Wavelet-based feature extraction for behavior recognition in mobile robots

Xin Jin, Kushal Mukherjee, Shalabh Gupta, Asok Ray

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper introduces a dynamic data-driven method for behavior recognition in mobile robots. The core concept of the paper is built upon the principle of symbolic dynamic filtering (SDF) that is used to extract relevant information in complex dynamical systems. The objective here is to identify the robot behavior from time-series data of piezoelectric sensor signals from the pressure sensitive floor in a laboratory environment. A symbolic feature extraction method is presented by partitioning of two-dimensional wavelet images of sensor time-series data. The K-nearest neighbors (k-NN) algorithm is used to identify the patterns extracted by SDF. The proposed method is validated by experimentation on a networked robotics test bed to detect and identify the type and motion profile of mobile robots.

Original languageEnglish (US)
Title of host publicationASME 2010 Dynamic Systems and Control Conference, DSCC2010
Pages875-882
Number of pages8
DOIs
StatePublished - 2010
EventASME 2010 Dynamic Systems and Control Conference, DSCC2010 - Cambridge, MA, United States
Duration: Sep 12 2010 → Sep 15 2010

Publication series

NameASME 2010 Dynamic Systems and Control Conference, DSCC2010
Volume1

Other

OtherASME 2010 Dynamic Systems and Control Conference, DSCC2010
CountryUnited States
CityCambridge, MA
Period9/12/10 → 9/15/10

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Access to Document

Fingerprint Dive into the research topics of 'Wavelet-based feature extraction for behavior recognition in mobile robots'. Together they form a unique fingerprint.

Cite this