Abstract
Appropriate design and control of behaviors of mobile robots are important for their successful autonomous navigation in a real dynamic environment. This work 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 the 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 a 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.
Fig.1 shows the resultant GSPNs model of two navigation behaviors, AutoMove and Contour tracking. In our model, three tokens are exploited to represent the statuses of the localizer, path planner and behavior, respectively. To perform analytic evaluations of GSPNs designs, we need to obtain an embedded Markov chain (EMC). Fig.1.(c) shows the EMCs induced from the rechability graph of Fig.1.(b), which is derived from the GSPNs model of Fig.1.(a).
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(a) A GSPN model for performance estimation
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(b) A derived model - The reachability graph
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(c) A derived model - The reduced embedded Markov
chain
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Fig.1. A GSPN model of navigation behaviors
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The developed framework is tested on the Jinny, the guide robot
of the National Science Museum of Korea. Fig.2.(a) shows a target
workspace, one of sections popular among visitors in the museum. The
mission is to navigate from the start point (8.1, 6.4) to the goal (8.5,
27.5).
Fig.2.(b)-(d) shows the detailed results of the behavior transitions
during the guide. The robot initially starts with AutoMove. At
point A, the robot turns its motion to Contour tracking when
the localization Warning is detected. At this time, many people
were around the robot, and the robot was located far from the wall.
Fig.2.(b) shows the resultant trajectory. Fig.7.(c) is a typical example
of the localizer Success, and Fig.7.(d) shows an instance of
the localizer Warning. They contain the information about the
local map, laser scan data, sample distributions, and an estimated position
of each calculation. As shown in these pictures, the environment is
very crowded and dynamic due to visitors.
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(a) An experimental environment
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(b) The resultant trajectory and behavior changes |
(c) Localization result during AutoMove
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(d) Localization result during Contour tracking
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Fig.2. Experimental results during one execution
of a guide task
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Publication
1. Gunhee Kim, and Woojin Chung, "Navigation Behavior Selection Using Generalized Stochastic Petri Nets (GSPN) for a Service Robot," IEEE Transactions on Systems, Man and Cybernetics Part C (SCI), vol.37, no.4, July 2007.
2. Gunhee Kim, Woojin Chung, Sung-Kee Park, and Munsang Kim, "Experimental Research of Navigation Primitive Selection Using Generalized Stochastic Petri Nets (GSPNs) for a Tour-Guide Robot", Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), pp.1392-1398, Alberta, Canada, August 2-6, 2005.
3. Gunhee Kim, Woojin Chung, and Munsang Kim, "A Selection Framework of Multiple Navigation Primitives Using Generalized Stochastic Petri Nets", Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), pp.3801-3806, Barcelona, Spain, April 18-22, 2005.
Funding
- Development of a Silver Mate Robot Platform: The Intelligent Robotics
Development Program, one of the 21st Century Frontier R&D Programs
(Oct. 2003 ~ Jul. 2006)
- Development of Science Museum Guide Robots (Oct. 2003 ~ Feb. 2005).
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