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UAV Navigation by Expert System for Contaminant Mapping. George S. Young Yuki Kuroki, Sue Ellen Haupt. Goals. Background Source and wx information needed for contaminant modeling Long et al.(2008) demonstrated the use of Gaussian puff to back- - PowerPoint PPT Presentation
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UAV Navigation by Expert System for Contaminant
Mapping
UAV Navigation by Expert System for Contaminant
MappingGeorge S. Young
Yuki Kuroki, Sue Ellen Haupt
GoalsGoals
BackgroundBackground• Source and wx information needed for contaminant modeling• Long et al.(2008) demonstrated the use of Gaussian puff to back- calculate the source characteristics via a Genetic Algorithm
BackgroundBackground• Source and wx information needed for contaminant modeling• Long et al.(2008) demonstrated the use of Gaussian puff to back- calculate the source characteristics via a Genetic Algorithm
ConstraintsConstraints• Number of sensors & time to solution
ConstraintsConstraints• Number of sensors & time to solution
MissionMission• Identify a total of 4 parameters (source strength, source location (x,y) and wind direction) describing the release using mobile sensors
MissionMission• Identify a total of 4 parameters (source strength, source location (x,y) and wind direction) describing the release using mobile sensors
Dispersion Dispersion modelmodel
Dispersion Dispersion modelmodel
Gaussian Gaussian plumeplume
Gaussian Gaussian plumeplume
GaussianGaussian puffpuff
GaussianGaussian puffpuff
noisenoisenoisenoise
Identical twin experiment
System ComponentsSystem Components
ModelModel inverterinverterModelModel
inverterinverter
Genetic Genetic AlgorithmAlgorithmGenetic Genetic AlgorithmAlgorithm
Nelder-Mead Nelder-Mead downhill simplexdownhill simplexNelder-Mead Nelder-Mead
downhill simplexdownhill simplex
ObservingObserving systemsystem
ObservingObserving systemsystem
Fixed Fixed concentrationconcentration
sensorsensor
Fixed Fixed concentrationconcentration
sensorsensor
Autonomous Autonomous aircraftaircraft
Autonomous Autonomous aircraftaircraft
Dispersion modelDispersion model
2
2
2
2
2
2
2
2
5.1 2exp
2exp
2exp
2exp
2 z
er
z
er
y
r
x
r
zyx
r
HzHzyUtxtQC
Gaussian Puff• An instantaneous release
Gaussian Puff• An instantaneous release
true conc
1 2 3 4 5 6 7 81
2
3
4
5
6
7
8
1
2
3
4
5
6
7x 10
-6
Gaussian plume• A time averaged continuous emission• wind speed, eddy diffusivity are const• Mass is conserved
Gaussian plume• A time averaged continuous emission• wind speed, eddy diffusivity are const• Mass is conserved
2
2
2
2
2
2
2exp
2exp
2exp
2),,(
yzzzy
yhzhz
u
QzyxC
C: the concentration, Q: the emission masst: the length of time of the release itself t: the time since the release U: the wind speed : the standard deviations h: source height
C: the concentration, Q: the emission masst: the length of time of the release itself t: the time since the release U: the wind speed : the standard deviations h: source height
Hybrid Genetic Algorithm (GA)
Hybrid Genetic Algorithm (GA)
Mutation
Mate Selection
Mating
Optimization with a GA
Optimization with a GA
Evaluatecost Converge?
Initialize population
Solution
no Yes
Exchange informationBetween parents
Combine best of last generation
Nelder MeadeDownhill Simplex
Fine-tune GA solution
GA TuningGA Tuning
1. What we did?• Determine best combination of GA parameters
1. What we did?• Determine best combination of GA parameters
Pseudo-Runtime= pop*it# Pseudo-Runtime= pop*it#
102
103
104
105
106
107
10-3
10-2
10-1
100
101
Pseudo Runtime
Err
or
Ma
gn
itu
de
Error Sensitivity to GA parameters for snr = 5
0.010.020.040.080.160.32
105
10-4
10-3
10-2
10-1
100
Pseudo Runtime
Err
or M
agni
tude
q=1 Error Sensitivity to GA parameters for snr = 5
0.010.020.040.080.160.32
2. Concerns?• Minimizing CPU timeMinimizing CPU time• Increasing accuracyIncreasing accuracy
2. Concerns?• Minimizing CPU timeMinimizing CPU time• Increasing accuracyIncreasing accuracy
3. Best combination?• Population size = 40Population size = 40• Mutation rate = 0.32 Mutation rate = 0.32 • Iteration counts = 640Iteration counts = 640
3. Best combination?• Population size = 40Population size = 40• Mutation rate = 0.32 Mutation rate = 0.32 • Iteration counts = 640Iteration counts = 640
Experimental SetupExperimental Setup
• Wind direction 270 degrees
• Random source location in upwind half of domain
• Single fixed sensor in downwind half of domain
• UAV takes off from upwind corner of domain
– Worst case position
– Launches on first detection by fixed sensor
• UAV speed is 4 times wind speed
Autonomous AircraftAutonomous Aircraft
Why use aircraft?Why use aircraft?• Equipping the UAV with GPS & concentration sensor • Avoid the cost of a dense array of fixed sensors
Why use aircraft?Why use aircraft?• Equipping the UAV with GPS & concentration sensor • Avoid the cost of a dense array of fixed sensors
Why autonomous?Why autonomous?• AI required for rapid decision making• Ensemble of manned aircraft would be too expensive
Why autonomous?Why autonomous?• AI required for rapid decision making• Ensemble of manned aircraft would be too expensive
Why virutalWhy virutal• Test in a fully controlled environment• Test UAV naviagtion algorthims without societal risk
Why virutalWhy virutal• Test in a fully controlled environment• Test UAV naviagtion algorthims without societal risk
Information FlowInformation Flow
• UAV AI needs observed & modeled concentration fields to navigate
• UAV AI needs observed & modeled concentration fields to navigate
• GA needs UAV wind & concentration observations to locate source
• GA needs UAV wind & concentration observations to locate source
• Forward model needs wind and source locaton to predict concentration field
• Forward model needs wind and source locaton to predict concentration field
Expert System DesignExpert System Design
•Plume Puff Difference
– How many passes
through plume?
