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Boundary Estimation and Tracking Algorithms

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Title: Boundary Estimation and Tracking Algorithms


1
Boundary Estimation and Tracking Algorithms
Final Presentation 29 August 2007
  • Trevor Ashley (HMC)
  • Yuan Rick Huang (UCLA)

2
Problem and Objective
  • Design and implement hardware and software for
    the UCLA Applied Math Labs 2nd Generation
    Testbed Vehicles in order to track the boundaries
    of floating, multi-colored occlusions via a
    posteriori data acquisition (i.e. no a priori
    knowledge of occlusions is given to sensing
    vehicle other than an initial condition)

3
Background of Project
  • Eleven week summer research project
  • UCLA Applied Mathematics Laboratory
  • Project involvement
  • Zhipu Jin (UCLA)
  • Yuan Rick Huang (UCLA)
  • Trevor Ashley (HMC)

4
Outline
  • Overview of 2nd Generation Testbed
  • Algorithm Verification
  • Testing with Virtual Boundaries
  • UUV-gas Algorithm
  • Time-Corrected Algorithm
  • Sensor Selection and Testing
  • Empirical Tests of Chosen Sensor
  • Sensor Height Determination
  • Boundary Color Selection
  • Coalescence of Vehicle and Sensor
  • Hardware/Testbed Modifications
  • CUSUM Filter

5
Second Generation Overview
  • Consists of
  • Robotic Vehicles
  • Vehicle Testbed
  • Vision Software
  • Cameras
  • Various Receiver/Transceiver modules

6
Robotic Vehicles
  • Car and Tank Vehicles
  • ZipZaps RC toys
  • Gives support for stacked PCBs
  • Servomotors allow for variable speed and dynamic
    steering
  • Plantraco Micro R/C 3.7 V 850 mAh Lithium Polymer
    battery
  • Provides approximately 40 minutes continuous
    runtime
  • Easily rechargable
  • Atmel Atmega8 Processor
  • Onboard decision making and data acquisition
  • Sharp Proximity Sensor

7
Vision System
  • Cameras wired to Windows-based CPU (via IEEE
    1394) running software designed with OpenCV and
    C
  • Vehicles identified by binary-coded tags
  • CPU sends location and orientation information to
    vehicles via Radiotronix Wi.232DTS module

8
The Testbed Layout
  • Rectangular
  • 640 x 890 pixels
  • 0.0937 inch/pixel

9
Stages of Algorithm Implementation
  • Stage 1 Algorithm Verification
  • Test algorithms with virtual, software-based
    boundaries
  • Stage 2 Sensor Selection and Testing
  • Obtain empirical data
  • Stage 3 Algorithm Debugging with Sensor
  • Hardware modifications
  • CUSUM filter

10
Virtual Boundaries
  • Rectangle
  • Vertices at (100,100), (100,750), (300,750),
    (500,100)
  • Circle
  • Center at (320,430) Radius of 200 pixels
  • Ellipse
  • Center at (320,430) Semimajor axis of 200
    pixels Semiminor axis of 250 pixels

11
Control Algorithms for Boundary Tracking
  • UUV-gas 1
  • Time-corrected algorithm 2

1 Multi-UUV Perimeter Surveillance. Kemp, et
al. 2 Environmental Boundary Tracking and
Estimation Using Multiple Autonomous Vehicles.
Jin and Bertozzi.
12
Simple Control Law
  • UUV-gas algorithm

13
Tracking the Virtual Rectangle
  • Limitations
  • High speed creates wide turning radius
  • Steering angle limited by /- 25o
  • Observations
  • Covers large amount of space not relevant to
    boundary

14
Tracking the Virtual Circle
  • Limitations
  • Same as rectangle
  • Observations
  • Covers large amount of space not relevant to
    boundary
  • Covers redundant space
  • Risk of instability caused by detection error

15
Tracking the Virtual Ellipse
  • Limitations
  • Same as rectangle
  • Observations
  • Covers large amount of space not relevant to
    boundary
  • Less risk of instability

16
Advanced Control Law
  • Time-corrected algorithm
  • Includes time difference between crossing points
    on boundary,
  • Uses a reference angle, qref

17
Slowly Tracking a Straight Line
  • Parameters
  • w 0.0003
  • qref 25o
  • Limitations
  • Initial conditions create wide starting angle
  • Observations
  • Traces boundary more efficiently than UUV-gas

18
Quickly Tracking a Straight Line
  • Parameters
  • w 0.0003
  • qref 25o
  • Speed 40 faster
  • Limitations
  • Higher speed creates wider turning radius
  • Observations
  • Offers no benefit over UUV-gas

19
Algorithm Summary
  • UUV-gas
  • Inefficient
  • Covers irrelevant and redundant space
  • High probability of becoming unstable
  • Time-dependent algorithm
  • Efficient depending on speed of vehicle

20
The Role of the Sensor
  • Processor will decide state based on sensor data
  • Vision system no longer necessary to track
    boundary
  • Floating occlusions block vehicle from cameras

21
Sensor Selection and Testing
  • Phototransistor
  • Fairchild Semiconductor
  • QRB1134
  • TT Electronics
  • OPB608V

22
QRB1134
  • Characteristics
  • Linear decline in current for distances greater
    than 0.15 inches until 0.35 inches
  • Moderate current drawn from collector

