Local Movement Control with Neural Networks on Autonomous Robots

In the RoboCup small-size league, the positions of the robots are commonly calculated via a camera mounted above the field and different kinds of artificial intelligence methods hosted on a PC outside the field. In this case, the robot´s position must be predicted because of the various time delays until the position data arrived at the robot. This paper focuses on the use of local sensors attached to the robot, and a neural network to estimate the actual robot position. This paper shows how local sensors can compensate for the effect of latency times and how the actual robot position can be estimated. Slip and friction effects that cannot be measured with local sensors can be adjusted by a neutral network, for which the implementation is adapted to the low resources available on the robot.