Development of a Gyroscope-FreeInertial Navigation System

Matthew BecklerProject Advisor: Charles Rennolet

Department of Electrical and Computer EngineeringUniversity of Minnesota

Minneapolis, Minnesota 55455

beck0778@umn.edu

May 6, 2008

Problem Overview

Inertial Navigation System

A navigation system that uses sensors to determine and track a vehicle’sposition and attitude without any external references.

Used in guided missiles, aircraft, andspacecraft.

Traditionally use a mechanical gimbal systemto maintain a reference orientation.

GPS can be used to eliminate long-term drift.

MEMS gryos are relatively expensive ($30+ peraxis) compared to MEMS accelerometers ($6for 3-axis part).

Is it possible to track position and orientationwith nothing but accelerometers?

A gimbal platform

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Approach

MEMS accelerometers carefully arranged.

Digitize and record 50 samples per second.

Store data to SD card.

Prototyping board provides rigid mounting.

Characterize each of the sensors with controlledaccelerations.

Matlab software to perform numeric integrationand recover position data.

Board Layout

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Project Progress, I: Hardware Construction

Picture ofprotoboardconstruction.

User interface:Switches formain power and“Enable logging”

Added 7-segmentdisplay to providestate feedback.

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Project Progress, II: Turntable Construction

Sensors output an analog value, proportional to acceleration.

Need to find 0g value, and conversion ratio to convert between offsetfrom 0g reading and the actual acceleration.

Created a rotating assembly to apply a controlled and calculableacceleration to sensors.

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Project Progress, II: Turntable Construction Pictures

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Project Progress, II: Turntable Construction Pictures

To calculateacceleration, we need anaccurate measure ofangular velocity.

A rolling microswitchwas attached to thestationary motor, andtriggered once perrevolution.

PIC processor used tomeasure rotation time,and report it over serialconnection.

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Project Progress, III: Sensor Characterization

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x1y1z1

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Project Progress, III: Sensor Characterization Results

Values intable are:

ADCvalue(m

s2

)

0g value usedto computeoffset.

10.6 meansα = 1m

s2

produces areading of10.6 awayfrom the 0greading.

calibration of y-axis sensors

Y1 (522) Y2 (523) Y3 (511)

ω = 1.62 rad

s10.5953 10.6742 10.5657

αin = 0.13g 10.5600 10.6180 10.4809αout = 0.16g 10.6112 10.6856 10.5772

10.6388 10.7126 10.590210.6094 10.6795 10.5273

ω = 2.92 rad

s10.6818 10.7425 10.6412

αin = 0.43g 10.6818 10.7761 10.7137αout = 0.53g 10.7569 10.8094 10.7287

10.7003 10.7333 10.653110.7396 10.7952 10.6967

α = 1g 10.8577 10.9682 10.7078

Mean 10.6757 10.7450 10.6257StdDev 0.0862 0.0936 0.0831

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Project Progress, IV: Real-life Accelerations

This is the raw sensor data when I walked around my house.

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Project Progress, IV: Real-life Accelerations

We can use a filter to clean up the raw data (top) to get a smoother plot.

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Further Work

Finish the MATLAB code to analyze recorded accelerations.

Develop a decent way to visualize three-dimensional movement overtime.

Current Results

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Desired Results

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Lessons Learned

Everything takes longer than estimated.

Accurate estimation is hard.

Don’t be afraid to ask for help.

Building things in the real world is much more difficult thansimulation!

Accomplish something worthwhile every day.

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Acknowledgements / Questions

Many thanks to:

Project Advisor Professor Charles Rennolet

ECE Department

Any Questions?

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