Boundary Estimation and
Tracking Algorithms
Final Presentation
29 August 2007
Trevor Ashley (HMC)
Yuan Rick Huang (UCLA)
Problem and Objective

Design and implement hardware and
software for the UCLA Applied Math
Lab’s 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)
29 August 2007
2
Background of Project



Eleven week summer research project
UCLA Applied Mathematics Laboratory
Project involvement:



Zhipu Jin (UCLA)
Yuan Rick Huang (UCLA)
Trevor Ashley (HMC)
29 August 2007
3
Outline


Overview of 2nd Generation Testbed
Algorithm Verification

Testing with Virtual Boundaries



Sensor Selection and Testing

Empirical Tests of Chosen Sensor



UUV-gas Algorithm
Time-Corrected Algorithm
Sensor Height Determination
Boundary Color Selection
Coalescence of Vehicle and Sensor


Hardware/Testbed Modifications
CUSUM Filter
29 August 2007
4
Second Generation Overview

Consists of:





29 August 2007
Robotic Vehicles
Vehicle Testbed
Vision Software
Cameras
Various
Receiver/Transceiver
modules
5
Robotic Vehicles

Car and Tank Vehicles

ZipZaps RC toys



Plantraco Micro R/C 3.7 V 850 mAh
Lithium Polymer battery



Provides approximately 40 minutes
continuous runtime
Easily rechargable
Atmel Atmega8 Processor


Gives support for stacked PCBs
Servomotors allow for variable speed and
dynamic steering
Onboard decision making and data
acquisition
Sharp Proximity Sensor
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6
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
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7
The Testbed Layout



Rectangular
640 x 890 pixels
0.0937 inch/pixel
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8
Stages of Algorithm
Implementation

Stage 1: Algorithm Verification


Stage 2: Sensor Selection and Testing


Test algorithms with “virtual,” softwarebased boundaries
Obtain empirical data
Stage 3: Algorithm Debugging with Sensor


Hardware modifications
CUSUM filter
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9
Virtual Boundaries
•
Rectangle
•
•
Circle
•
•
Vertices at: (100,100), (100,750),
(300,750), (500,100)
Center at (320,430); Radius of 200 pixels
Ellipse
•
Center at (320,430); Semimajor axis of
200 pixels; Semiminor axis of 250 pixels
29 August 2007
10
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.
29 August 2007
11
Simple Control Law

UUV-gas algorithm:
 next
29 August 2007
  if outside boundary

  previous   0 if on boundary
  if inside boundary

12
Tracking the Virtual Rectangle

Limitations:



Observations:

29 August 2007
High speed creates wide
turning radius
Steering angle limited
by +/- 25o
Covers large amount of
space not relevant to
boundary
13
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
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14
Tracking the Virtual Ellipse

Limitations:


Observations:


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Same as rectangle
Covers large amount of
space not relevant to
boundary
Less risk of instability
15
Advanced Control Law

 next


Time-corrected algorithm:
~
 0.5  ( t   2 ref ) if outside boundary
  previous  
~
 0.5  ( t   2 ref ) if inside boundary
Includes time difference between
~t
crossing points on boundary,
Uses a reference angle, ref
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16
Slowly Tracking a Straight Line

Parameters:



Limitations:


Initial conditions create
wide starting angle
Observations:

29 August 2007
 = 0.0003
ref = 25o
Traces boundary more
efficiently than UUV-gas
17
Quickly Tracking a Straight Line

Parameters:




Higher speed creates
wider turning radius
Observations:

29 August 2007
Speed: 40% faster
Limitations:


 = 0.0003
ref = 25o
Offers no benefit over
UUV-gas
18
Algorithm Summary

UUV-gas

Inefficient



Covers irrelevant and redundant space
High probability of becoming unstable
Time-dependent algorithm

Efficient depending on speed of vehicle
29 August 2007
19
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
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20
Sensor Selection and Testing

Phototransistor

Fairchild Semiconductor


TT Electronics

29 August 2007
QRB1134
OPB608V
21
QRB1134

Characteristics:


29 August 2007
Linear decline in
current for distances
greater than 0.15
inches until 0.35
inches
Moderate current
drawn from collector
22
OPB608V

Characteristics:



29 August 2007
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
23
Sensor Selection

QRB1134


OPB608V


Optimal Range: 0.2 to 0.35 inches
Optimal Range: 1 to 1.5 inches
Selected: QRB1134


Ease of implementation on vehicle
Low power consumption
29 August 2007
24
Sensor Circuitry

QRB1134 possesses:

Sensor


Emitter



29 August 2007
IR Phototransistor
(behaves like NPN BJT)
IR LED
Phototransistor in
emitter follower
configuration
VDD set to 5 V
25
Height and Color
Characteristics
QRB1134 Phototransistor Characteristics
Black
Green
Yellow
Red
Teal
Gray
5
4
Voltage / V
•
3
2
•
1
•
0
0
0.1
0.2
0.3
0.4
0.5
Distance / inch
29 August 2007
0.6
0.7
0.8
0.9
1
Figure shows
dependence of height
and tape color on voltage
Voltage represents Vout
Distance is measured
from sensor tip to colored
tape
26
Height and Color
Characteristics
•
QRB1134 Phototransistor Characteristics Zoomed
Black
Green
Yellow
Red
Teal
Gray
Voltage / V
5
4
•
3
•
2
0.33
0.43
Distance / inch
29 August 2007
Statistically significant
difference
Yellow, red, teal
•
1
0
0.23
Testbed (gray), black,
green
•
Similar height-voltage
characteristics
Significantly different
from testbed, black,
green
27
Sensor Sweep Tests



