dynamic environments based on laser and vision data

For a robot situated in a dynamic real world environment the knowledge of its position and orientation is very advantageous and sometimes essential for carrying out a given task. Particularly, one would appreciate a robust, accurate and efficient self-localization method which allows a global localization of the robot. In certain polygonal environments a laser based localization method is capable of combining all these properties by correlating observed lines with an a priori line model of the environment [J. Gutmann et al., 2001]. However, often line features can rather be detected by a vision system than by a laser range finder. For this reason we propose an extension of the laser based approach for the simultaneous use with lines detected by an omni-directional camera. The approach is evaluated in the RoboCup domain and experimental evidence is given for its robustness, accuracy and efficiency, as well as for its capability of global localization.

KiRo – A Table Soccer Robot Ready for the Market

This paper presents the autonomous table soccer robot KiRo. KiRo provides a competitive challenge for even advanced human players and is well suited as a toy or even as a training partner for professional players. Moreover, the table soccer game represents a demanding testbed for evaluating a multitude of techniques and approaches in the fields of robotics and artificial intelligence. KiRo has reached a technically mature level and will be commercially available by January 2005.

Behavior recognition and opponent modeling for adaptive table soccer playing

We present an approach for automatically adapting the behavior of an autonomous table soccer robot to a human adversary. Basic actions are recognized as they are performed by the human player, and characteristic action observations are used to establish a model of the opponent. Based on this model, the opponents playing skills are classified with respect to different levels of expertise and particular offensive and defensive skills are assessed. In response to the knowledge about the opponent, the robot adapts the velocities at which it attacks and defends in order to provide entertaining games for a wide range of human players with different playing skills. Experiments with two different table soccer robots validate our approach.