stingray
DESCRIPTION
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 PresentationTRANSCRIPT
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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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