Most multi-robot navigation planning methods make assumptions about the kind of navigation problems they are to solve and the capabilities of the robots they are to control. In this paper, we propose to select problem-adequate navigation planning methods based on empirical investigations, that is, the robots should learn by experimentation to use the best planning methods. To support this development strategy we provide software tools that enable the robots to automatically learn predictive models for the performance of different navigation planning methods in a given application domain. We show, in the context of robot soccer, that the hybrid planning method which selects planning methods based on a learned predictive model outperforms the individual planning methods. The results are validated in extensive experiments using a realistic and accurate robot simulator that has learned the dynamic model of the real robots.
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