Symbolic dynamic filtering and language measure for behavior identification of mobile robots

Goutha Mallapragada, Asok Ray, Xin Jin

    Research output: Contribution to journalArticle

    18 Citations (Scopus)

    Abstract

    This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification.

    Original languageEnglish (US)
    Article number6069609
    Pages (from-to)647-659
    Number of pages13
    JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
    Volume42
    Issue number3
    DOIs
    StatePublished - Jan 1 2012

    Fingerprint

    Risk analysis
    Principal component analysis
    Mobile robots
    Pattern recognition
    Feature extraction
    Time series
    Robotics
    Language
    Robots
    Bayes Theorem
    Principal Component Analysis

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Electrical and Electronic Engineering
    • Computer Science Applications
    • Human-Computer Interaction
    • Information Systems
    • Software
    • Medicine(all)

    Cite this

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    Symbolic dynamic filtering and language measure for behavior identification of mobile robots. / Mallapragada, Goutha; Ray, Asok; Jin, Xin.

    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42, No. 3, 6069609, 01.01.2012, p. 647-659.

    Research output: Contribution to journalArticle

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