– How much separation
in space?
– How many passes
through puff?
– How much separation
in time?
– Why the difference?
Amount of data needed
Plume Expert SystemPlume Expert System
• Plume decision logic
pass1actual source
sensorpass2
-700 700
700 Route 2
-700 700
Route 1
300m
Route 3
-700 700
Puff Expert SystemPuff Expert System
• Puff decision logic
aircraftaircraft u
anNt
22
windpuff u
axt
0coscossinsin 2121
1tanx
nN
2tantan n
ax
Origin
Sensor
pass1
pass2
Pass1 Max Conc
N
y
a
n
Mean wind direction
(-7000,7000)
Flight Track – Plume Example
Flight Track – Plume Example
Flight Track – Puff ExampleFlight Track – Puff Example
Testing ArchitectureTesting Architecture
Identical twin experimentCreate data
NoiseContaminate data
Collect data
• Monte Carlo testing of UAV non-collaborative ensemble• Pseudo-random initial population and sensor location
Hybrid GA optimizing
Ensemble size
• Ensemble median to back calculate source and wind dir.• Monte Carlo mean of ensemble median will be shown
Plume ResultsPlume Results
245
250
255
260
265
270
275
280
285
290 wind direction distribution
Ensembel member
win
d di
rect
ion
Route1
Route2Route3
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4 strength distribution
Ensembel member
stre
ngth
dis
tribu
tion
Route1
Route2Route3
-60
-40
-20
0
20
40
60
80 x distribution
Ensembel member
x di
strib
utio
n
-30
-20
-10
0
10
20
30
40 y distribution
Ensembel member
y di
strib
utio
n
4 10 20 50 4 10 20 50
Wind Concentration
X Y
0.2m0.3m
0.05 0.02 [kg/s]
Puff ResultsPuff Results
268
268.5
269
269.5
270
270.5
271
271.5 wind direction distribution
Ensembel member
win
d di
rect
ion
0.75
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
1.25 strength distribution
Ensembel member
stre
ngth
dist
ribut
ion
-6015
-6010
-6005
-6000
-5995
-5990
-5985 x distribution
Ensembel member
x di
strib
utio
n
-300
-250
-200
-150
-100
-50
0
50
100
150
200 y distribution
Ensembel member
y di
strib
utio
n
4 10 20 50 4 10 20 50
Wind Concentration
X Y
3m55m
0.3 0.02 [kg/s]
ConclusionsConclusions
ExperimentalExperimentalSetupSetup
GaussianGaussianPuff UAVPuff UAV
DiscussionDiscussion
• Idential twin• 1 fixed sensor• Single UAVor • UAV ensemble• No cooperation
• 2 flight legs• 1 UAV• UAV navigation by expert system• GA optimization for source & dir• 1400m domain• Results improve
• 6 flight legs• 20 UAVs• Median Solution• 14km domain • Greater tracking challenge• Most UAVs succeed
GaussianGaussian plume UAVplume UAV
• UAV Ensemble • Expert system naviagaion
Solves single-sensor source
characterization
Future WorkFuture Work
Goal: Compensate for the tight time constraints inherent in emergency management
• Cooperation between Multiple UAVs• Improve Gaussian Puff Model Navigation• Actual UAVs• Field Test
AcknowledgementsAcknowledgements
• The second author was supported by Japan Ground Self Defense Forces during this study
• Thanks to J. Wyngaard, K. Long, A. Annunzio, A. Beyer-Lout, L. Rodriguez for insights and advice
Questions?Questions?