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There have been two major streams for the motion control of mobile robots. The first is the model-based deliberate control and the second is the sensor-based reactive control. Since two schemes have complementary advantages and disadvantages, one cannot completely replace the other. There are a variety of environmental conditions which affect the navigation performances. The main idea of this paper is to design discrete navigation behaviors and to integrate behaviors by an appropriate selection framework. In this paper, we propose a behavior selection framework using the GSPN (Generalized Stochastic Petri Nets). We have designed two navigation behaviors which show completely different performances. In order to define behavior selection criteria, two kinds of navigation statuses are defined to monitor navigation performances of the robot. The proposed navigation strategy is simulated using the open source simulator Player/Stage to investigate the performances in a variety of conditions. Through the simulations, it was made clear that different behaviors show remarkably different performances. Moreover, the average navigation time of the proposed behavior selection framework is significantly decreased than that of any single navigation scheme DWA or tracking.