A selection framework of multiple navigation primitives using Generalized Stochastic Petri Nets

Gunhee Kim, Woojin Chung, Munsang Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    12 Citations (Scopus)

    Abstract

    This paper proposes a selection framework of multiple navigation primitives for a service robot using Generalized Stochastic Petri Nets (GSPN's). By adopting probabilistic approach, our framework helps the robot to select the most desirable navigation primitive in run time through the performance estimation according to environmental conditions. Moreover, after a mission, the robot evaluates prior navigation performance from accumulated data, and uses the results for the improvement of future operations. Modeling, analysis, and performance evaluation are conducted on firm mathematical foundation. Also, GSPN's have several advantages over classic automata or direct use of Markov Process. We conducted simulations of the model derived from our experience of practical installations. The results showed that the framework is useful for primitive selection and performance analysis.

    Original languageEnglish
    Title of host publicationProceedings of the 2005 IEEE International Conference on Robotics and Automation
    Pages3790-3795
    Number of pages6
    DOIs
    Publication statusPublished - 2005
    Event2005 IEEE International Conference on Robotics and Automation - Barcelona, Spain
    Duration: 2005 Apr 182005 Apr 22

    Publication series

    NameProceedings - IEEE International Conference on Robotics and Automation
    Volume2005
    ISSN (Print)1050-4729

    Other

    Other2005 IEEE International Conference on Robotics and Automation
    Country/TerritorySpain
    CityBarcelona
    Period05/4/1805/4/22

    Keywords

    • A guide robot
    • Behavior selection
    • Generalized Stochastic Petri Nets
    • Markov process
    • Mobile robot navigation

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Artificial Intelligence
    • Electrical and Electronic Engineering

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