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Underwater Vehicle Navigation Techniques Chris Barngrover CSE 237D

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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. - PowerPoint PPT Presentation

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Underwater Vehicle Navigation Techniques

Chris BarngroverCSE 237D

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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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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

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Microstrain 3DM-GX1 INS

SSI Technologies Pressure Sensor

2 Remote Ocean System CE-X-18 Underwater Cameras

OpenCV Library

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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

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Incorporated Planner Module

Developed LPS Module

Researched pressure to depth conversion

Researched Kalman filter techniques

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Depth Conversion Function

Basic Kalman Filter◦ Ground up development – Stalled◦ OpenCV Libraray - Success

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SSI Technologies Pressure Sensor

Take depth measurements atDepth PSI (avg) PSI (mode) STDEV

< 0 0.487980531 0.4878 0.002353867

0 0.504468293 0.5082 0.00547204

6 0.5609 0.5592 0.008404093

12 0.626790909 0.6305 0.005927089

18 0.680715385 0.6815 0.009534933

24 0.734753846 0.7324 0.007245701

36 0.844180769 0.8446 0.008845203

30 0.786181818 0.7834 0.006437068

36 0.831566667 0.8344 0.005860034

46 0.945572727 0.9465 0.004676756

52 0.978253659 0.977 0.00565677

< 0 0.488291566 0.4878 0.003332674

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Variables:

Average Function:

Mode Function:

Amalgamation:

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Created a kalman library◦ init_kalman()◦ close_kalman()◦ kalman_update( time, status )◦ kalman_get_location( &loc )

Manages the CvKalman class from OpenCV

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State Equation:

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Measurement Equation:

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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

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