Overview

The Stingray Project is an unmanned underwater vehicle (UUV) or autonomous underwater vehicle (AUV) specifically designed with the AUVSI AUV Competition in mind. The vehicle currently does all of its navigation based on vision and object detection. The focus of my project will be in underwater navigation techniques, including vision based navigation and underwater GPS techniques. The goal for this navigation capability will be for use at the Center for Marine Biodiversity & Conservation facility in the Islands of Moorea in French Polynesia. Part of this effort involves deploying underwater sensors throughout the reef. The problem is thatthere is a long turn around time on the sensor data. The role of the Stingray would be to autonomously navigate to each sensor and download the data. This could be done daily, weekly, monthly as needed to provide more regular data for analysis. In addition, I will use this project as a forum to discuss progress in underwater navigation through vision and object detection as it relates specifically to the AUVSI AUV 2009 Competition.

Updated on April 20, 2009

Methods

There are many techniques that can be used in soley or together through standard Kalman filtering algorithms. Some of these techniques will be useful for this project, and some may not. The ones that will not be utilized for this specific task could ultimately be integrated into the Kalman filter at a later date. Here are the currently supported techniques for navigation and vehicle position estimation (more details on each to come):

  • Dead Reckoning
  • Inertial Navigation System (INS)
  • Doppler Velocity Log (DVL) or Doppler Velocity Sonar (DVS)
  • Accoustic Navigation
    • Long Basiline (LBL)
    • Ultra-short Baseline (USBL)
  • Geophysical Navigation
  • Vision Based Navigation
  • GPS Updates via Surfacing

Updated on April 27, 2009

Hardware

The Stingray vehicle uses a lot of different hardware and sensors. Listed here are those sensor that will be directly accessed for use in this project.

  • 1 Microstrain 3DM-GX1 Inertial Navigation System
  • 2 Voith-Schneider Propellers
  • 1 SSI Technologies Pressure Sensor
    • Range from 14.69 − 29.50psi
    • Accuracies of < ±1%
  • 1 Labjack U3-HV A/D converter

Updated on May 2, 2009

Software

The Stingray software is written in C and C++ and runs in a Ubuntu-based Linux distribution with the Real-Time Application Interface (RTAI) built into the kernel. The main components of the software are planner, vision, and navigation. The planner controls higher level concepts such as direction through the navigation unit or vision mode through the vision unit. There is also a GUI called the Dock Control Station (DCS), which is used to control all aspects of the vehicle when tethered.

Updated on April 21, 2009

References

  • [1] Leonard, John J. and Bennett, Andrew A. and Smith, Christopher M. and Jacob, Hans and Feder, S. Autonomous Underwater Vehicle Navigation. MIT Marine Robotics Laboratory Technical Memorandum (1998).
  • [2] Balasuriya, B.A.A.P. and Takai, M. and Lam, W.C. and Ura, T. and Kuroda, Y. Vision based autonomous underwater vehicle navigation: underwatercable tracking. OCEANS '97. MTS/IEEE Conference Proceedings (1997).
  • [3] Whitcomb, L. and Yoerger, D. and Singh, H. Advances in Doppler-based navigation of underwater robotic vehicles. Robotics and Automation (1999). Proceedings IEEE International Conference (1999).
  • [4] Garcia, R. and Batlle, J. and Cufi, X. and Amat, J. Positioning an underwater vehicle through image mosaicking. Robotics and Automation (2001). Proceedings IEEE International Conference (2001).
  • [5] Sound Ocean Systems, Inc. Diver Hand-Held GPS System.
  • [6] ACSA Underwater GPS.
  • [7] Mapping Vehicle Navigation System. Monterey Bay Aquarium Research Institute.
  • [8] Ocean Explorer AUV Navigation. National Oceanic and Atmospheric Administration. US Department of Commerce.
  • [9] Kondo, Hayato and Maki, Toshihiro and Ura, Tamaki and Sakamaki, Takashi. AUV Navigation Based on Multi-Sensor Fusion for Breakwater Observation. International Symposium on Automation and Robotics in Construction (2006).
  • [10] Zhen, Guo and Feng, Sun. Research on ntegrated navigation method for AUV. Journal of Marine Science and Application, Vol. 4, No. 2, June 2005.
  • [11] Beckman, R. and Martinez, A. and Bourgeois, B. AUV positioning using bathymetry matching. OCEANS 2000 MTS/IEEE Conference and Exhibition (2000).

Updated on April 27, 2009