23
OPB608V
  • Characteristics
  • Large rise from 0 inch to 0.1 inch
  • Logarithmic drop after 0.1 inch
  • Large collector current drawn between 0.1 inch
    and 0.5 inch

24
Sensor Selection
  • QRB1134
  • Optimal Range 0.2 to 0.35 inches
  • OPB608V
  • Optimal Range 1 to 1.5 inches
  • Selected QRB1134
  • Ease of implementation on vehicle
  • Low power consumption

25
Sensor Circuitry
  • QRB1134 possesses
  • Sensor
  • IR Phototransistor (behaves like NPN BJT)
  • Emitter
  • IR LED
  • Phototransistor in emitter follower configuration
  • VDD set to 5 V

26
Height and Color Characteristics
  • Figure shows dependence of height and tape color
    on voltage
  • Voltage represents Vout
  • Distance is measured from sensor tip to colored
    tape

27
Height and Color Characteristics
  • Testbed (gray), black, green
  • Statistically significant difference
  • Yellow, red, teal
  • Similar height-voltage characteristics
  • Significantly different from testbed, black, green

28
Sensor Sweep Tests
  • Sensor attached to ruler at fixed height
  • Ruler dragged across sample of testbed with
    various tapes
  • Voltage measured with Dynon ELAB-080 oscilloscope

29
Sweep Characteristics Teal
  • Height 0.25 in
  • Average Speed 1.6 in/s
  • Voltage Trigger 0.8584 V
  • 32K samples
  • Sample Frequency 19.9883 KHz

30
Sweep Characteristics Teal
  • Height 0.375 in
  • Average Speed 1.7 in/s
  • Voltage Trigger 0.6121 V
  • 32K samples
  • Sample Frequency 19.9883 KHz

31
Sweep Characteristics Black
  • Height 0.25 in
  • Average Speed 1.7 in/s
  • Voltage Trigger 0.8174 V
  • 32K samples
  • Sample Frequency 19.9883 KHz

32
Sweep Characteristics Black
  • Height 0.375 in
  • Average Speed 1.7 in/s
  • Voltage Trigger 0.5300 V
  • 32K samples
  • Sample Frequency 19.9883 KHz

33
Sensor Testing Summary
  • Fairfield Semiconductor QRB1134
  • Low power consumption
  • Stable region appropriate for vehicle
  • Distance of 0.25 inches between sensor and tape
  • Sensor measures distinguishing voltages
  • Black and teal tapes chosen
  • Invisible to tracking cameras

34
Sensor Mounting
  • Proximity sensor removed
  • Voltage output sampled by Atmega8 ADC
  • ADC maps 0,5 volts to integer values 0,1024
  • Sensor glued to front of car
  • Remaining hardware unchanged

35
The Physical Boundary
  • Boundary created by junction of teal and black
    tape
  • Modeled after concave Jordan curve
  • Experiment setup

36
CUSUM Filter
  • Necessary to filter collected ADC data
  • Reduce random error
  • Convert data to binary states

37
CUSUM Filter cont.
  • Upper
  • Lower

38
CUSUM Filter cont.
  • Dual CUSUM filter
  • Three states from which to distinguish on-teal,
    on-black, on-testbed
  • Two CUSUM filters with concatenated outputs
  • Outputs two binary bits
  • 00 on testbed
  • 01 on black tape
  • 11 on teal tape
  • 10 not used

39
CUSUM Parameter Testing
  • CUSUM1 (bit 1)
  • Uo350
  • cl120
  • Cu120
  • Lo-350
  • B150
  • CUSUM2 (bit 2)
  • Uo 950
  • cl 630
  • cu 630
  • Lo -950
  • B 150

40
CUSUM Parameter Testing
  • CUSUM1 (bit 1)
  • Uo350
  • cl120
  • Cu120
  • Lo-350
  • B150
  • CUSUM2 (bit 2)
  • Uo 950
  • cl 630
  • cu 630
  • Lo -950
  • B 150

41
CUSUM Parameter Testing
  • CUSUM1 (bit 1)
  • Uo350
  • cl120
  • Cu120
  • Lo-350
  • B150
  • CUSUM2 (bit 2)
  • Uo 1300
  • cl 630
  • cu 630
  • Lo -1300
  • B 150

42
Final CUSUM Parameters
  • CUSUM2 (bit 2)
  • Uo 10,000
  • cl 630
  • cu 630
  • Lo -10,000
  • B 150
  • CUSUM1 (bit 1)
  • Uo350
  • cl120
  • Cu120
  • Lo-350
  • B150
  • Upper threshold set to 70 of maximum
  • Lower threshold set to 100 of minimum

43
Final CUSUM Parameters
44
State Transition Diagram
45
Tracking the Physical Boundary
  • Algorithm
  • UUV-gas
  • Parameter
  • w 15o

46
Tracking the Physical Boundary cont.
  • Algorithm
  • UUV-gas
  • Parameter
  • w 15o

47
Tracking the Physical Boundary cont.
  • Algorithm
  • Time-dependent algorithm
  • Parameters
  • w 0.0003
  • qref 25o

48
Conclusions and Suggestions for Future Work
  • Further testing needed to critically determine
    effectiveness of time-dependent algorithm
  • Implement cooperative boundary tracking
  • P2P networking between multiple vehicles
  • Implement multiple sensors
  • Reduce error variance

49
Acknowledgments
  • Zhipu Jin
  • Andrea L. Bertozzi
  • Andrew Bernoff
  • Rachel Levy
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