Predicting Away Robot Control Latency

Sven Behnke, Anna Egorova, Alexander Gloye, Raśl Rojas, Mark Simon

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.