Paper Info
Title
Behavior recognition in mobile robots using Symbolic Dynamic Filtering and language measure
Abstract
This paper addresses dynamic data-driven signature detection in mobile robots. The core concept of the paper is built upon the principles of Symbolic Dynamic Filtering (SDF) that has been recently reported in literature for extraction of relevant information (i.e., features) in complex dynamical systems. The objective here is to identify the robot behavior in real time as accurately as possible. Two different approaches to classifier design are presented in the paper; the first one is Bayesian and the second is based on measures of formal languages. The proposed methods have been experimentally validated on a networked robotic test-bed to detect and identify the type and motion profile of the robots under consideration.
Year
DOI
Venue
2009
10.1109/ACC.2009.5160145
ACC'09 Proceedings of the 2009 conference on American Control Conference
Keywords
DocType
ISSN
Bayes methods,filtering theory,formal languages,mobile robots,pattern classification,Bayesian approach,behavior recognition,classifier design,complex dynamical systems,dynamic data-driven signature detection,formal languages,language measure,mobile robots,networked robotic testbed,relevant information extraction,symbolic dynamic filtering
Conference
0743-1619 E-ISBN : 978-1-4244-4524-0
ISBN
Citations 
PageRank 
978-1-4244-4524-0
0
0.34
References 
Authors
2
2
Name
Order
Citations
PageRank
Goutham Mallapragada1233.07
Ray, A.2832184.32