title: Symbolic dynamic filtering and language measure for behavior identification of mobile robots.
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.
authors: Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA. mr.goutham@gmail.com
authors: Goutham
authors: G
authors: Mallapragada
authors: Asok
authors: A
authors: Ray
authors: Xin
authors: X
authors: Jin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods*