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AUV behavior recognition using behavior histograms, HMMs, and CRFs

Published online by Cambridge University Press:  10 February 2014

Michael Novitzky ,
Charles Pippin ,
Thomas R. Collins ,
Tucker R. Balch  and
Michael E. West
Michael Novitzky*
Affiliation:
College of Computing, The Georgia Institute of Technology, Atlanta, GA, USA
Charles Pippin
Affiliation:
College of Computing, The Georgia Institute of Technology, Atlanta, GA, USA
Thomas R. Collins
Affiliation:
Electrical Engineering, The Georgia Institute of Technology, Atlanta, GA, USA
Tucker R. Balch
Affiliation:
College of Computing, The Georgia Institute of Technology, Atlanta, GA, USA
Michael E. West
Affiliation:
Georgia Tech Research Institute, Atlanta, GA, USA
*
*Corresponding author. E-mail: misha@gatech.edu
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Summary

This paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

Keywords

Autonomous underwater vehiclesMulti-robot systemsBehavior recognitionAuction-based methods

Information

Type
Articles
Information
Robotica , Volume 32 , Issue 2: Distributed Autonomous Robotic Systems (DARS). , March 2014 , pp. 291 - 304
Copyright
Copyright © Cambridge University Press 2014 

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