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Michael Beetz, Thorsten Schmitt, Robert Hanek, Sebastian Buck, Freek Stulp, Derik Schröter, and Bernd Radig. The AGILO 2001 Robot Soccer Team: Experience-based Learning and Probabilistic Reasoning in Autonomous Robot control. Autonomous Robots, special issue on Analysis and Experiments in Distributed Multi-Robot Systems, 17(1):55–77, July 2004.

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Abstract

This article describes the computational model underlying the Agilo autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot's belief state with respect to the current game situation using a simple off-the-shelf camera system. The estimated game state comprises the positions and dynamic states of the robot itself and its teammates as well as the positions of the ball and the opponent players. Employing sophisticated probabilistic reasoning techniques and exploiting the cooperation between team mates, the robot can estimate complex game states reliably and accurately despite incomplete and inaccurate state information. The action selection module selects actions according to specified selection criteria as well as learned experiences. Automatic learning techniques made it possible to develop fast and skillful routines for approaching the ball, assigning roles, and performing coordinated plays. The paper discusses the computational techniques based on experimental data from the 2001 robot soccer world championship.

BibTeX Entry

@Article{beetz04agilo,
  author =            {Michael Beetz and Thorsten Schmitt and Robert Hanek and Sebastian Buck and
                       Freek Stulp and Derik Schr\"oter and Bernd Radig},
  title =             {The {AGILO} 2001 Robot Soccer Team: Experience-based Learning and
                       Probabilistic Reasoning in Autonomous Robot control},
  journal =           {Autonomous Robots, special issue on Analysis and Experiments in Distributed Multi-Robot Systems},
  volume =            {17},
  number =            {1},
  pages =             {55-77},
  month =             {July},
  year =              {2004},
  pdf =               {http://www9.in.tum.de/papers/2004/AR-2004-AGILO.pdf},
  url =               {http://citeseer.ist.psu.edu/beetz04agilo.html},
  bib2html_pubtype =  {Journal},
  bib2html_rescat =   {Robotics - RoboCup},
  abstract =          {This article describes the computational model underlying
  the Agilo autonomous robot soccer team, its implementation, and our
  experiences with it. According to our model the control system of an
  autonomous soccer robot consists of a probabilistic game state estimator and a
  situated action selection module. The game state estimator computes the
  robot's belief state with respect to the current game situation using a simple
  off-the-shelf camera system. The estimated game state comprises the positions
  and dynamic states of the robot itself and its teammates as well as the
  positions of the ball and the opponent players.  Employing sophisticated
  probabilistic reasoning techniques and exploiting the cooperation between team
  mates, the robot can estimate complex game states reliably and accurately
  despite incomplete and inaccurate state information. The action selection
  module selects actions according to specified selection criteria as well as
  learned experiences. Automatic learning techniques made it possible to develop
  fast and skillful routines for approaching the ball, assigning roles, and
  performing coordinated plays.  The paper discusses the computational
  techniques based on experimental data from the 2001 robot soccer world
  championship.}
}

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