Navigation Behavior Selection Using Generalized Stochastic Petri Nets for a Service Robot

Appropriate design and control of behaviors of mobile robots are important for their successful autonomous navigation in a real dynamic environment. This paper proposes a formal selection framework of multiple navigation behaviors for a service robot. In the presented approach, modeling, analysis, and performance evaluation are carried out based on generalized stochastic Petri nets (GSPNs). By adopting a probabilistic approach, the proposed framework helps the robot to select the most desirable navigation behavior in run time according to environmental conditions. Moreover, after mission completion, the robot evaluates its prior navigation performance from accumulated data, and automatically uses the results to improve its future operations. Also, GSPNs have several advantages over direct use of other modeling formalisms such as finite state automata (FSA) or Markov processes (MPs). We conduct experiments on real guidance tasks with visitors by implementing the framework in the guide robot Jinny at the National Science Museum of Korea. The results show that the proposed strategy is useful for a robot's selection of an appropriate navigation behavior in a dynamic environment.