Vision based heuristic reasoning for intelligent auv navigation
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Date
2006
Authors
Kia, Chua
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Abstract
This work introduces a novel path planning strategy to improve the level
of automation of underwater Remote Operated Vehicles (ROVs) operations. It is
originated from the desire to develop robotics vision system with the ability to
mimic the human expert's judgement and reasoning when maneuvering ROV in
the traverse of the underwater terrain. The study focuses on applying image
processing procedure and fuzzy inference system for the analysis of the terrain.
The vision system developed is capable of interpreting underwater scene by
extracting subjective uncertainties of the object of interest. Subjective
uncertainties are further processed as multiple inputs of a fuzzy inference
system that is capable of making crisp decisions concerning where to navigate.
The important part of the image analysis is morphological filtering. The
applications focus on binary images with the extension of gray-level concepts.
An open-loop fuzzy control system is developed for classifying the traverse of
terrain. Robustness is further enhanced by the implementation of supervised
fuzzy learning control technique to the system. Supervision to the fuzzy control
system allows for the incorporation of additional learnt information (knowledge)
into the control decision-making process. As a whole, the system is designed to
perform path planning for the purpose of target (pipeline) tracking and
navigation from a perspective view perceived condition. Computer simulations
and prototype simulations demonstrate the effectiveness of this approach. This
method is found to be very practical and have great potential usefulness for
application in Autonomous Underwater Vehicle (AUV) target tracking.
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Keywords
Heuristic reasoning , Intelligent auv