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UW Computer Science DepartmentUW Computer Science Department
Strategies for Multi-Asset Surveillance
Dr. William M. Spears
Dimitri Zarzhitsky
Suranga Hettiarachchi
Wesley Kerr
University of Wyoming
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UW Computer Science DepartmentUW Computer Science Department
Scenario
Foliage detector
Target detector
Maximize the number of T targets found by α assets.
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UW Computer Science DepartmentUW Computer Science Department
Forest Generator
L x L environmentwith T targetsand foliage.
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UW Computer Science DepartmentUW Computer Science Department
Asset Control
• Behavior-based asset controllers.– Straight Line (SL)
• Assets “bounce” off boundary walls. Ignores foliage.
– Straight Line Avoid Forest (SLAF)• Like SL but also reverse course if encounter foliage.
– Super Straight Line Avoid Forest (SSLAF)• Like SLAF but move opposite to center of mass of
foliage (a more sophisticated foliage sensor).
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UW Computer Science DepartmentUW Computer Science Department
Target Control
• Stationary targets for baseline study.
• “Hiding Gollum” target controller:– Targets try to cross from left to right through
environment while hiding in foliage.
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Stationary Targets
Why is SLAF so poor and SSLAF so good?
0
20
40
60
% Targets Found
10 20 30 40 50 60 70
% Foliage
Performance on Stationary Targets
SL
SLAF
SSLAF
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Asset Coverage Maps
SL SLAF SSLAF
SL provides uniform coverage of the space. SSLAF provides increaseduniform coverage of the non-foliage space. But SLAF misses entire regions.
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Gedanken Experiment
What if the targets move slowly from left to right? Will the prior results change?
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UW Computer Science DepartmentUW Computer Science Department
Gollum Targets
Why is SLAF so good?
0
20
40
60
80
% Targets Found
10 20 30 40 50 60 70
% Foliage
Performance on Gollum Targets
SL
SLAF
SSLAF
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Probabilistic AnalysisController 1:Uniformly coverwhole area (like SL).
Controller 4:Uniformly coverone row (worst case SLAF).
Controller 2:Uniformly coverone column (bestcase SLAF).
Controller 3:Uniformly coverone diagonal (average case SLAF).
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UW Computer Science DepartmentUW Computer Science Department
Area Controller
t
t
t
tt
t
S
rv
r
v
v
LS
r
LS
STE
t
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
111found] targets[
2
2
Expected number of timesteps for asset to cover area.
Visibility timeof target.
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UW Computer Science DepartmentUW Computer Science Department
Column Controller
t
t
t
t
tt
S
rd
rv
r
v
v
LS
d
LS
STE
t
2thcolumn wid
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
111found] targets[
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UW Computer Science DepartmentUW Computer Science Department
Diagonal Controller
t
t
t
t
tt
S
rd
rv
r
v
v
LS
d
LS
STE
t
2thcolumn wid
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
22111found] targets[
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UW Computer Science DepartmentUW Computer Science Department
Row Controller
height row2
2found] targets[
t
t
rL
TrE
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UW Computer Science DepartmentUW Computer Science Department
Comparison of Controllers
SLAF works well on moving targetsbecause diagonal controller performance is like column controller performance.
Comparison of Controllers
0
0.2
0.4
0.6
0.8
1
1.2
0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8
target velocity
% t
arg
ets
fo
un
d Area Controller
Colum n/DiagonalController
Row Controller
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UW Computer Science DepartmentUW Computer Science Department
Summary
• Developing predictive mathematical theory for multiple assets performing surveillance.– Currently includes number of assets, their speed, target
speed, and environment size.
– Working on including probability of detection (a noisy sensor), percentage of foliage, and time limits on mission length.
• Goal is to provide mathematical tools to yield an optimal strategy for a surveillance mission.