Planning and executing joint navigation tasks in autonomous robot soccer

Abstract

In this paper we propose a hybrid navigation planning and execution system for performing joint navigation tasks in autonomous robot soccer. The proposed system consists of three components: an artificial neural network controller, a library of software tools for planning and plan merging, and a decision module that selects the appropriate planning and execution methods in a situation-specific way. The system learns by experimentation predictive models for the performance of different navigation planning methods. The decision module uses the learned predictive models to select the most promising planning method for the given navigation task. In extensive experiments using a realistic and accurate robot simulator that has learned the dynamic model of the real robots we show that our navigation system is capable to (1) generate fast and smooth navigation trajectories and (2) outperform the state of the art planning methods. © 2002 Springer-Verlag Berlin Heidelberg.