by M Beetz, S Buck, R Hanek, T Schmitt and B Radig
Abstract:
This paper describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. The most salient aspects of the AGILO control software are that it includes (1) a cooperative probabilistic game state estimator working with a simple off-the-shelf camera system; (2) a situated action selection module that makes amble use of experience-based learning and produces coherent team behavior even if inter-robot communication is perturbed; and (3) a playbook executor that can perform preprogrammed complex soccer plays in appropriate situations by employing plan-based control techniques. The use of such sophisticated state estimation and control techniques distinguishes the AGILO software from many others applied to mid-size autonomous robot soccer. The paper discusses the computational techniques and necessary extensions based on experimental data from the 2001 robot soccer world championship.
Reference:
The AGILO Autonomous Robot Soccer Team: Computational Principles, Experiences, and Perspectives (M Beetz, S Buck, R Hanek, T Schmitt and B Radig), In International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS) 2002, 2002.
Bibtex Entry:
@inproceedings{beetz_agilo_2002,
author = {M Beetz and S Buck and R Hanek and T Schmitt and B Radig},
title = {The {AGILO} Autonomous Robot Soccer Team: Computational Principles,
Experiences, and Perspectives},
booktitle = {International Joint Conference on Autonomous Agents and Multi Agent
Systems ({AAMAS)} 2002},
year = {2002},
pages = {805–812},
address = {Bologna, Italy},
abstract = {This paper describes the computational model underlying the {AGILO}
autonomous robot soccer team, its implementation, and our experiences
with it. The most salient aspects of the {AGILO} control software
are that it includes (1) a cooperative probabilistic game state estimator
working with a simple off-the-shelf camera system; (2) a situated
action selection module that makes amble use of experience-based
learning and produces coherent team behavior even if inter-robot
communication is perturbed; and (3) a playbook executor that can
perform preprogrammed complex soccer plays in appropriate situations
by employing plan-based control techniques. The use of such sophisticated
state estimation and control techniques distinguishes the {AGILO}
software from many others applied to mid-size autonomous robot soccer.
The paper discusses the computational techniques and necessary extensions
based on experimental data from the 2001 robot soccer world championship.},
}