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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
• 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