by M Beetz, T Schmitt, R Hanek, S Buck, F Stulp, D Schröter and B Radig
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-theshelf 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.
Reference:
The AGILO Robot Soccer Team – Experience-based Learning and Probabilistic Reasoning in Autonomous Robot Control (M Beetz, T Schmitt, R Hanek, S Buck, F Stulp, D Schröter and B Radig), In Autonomous Robots, volume 17, 2004.
Bibtex Entry:
@article{beetz_agilo_2004,
author = {M Beetz and T Schmitt and R Hanek and S Buck and F Stulp and D Schröter and B Radig},
title = {The {AGILO} Robot Soccer Team – Experience-based Learning and Probabilistic
Reasoning in Autonomous Robot Control},
journal = {Autonomous Robots},
year = {2004},
volume = {17},
pages = {55–77},
number = {1},
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-theshelf 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.},
}