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.
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Buck, S., Beetz, M., Schmitt, T. (2002). Planning and Executing Joint Navigation Tasks in Autonomous Robot Soccer. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds) RoboCup 2001: Robot Soccer World Cup V. RoboCup 2001. Lecture Notes in Computer Science(), vol 2377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45603-1_12
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DOI: https://doi.org/10.1007/3-540-45603-1_12
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