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Ultrasonic Tracking System. Group # 4 4/22/03 Bill Harris Sabie Pettengill Enrico Telemaque Eric Zweighaft. Overview. Objective Motivation Specifications Design Approach Results Design Evaluation Conclusion. Objective.

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ultrasonic tracking system
Ultrasonic Tracking System

Group # 4

4/22/03

Bill Harris

Sabie Pettengill

Enrico Telemaque

Eric Zweighaft

overview
Overview
  • Objective
  • Motivation
  • Specifications
  • Design Approach
  • Results
  • Design Evaluation
  • Conclusion
objective
Objective
  • Design a pan/tilt system which acts as a tracking device using ultrasonic transmitters and receivers
motivation
Motivation
  • Applications of tracking are basic tasks worked on by engineers in various fields
    • Aerospace- Flight control radar
    • Defense- Smart targeting smart weapons
    • Sensors- Smart collision sensors on cars
  • Incorporation of tracking in model teaches fundamentals of sensor technology in conjunction with control technology
specifications
Specifications
  • The system will track objects between 2 and 10 meters from the array
  • The system will track objects between 0 and 2 meters off the ground
  • The system will track items within .5 degree of accuracy (within 10 cms of the object with beacon)
  • The system must be able to track the beacon at the speed of a human walking (.64 rad/sec)
design approach
Design Approach
  • CAD and Matlab used to model core pan/tilt system with addition of
    • Motors, belts, gears, pulleys
    • L shaped sensor structure
    • Laser pointer
  • Linear simulation of system with
    • Matlab
    • Simulink diagrams
cad drawing of system
Key Issues

L Shape sensor mount

Mounting sensors on to the beams

Cad drawing of system

t

motor specifications
Motor Specifications
  • Pittman GM8724S017
    • 19.5:1 internal gearing ratio
    • Encoder mounted directly to rotor increases accuracy of encoder (encoder is not geared down)
    • External transmission gives additional reduction ratio of 3:1
    • Larger motor size needed to meet system specifications
linear approach
Linear Approach
  • Linear design of controller
    • PD controller designed
      • SISO design tool used for testing
nonlinear approach
Nonlinear Approach
  • Nonlinear design of controller
    • Input of transfer functions from linear design
    • Motor feasibility, torque requirements, and tracking ability observed
circuit approach
Circuit Approach
  • Circuit design for sensors
    • Input- Logic gates obtain time difference between signals received by sensors
    • Output – 12-bit accuracy in pitch and yaw direction
    • 3 additional digital I/O for circuit/controller communication
circuit diagram
Circuit Diagram
  • Key Issues
  • DC OpAmps
  • Flip Flops
software development
Software Development
  • Software Algorithms have several levels
    • Binary to Decimal Conversion
      • Gives us magnitude of time difference, and sign of difference
    • Angle Calculation Algorithm
      • Takes these 2 inputs, along with estimated distance, and returns the desired change in angle to the controller
    • Controller
software development1
Software Development
  • Binary Conversion
    • Takes in 12 Digital I/O inputs and treats them as a binary number, then converts this number to an integer
software development2
Software Development

y

Transmitter (point X,Y)

  • Current Angle Calculation Algorithm
  • d2 = sqrt(X2 + Y2)
  • d1 = sqrt((X – c)2 + Y2)
  • dm = sqrt((X – c)2 + Y2) - sqrt(X2 + Y2)
  • 4 Lookup Tables were generated using a range of Y’s, and a range of dm’s
    • One each for positive pan, negative pan, positive tilt, and negative tilt

x

d1

d2

dm = d1 – d2

θ

Sensor 1

Sensor 2

e

c

software development3
Software Development

(X,Y)

  • This only calculates the angle we are currently at
  • We also need to calculate the angle we want to be at, given the Range estimate Y
  • θ = atan2(Y , e + 0.5 * c)

Sensor 1

Sensor 2

θ

e

c

software development4
Software Development
  • Now that we know our current angle, and desired angle, we subtract the two, and send this value to the controller.
linear results
Linear Results
  • Simulation results
    • Step response of controller
    • Within 1% steady state error
nonlinear results
Nonlinear Results
  • Simulation results
    • Motor torque
    • Motor tracking
nonlinear results1
Nonlinear Results
  • Simulation results
    • Motor feasibility
friction id results
Friction ID Results
  • Pan
    • Coulomb
      • Pos: 0.13
      • Neg: -0.13
    • Viscous
      • Pos: .01
      • Neg: -.0089
final results
Final Results
  • Original specifications vs Final specifications
    • Tracking accuracy
    • Tracking accuracy with motion
    • Affect of friction compensation
results demo
Results Demo
  • System demo video
    • Demo
design evaluation
Design Evaluation
  • Problems encountered
    • Sensor functionality
  • Future Improvements
    • Improved integration of sensor and control system
    • Faster sensor algorithms
    • Addition of filters to improve motion of system
conclusion
Conclusion
  • PD controller used
  • Accurate linear vs nonlinear results obtained
  • System is expandable for future improvements
  • Questions?
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