CS Freiburg: coordinating robots for successful soccer playing

The aim of this work is to contribute some insights and a partial overview of how machine learning methods are used in robotics. We first discuss typical general issues in the relationship between robotics and machine learning. Then we focus on projects associated with the RoboCup competition and symposium, and review the extent to which machine learning approaches have been used in the 4-legged league at RoboCup during the years 1998-2003. Further, we summarise the machine learning methods that were used by our own RoboCup team-the NUbots-in 2002NUbots-in /2003

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“…This has led to the development of interesting new methods and spin-off projects, see e.g. (Weigel et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Four-Legged League

Chalup

Murch

2004

IFAC Proceedings Volumes

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“…In the RoboCup Middle-Size competitions, an approach based only on laser rangefinders was used, very effectively, by the CS Freiburg Team [27]. They extracted the lines of the walls from the laser scans and matched them against a model of the field of play.…”

Section: Related Workmentioning

confidence: 99%

Omnidirectional vision scan matching for robot localization in dynamic environments

et al. 2006

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Abstract-The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.

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“…They are nevertheless more efficient to deal with real-word, non structured scenarios: since decisions are taken locally, the system is still able to work when communication is not available, and -in general -allow a quicker, real-time response and a higher fault-tolerance in a dynamically changing environment. Most teams in RoboCup share these considerations, even if counterexamples exist (Weigel et al, 2002). Notice also that, a part from optimality, univocally accepted metrics or criteria to compare the performance of centralized and distributed Multi Robot systems are missing; the problem is raised, for example, in .…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Systems and Distributed Intelligence: The ETHNOS Approach to Heterogeneity

Sgorbissa¹

2006

Mobile Robotics, Moving Intelligence

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CS Freiburg: coordinating robots for successful soccer playing

Cited by 112 publications

References 35 publications

Machine Learning in the Four-Legged League

Machine Learning in the Four-Legged League

Omnidirectional vision scan matching for robot localization in dynamic environments

Multi-Robot Systems and Distributed Intelligence: The ETHNOS Approach to Heterogeneity

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