Predicting Away Robot Control Latency

Sven Behnke², Anna Egorova¹, Alexander Gloye¹, Raúl Rojas¹, and Mark Simon¹

¹Freie Universität Berlin, Institute for Computer Science, Takustraße 9, 14195 Berlin, Germany

²International Computer Science Institute, 1947 Center St., Berkeley, CA, 94704, USA
http://www.fu-fighters.de

Abstract. This paper describes a method to reduce the effects of the system immanent control delay for the RoboCup small size league. It explains how we solved the task by predicting the movement of our robots using a neural network. Recently sensed robot positions and orientations as well as the most recent motion commands sent to the robot are used as input for the prediction. The neural network is trained with data recorded from real robots.

We have successfully field-tested the system at several RoboCup competitions with our FU-Fighters team. The predictions improve speed and accuracy of play.

LNAI 3020, p. 712 ff.

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