Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-18T21:49:47.740Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Dudek, G., Jenkin, M., Milios, E. and Wilkes, D., “A taxonomy for multi-agent robotics,” Auton. Robots 3 (4), 375–397 (1996).CrossRefGoogle Scholar
2.Cao, Y., Fukunaga, A. and Kahng, A., “Cooperative mobile robotics: antecedents and directions,” Auton. Robots 4 (1), 7–27 (1997).CrossRefGoogle Scholar
3.Arkin, R. C., Behavior-Based Robotics, ch. 9. (MIT Press, Cambridge, MA, 1998).Google Scholar
4.Dias, M. and Stentz, A., “A free market architecture for distributed control of a multirobot system,” Proceedings of the 6th International Conference on Intelligent Autonomous Systems (IAS-6) (Jul. 2000) pp. 115–122.Google Scholar
5.Gerkey, B. and Mataric, M., “Sold!: Auction methods for multirobot coordination,” IEEE Trans. Robot. Autom. 18 (5), 758–768 (2002).CrossRefGoogle Scholar
6.Sariel, S., Balch, T. and Erdogan, N., “Naval mine countermeasure missions,” IEEE Robot. Autom. Mag. 15 (1), 45–52 (2008).CrossRefGoogle Scholar
7.Sotzing, C. and Lane, D., “Improving the coordination efficiency of limited-communication multi-autonomus underwater vehicle operations using a multiagent architecture,” J. Field Robot. 27 (4), 412–429 (2010).CrossRefGoogle Scholar
8.West, M., Novitzky, M., Varnell, J., Melim, A., Sequin, E., Toler, T., Collins, T. and Bogle, J., “Design and Development of the Yellow fin uuv for Homogenous Collaborative Missions,” Association for Unmanned Vehicle Systems International (2010).Google Scholar
9.Novitzky, M., “Improvement of Multi-AUV Cooperation Through Teammate Verification,” Automated Action Planning for Autonomous Mobile Robots: Papers from the 2011 AAAI Workshop (WS-11-09), San Francisco, CA (2011) pp. 72–73.Google Scholar
10.Baxter, R., Lane, D. and Petillot, Y., “Behaviour Recognition for Spatially Unconstrained Unmanned Vehicles,” Proceedings of the IJCAI 9, 1–8 (2009).Google Scholar
11.Baxter, R. H., Lane, D. M. and Petillot, Y., “Recognising Agent Behaviour During Variable Length Activities,” Proceedings of The 19th European Conference on Artificial Intelligence (2010).Google Scholar
12.Novitzky, M., Pippin, C., Balch, T., Collins, T. and West, E., “Behavior Recognition of an AUV Using a Forward-Looking Sonar,” In: Workshops at the Robotics: Science and Systems, (Los Angeles, CA, 2011).Google Scholar
13.Vail, D., Veloso, M. and Lafferty, J., “Conditional Random Fields for Activity Recognition,” Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, ACM (2007) pp. 1–8.Google Scholar
14.Vail, D. and Veloso, M., “Feature Selection for Activity Recognition in Multi-Robot Domains,” Proceedings of AAAI, Pittsburgh (2008).Google Scholar
15.Irish, J. and Lillycrop, J., “Scanning laser mapping of the coastal zone: the shoals system,” ISPRS J. Photogramm. Remote Sens. (1999).CrossRefGoogle Scholar
“Mine warfare platforms, programs and systems,” Naval Expeditionary Warfare Directorate (N85): Naval expeditionary warfare vision 2010 (2010).Google Scholar
17.Tulldahl, M. and Pettersson, M., “Lidar for Shallow Underwater Target Detection,” Proceedings of the SPIE, Electro-Optical Remote Sensing, Detection, and Photonic Technologies and Their Applications (2007).CrossRefGoogle Scholar
18.Dzielski, J., DeLorme, M., Sedunov, A., Sammut, P. and Tsionskiy, M., “Guidance of an Unmanned Unederwater Vehicle Using a Passive Acoustic Threat Detection System,” IEEE Waterside Security Conference (WSS) (2010).CrossRefGoogle Scholar
19.Rabiner, L., “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE 77 (2), 257–286 (1989).CrossRefGoogle Scholar
20.Lafferty, J., McCallum, A. and Pereira, F., “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” International Conference on Machine Learning (2001).Google Scholar
21.Benjamin, M., Schmidt, H., Newman, P. and Leonard, J., “Nested autonomy for unmanned marine vehicles with MOOS-IvP,” J. Field Robot. 27 (6), 834–875 (2010).CrossRefGoogle Scholar
22.Plamondon, R. and Srihari, S. N., “Online and off-line handwriting recognition: a comprehensive survey,” IEEE Trans. Pattern Anal. Mach. Intell. 22 (1), 63–84 (2000).CrossRefGoogle Scholar