29 August 2007
Sensor attached to
ruler at fixed height
Ruler dragged
across sample of
testbed with various
tapes
Voltage measured
with Dynon ELAB080 oscilloscope
28
Sweep Characteristics: Teal
•
•
•
•
•
29 August 2007
Height: 0.25 in
Average Speed: 1.6 in/s
Voltage Trigger: 0.8584 V
32K samples
Sample Frequency:
19.9883 KHz
29
Sweep Characteristics: Teal
•
•
•
•
•
29 August 2007
Height: 0.375 in
Average Speed: 1.7 in/s
Voltage Trigger: 0.6121 V
32K samples
Sample Frequency:
19.9883 KHz
30
Sweep Characteristics: Black
•
•
•
•
•
29 August 2007
Height: 0.25 in
Average Speed: 1.7 in/s
Voltage Trigger: 0.8174 V
32K samples
Sample Frequency:
19.9883 KHz
31
Sweep Characteristics: Black
•
•
•
•
•
29 August 2007
Height: 0.375 in
Average Speed: 1.7 in/s
Voltage Trigger: 0.5300 V
32K samples
Sample Frequency:
19.9883 KHz
32
Sensor Testing Summary

Fairfield Semiconductor QRB1134



Distance of 0.25 inches between sensor
and tape


Low power consumption
Stable region appropriate for vehicle
Sensor measures distinguishing voltages
Black and teal tapes chosen

Invisible to tracking cameras
29 August 2007
33
Sensor Mounting


Proximity sensor
removed
Voltage output sampled
by Atmega8 ADC



29 August 2007
ADC maps [0,5] volts to
integer values [0,1024]
Sensor glued to front of
car
Remaining hardware
unchanged
34
The Physical Boundary



29 August 2007
Boundary created by
junction of teal and
black tape
Modeled after
concave Jordan
curve
Experiment setup
35
CUSUM Filter

Necessary to filter collected ADC data


Reduce random error
Convert data to binary states
29 August 2007
36
CUSUM Filter cont.

Upper
0
k 0

U (k )  
min(max( 0, z (k )  B  cu  U (k  1)), U ) k  0

Lower
0
k 0

L( k )  
max(min( 0, z (k )  B  cl  L(k  1)), L )k  0
29 August 2007
37
CUSUM Filter cont.

Dual CUSUM filter


Three states from which to distinguish: onteal, on-black, on-testbed
Two CUSUM filters with concatenated
outputs

Outputs two binary bits:




29 August 2007
00
01
11
10
:= on testbed
:= on black tape
:= on teal tape
not used
38
CUSUM Parameter Testing

CUSUM1
(bit 1)





29 August 2007
Uo=350
cl=120
Cu=120
Lo=-350
B=150

CUSUM2
(bit 2)





Uo =950
cl =630
cu =630
Lo =-950
B =150
39
CUSUM Parameter Testing

CUSUM1
(bit 1)





29 August 2007
Uo=350
cl=120
Cu=120
Lo=-350
B=150

CUSUM2
(bit 2)





Uo =950
cl =630
cu =630
Lo =-950
B =150
40
CUSUM Parameter Testing

CUSUM1  CUSUM2
(bit 1)
(bit 2)





29 August 2007
Uo=350
cl=120
Cu=120
Lo=-350
B=150





Uo =1300
cl =630
cu =630
Lo =-1300
B =150
41
Final CUSUM Parameters

CUSUM1 (bit 1)







Uo=350
cl=120
Cu=120
Lo=-350
B=150

CUSUM2 (bit 2)





Uo =10,000
cl =630
cu =630
Lo =-10,000
B =150
Upper threshold set to 70% of maximum
Lower threshold set to 100% of minimum
29 August 2007
42
Final CUSUM Parameters
29 August 2007
43
State Transition Diagram
29 August 2007
44
Tracking the Physical
Boundary

Algorithm:


Parameter:

29 August 2007
UUV-gas
 = 15o
45
Tracking the Physical
Boundary cont.

Algorithm:


UUV-gas
Parameter:

 = 15o
29 August 2007
46
Tracking the Physical
Boundary cont.

Algorithm:


Parameters:


29 August 2007
Time-dependent
algorithm
 = 0.0003
ref = 25o
47
Conclusions and Suggestions
for Future Work


Further testing needed to critically
determine effectiveness of timedependent algorithm
Implement cooperative boundary
tracking


P2P networking between multiple vehicles
Implement multiple sensors

Reduce error variance
29 August 2007
48
Acknowledgments
•
•
•
•
Zhipu Jin
Andrea L. Bertozzi
Andrew Bernoff
Rachel Levy
29 August 2007
49