Kia, C., Arshad, M.R., Adom, A.D. and Wilson, P.A.
Supervisory fuzzy learning control for underwater target tracking
In Proceedings of Enformatika.
World Enformatika Society., .
Full text not available from this repository.
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.
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