Detecting Abnormal Speed of Marine Robots using Controlled Lagrangian Particle Tracking Methods
Article No.: 11, Pages 1 - 5
Abstract
The ability to detect abnormal conditions is of great importance for the survivability of marine robots. However, false alarms can occur and may lead to unnecessary interruption of robotic missions. This paper presents recent results on anomaly detection, which may reduce the rate of false alarms in the framework of controlled Lagrangian particle tracking (CLPT), a theoretical tool that analyzes interactions between robot motion and ocean flow. Adaptive learning algorithms extract vehicle speed as an indicator of anomalies from trajectory information using a predicted trajectory to identify when abnormal motion is detectable. The methods are verified by simulation results.
References
[1]
K. J. Astrom and B. Wittenmark. Adaptive Control. Addison-Wesley, second edition, 1995.
[2]
S. Cho and F. Zhang. An adaptive control law for controlled Lagrangian particle tracking. In Proceedings of the Eleventh ACM International Conference on Underwater Networks and Systems (WUWNet '16), 2016.
[3]
S. Cho, F. Zhang, and C. R. Edwards. Adaptive learning for controlled Lagrangian particle tracking. In Proceedings of MTS/IEEE Oceans 2016, pages 1--6, Monterey, CA, USA, 2016.
[4]
G. Fagogenis, V. D. Carolis, and D. M. Lane. Online fault detection and model adaptation for underwater vehicles in the case of thruster failures. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 2625--2630, 2016.
[5]
H. K. Khalil. Nonlinear Systems. Printice Hall, third edition, 2002.
[6]
N. E. Leonard, D. A. Paley, and et. al. Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey bay. Journal of Field Robotics, 27(6):718--740, 2010.
[7]
X. Liang, W. Wu, D. Chang, and F. Zhang. Real-time modelling of tidal current for navigating underwater glider sensing networks. In Procedia Computer Science, pages 10:1121--1126, 2012.
[8]
B. Y. Raanan, J. Bellingham, and et. al. A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle. In Proceedings of MTS/IEEE Oceans 2015, Washington, DC, USA, 2015.
[9]
Y.-S. Sun, X.-R. Ran, Y. ming Li, G.-C. Zhang, and Y. hao Zhang. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles. International Journal of Naval Architecture and Ocean Engineering, 8:243--251, 2016.
[10]
K. Szwaykowska and F. Zhang. Controlled Lagrangian particle tracking error under biased flow prediction. In 2013 American Control Conference, pages 2575--2580, Washington, DC, USA, 2013.
Index Terms
- Detecting Abnormal Speed of Marine Robots using Controlled Lagrangian Particle Tracking Methods
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Published In
November 2017
144 pages
ISBN:9781450355612
DOI:10.1145/3148675
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- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
- ASA: American Statistical Association
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 November 2017
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- Research
- Refereed limited
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WUWNET'17
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- SIGMOBILE
- ASA
WUWNET'17: International Conference on Underwater Networks & Systems
November 6 - 8, 2017
NS, Halifax, Canada
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Overall Acceptance Rate 84 of 180 submissions, 47%
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