Symbolic Dynamic Filtering and Language Measure for Behavior Identification of Mobile Robots

Author

Mallapragada, Goutham ; Ray, Asok ; Jin, Xin

Author_Institution

Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA

Volume

42

Issue

3

fYear

2012

fDate

6/1/2012 12:00:00 AM

Firstpage

647

Lastpage

659

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.

Keywords

Bayes methods; feature extraction; filtering theory; mobile robots; pattern classification; principal component analysis; risk analysis; time series; Bayesian risk analysis; behavior identification; behavior patterns; domain knowledge; feature extraction; language measure theory; mobile robots; pattern classification; principal component analysis; symbolic dynamic filtering; time series; Feature extraction; Mobile robots; Principal component analysis; Time series analysis; Vectors; Weight measurement; Feature extraction; language measure; pattern classification; robotic signatures; symbolic dynamic filtering (SDF); Algorithms; Artificial Intelligence; Bayes Theorem; Computer Simulation; Decision Support Techniques; Models, Theoretical; Motion; Pattern Recognition, Automated; Robotics;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/TSMCB.2011.2172419

Filename

6069609