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By N. Cruz

ISBN-10: 9533074329

ISBN-13: 9789533074320

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Extra resources for Autonomous Underwater Vehicles

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The autonomous navigation mission can be divided into following four phases Fig. 16. Test environment and corresponding range sonar profile images. 5m, 90o), the vehicle turn Obstacle Detecting Phase: At start point a half cycle counter-clock wisely. In this period, the vehicle detects the obstacle using forward range sonar. Path Planning Phase: According to the profile image got from the Obstacle Detecting Phase, = ( , , , 0 ). the vehicle designs a target point , the vehicle Vision-based Underwater Localizing Phase: While approaching to recognizes the underwater pattern, from which defines the end point = (9, − , − , 0 ).

For extracting these dots, we use the cross-ratio invariant, which is a basic projective invariant of perspective space (Hartley & Zisserman, 2000). The cross-ratio is defined as following ( , , , )= | || | | || | , where = Fig. 5. Designed underwater pattern for P-SURO AUV. (1) Development of a Hovering-Type Intelligent Autonomous Underwater Vehicle, P-SURO 27 Fig. 6. Cross-ratio in the projective space. The eight dots in the pattern contain 3D pose information. For extracting these dots, we use the cross-ratio invariant, which is a basic projective invariant of perspective space (Hartley & Zisserman, 2000).

Kim, J. , Lee, M. , Lee, J. W. (2010). Development of an Intelligent Autonomous Underwater Vehicle, P-SURO, Proceedings of Oceans'10 IEEE Sydney, Sydney, Australia, May 24-27, 2010 Oh, S. , Yoon, B. , Li, J. H. (2010). Vision-based localization for an intelligent AUV, P-SURO, Proceedings of KAOSTS Annual conference, pp. , Coste, M. , Simon, D. (1994). And advanced control architecture for underwater vehicles, Proceedings of Oceans'94, Brest, France, pp. I-590-595, 1994 Prestero, T. (2001). Verification of a six-degree of freedom simulation model for the REMUS autonomous underwater vehicles, Masters Thesis, MIT, USA Quek, C & Wahab, A.

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Autonomous Underwater Vehicles by N. Cruz

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