By N. Cruz
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Extra resources for Autonomous Underwater Vehicles
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.
Autonomous Underwater Vehicles by N. Cruz