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Stingray

Stingray. Underwater Vehicle Navigation Techniques. Chris Barngrover CSE 237D. Unmanned Vehicle Navigation. Most funding goes to UAVs followed by UGVs Lots of UUV applications (e.g. Moorea) GPS is easiest way to know location, but this fails underwater Need to use other techniques.

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Stingray

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  1. Stingray Underwater Vehicle Navigation Techniques Chris Barngrover CSE 237D

  2. Unmanned Vehicle Navigation • Most funding goes to UAVs followed by UGVs • Lots of UUV applications (e.g. Moorea) • GPS is easiest way to know location, but this fails underwater • Need to use other techniques

  3. Navigation Techniques • Dead Reckoning • Inertial Navigation System (INS) • Doppler Velocity Log (DVL) • Acoustic Techniques • Long Baseline (LBL) • Ultra-short Baseline (USBL) • Geophysical (a priori maps) • Computer Vision

  4. Stingray • Microstrain 3DM-GX1 INS • SSI Technologies Pressure Sensor • 2 Remote Ocean System CE-X-18 Underwater Cameras • OpenCV Library

  5. Quarter Goals • Convert pressure sensor data to depth • Develop module that subscribes to INS, depth, and vision data • Develop a Kalman filter to create position estimation • Use vision techniques to rectify position estimation

  6. Midway Accomplishments • Incorporated Planner Module • Developed LPS Module • Researched pressure to depth conversion • Researched Kalman filter techniques

  7. Further Accomplishments • Depth Conversion Function • Basic Kalman Filter • Ground up development – Stalled • OpenCV Libraray - Success

  8. Depth Conversion • SSI Technologies Pressure Sensor • Take depth measurements at

  9. Depth Conversion • Variables: • Average Function: • Mode Function: • Amalgamation:

  10. Kalman Filter • Created a kalman library • init_kalman() • close_kalman() • kalman_update( time, status ) • kalman_get_location( &loc ) • Manages the CvKalman class from OpenCV

  11. Kalman Filter • State Equation: • : state vector • : transition matrix - relates state vectors • : control matrix – relates control to state • : control vector • : noise vector (k represents current time)

  12. Kalman Filter • State Equation:

  13. Kalman Filter • Measurement Equation: • : measurement vector • : relates state to measurement • : state vector • : noise vector (k represents current time)

  14. Kalman Filter • Measurement Equation:

  15. Kalman Filter

  16. Future Work • Continue Kalman Filter library • Add control elements – • Use angle and rotation angle to fix accelerations • Add velocity sensor for better results • Consider measured covariance matrices • Use vision to rectify location • Incorporate acoustic pinger triangulation • Other related work • Build standard course with dimensions • Develop visual tool

  17. Questions

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