Abstract
The autonomous underwater vehicle (AUV) and the problems associated with its safe navigation have been studied for the last two decades. The real-time underwater obstacle recognition procedure still has many complications associated with it and the issue becomes worse with vague sensor data. These problems can be coped with the merger of a robust classification mechanism and a domain knowledge acquisition technique. In this paper, we introduce a hybrid mechanism to recognize underwater obstacles for AUV based on fuzzy domain ontology and support vector machine (SVM). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years and is a new generation learning system based on recent advances in statistical learning theory. The amalgamation of fuzzy domain ontology with SVM boosts the performance of the obstacle recognition module by providing the timely semantic domain information of the surrounding circumstances. Also the reasoning ability of the fuzzy domain ontology can expedite the obstacle avoidance process. In order to evaluate the performance of the system, we developed a prototype simulator based on OpenGL and VC++. We compared the outcomes of our proposed technique with backpropagation algorithm and classic SVM based techniques.
Highlights
Introduction and BackgroundCurrently, the underwater obstacle recognition issue has received a lot of attention and, due to its significance, has become one of the hot research topics of machine learning and computer vision
We introduce a hybrid mechanism to recognize underwater obstacles for autonomous underwater vehicle (AUV) based on fuzzy domain ontology and support vector machine (SVM)
We investigated an appearance-based obstacle recognition mechanism using fuzzy ontology and SVM that solves many of these problems for AUVs navigation safety
Summary
The underwater obstacle recognition issue has received a lot of attention and, due to its significance, has become one of the hot research topics of machine learning and computer vision. We investigated an appearance-based obstacle recognition mechanism using fuzzy ontology and SVM that solves many of these problems for AUVs navigation safety. SVMs deliver state-of-the-art performance in real-world applications such text categorization, hand-written character recognition, and image classification Their first introduction in the early 1990s led to a recent explosion of applications and the deepening of theoretical analysis has established support vector machines along with neural networks as one of the standard tools for machine learning and data mining. We introduce a fast underwater obstacle recognition system for AUV based on fuzzy ontology and SVM [3, 10, 11].
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