This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.

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.