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 superv...
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School of Mechatronics Engineering
2013
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my.unimap-308002016-06-12T14:11:34Z Supervisory fuzzy learning control for underwater target tracking Kia, C. Arshad, M.R. Abdul Hamid, Adom Wilson, P.A. Artificial intelligence Autonomous underwater vehicles Fuzzy control Fuzzy controller Image processing Pipelines 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 leamt 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. 2013-12-23T08:43:00Z 2013-12-23T08:43:00Z 2005-07-30 Working Paper Kia, C., Arshad, M.R., Adom, A.H., Wilson, P.A. Supervisory fuzzy learning control for underwater target tracking (2005) Proceedings - WEC 05: Fourth World Enformatika Conference, 6, pp. 92-95. http://hdl.handle.net/123456789/30800 en World Enformatika Conference;4th, 2005 School of Mechatronics Engineering |
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| language |
English |
| topic |
Artificial intelligence Autonomous underwater vehicles Fuzzy control Fuzzy controller Image processing Pipelines |
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Artificial intelligence Autonomous underwater vehicles Fuzzy control Fuzzy controller Image processing Pipelines Kia, C. Arshad, M.R. Abdul Hamid, Adom Wilson, P.A. Supervisory fuzzy learning control for underwater target tracking |
| description |
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 leamt 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. |
| format |
Working Paper |
| author |
Kia, C. Arshad, M.R. Abdul Hamid, Adom Wilson, P.A. |
| author_facet |
Kia, C. Arshad, M.R. Abdul Hamid, Adom Wilson, P.A. |
| author_sort |
Kia, C. |
| title |
Supervisory fuzzy learning control for underwater target tracking |
| title_short |
Supervisory fuzzy learning control for underwater target tracking |
| title_full |
Supervisory fuzzy learning control for underwater target tracking |
| title_fullStr |
Supervisory fuzzy learning control for underwater target tracking |
| title_full_unstemmed |
Supervisory fuzzy learning control for underwater target tracking |
| title_sort |
supervisory fuzzy learning control for underwater target tracking |
| publisher |
School of Mechatronics Engineering |
| publishDate |
2013 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/30800 |
| _version_ |
1643796385287372800 |
| score |
13.222552 |