Supervisory Fuzzy Learning Control for Underwater Target Tracking
This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.
[1] An, E., A comparison of AUV navigation performance: a system
approach, OCEANS 2003. Proceedings , Volume: 2 , 22-26 Sept. 2003
Pages:654 - 662 Vol.2
[2] Evans, J., Petillot, Y., Redmond, P., Wilson, M. and Lane, D.,
AUTOTRACKER: AUV Embedded Control Architecture for
Autonomous Pipeline and Cable Tracking, OCEANS 2003. Proceeding ,
Volume: 5 , 22-26 Sept. 2003, pp. 2651 - 2658
[3] Arjuna Balasuriya and Ura, T., Vision-based underwater cable detection
and following using AUVs Oceans '02 MTS/IEEE , Volume: 3 , 29-31
Oct. 2002 Pages:1582 - 1587 vol.3
[4] Chua Kia and Mohd. Rizal Arshad, Robotics Vision-based Heuristic
Reasoning for Underwater Target Tracking and Navigation, Conference
Proceeding of The 2nd International conference on Mechatronics 2005,
Vol. 1 , 10-12 May 2005, Pages 132-139 Vol.1.
[5] The World Deepwater Atlas, Oilfield Publications Limited (England) /
Oilfield Peblications Inc. (USA).
[6] Kreyszig, Advanced Engineering Mathematics, Wayne Anderson, 1993.
[7] James, G., Burley, D., Clements, D., Dyke, P., Searl, J. and Wright, J.,
Modern Engineering Mathematics, Addison Wesley, 1994.
[8] Passino, K. M. and Yurkovich, S., Fuzzy Control, Addison Wesley, 1997.
[1] An, E., A comparison of AUV navigation performance: a system
approach, OCEANS 2003. Proceedings , Volume: 2 , 22-26 Sept. 2003
Pages:654 - 662 Vol.2
[2] Evans, J., Petillot, Y., Redmond, P., Wilson, M. and Lane, D.,
AUTOTRACKER: AUV Embedded Control Architecture for
Autonomous Pipeline and Cable Tracking, OCEANS 2003. Proceeding ,
Volume: 5 , 22-26 Sept. 2003, pp. 2651 - 2658
[3] Arjuna Balasuriya and Ura, T., Vision-based underwater cable detection
and following using AUVs Oceans '02 MTS/IEEE , Volume: 3 , 29-31
Oct. 2002 Pages:1582 - 1587 vol.3
[4] Chua Kia and Mohd. Rizal Arshad, Robotics Vision-based Heuristic
Reasoning for Underwater Target Tracking and Navigation, Conference
Proceeding of The 2nd International conference on Mechatronics 2005,
Vol. 1 , 10-12 May 2005, Pages 132-139 Vol.1.
[5] The World Deepwater Atlas, Oilfield Publications Limited (England) /
Oilfield Peblications Inc. (USA).
[6] Kreyszig, Advanced Engineering Mathematics, Wayne Anderson, 1993.
[7] James, G., Burley, D., Clements, D., Dyke, P., Searl, J. and Wright, J.,
Modern Engineering Mathematics, Addison Wesley, 1994.
[8] Passino, K. M. and Yurkovich, S., Fuzzy Control, Addison Wesley, 1997.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51855", author = "C.Kia and M.R.Arshad and A.H.Adom and P.A.Wilson", title = "Supervisory Fuzzy Learning Control for Underwater Target Tracking", abstract = "This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.", keywords = "Fuzzy logic, Underwater target tracking,
Autonomous underwater vehicles, Artificial intelligence,
Simulations, Robot navigation, Vision system.", volume = "1", number = "6", pages = "797-4", }