yves boussemart anna massie brian mekdeci optimization of a heterogeneous unmanned mission
TRANSCRIPT
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Yves BoussemartAnna Massie
Brian Mekdeci
Optimization of a heterogeneous unmanned
mission
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Motivation
2
Unmanned VehiclesWide variety of uses:
SurveillanceSearch & rescueMiningDull, Dirty, DangerousProblem:
Given a missionOptimal # of UVs?Optimal operator
strategies
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Problem Formulation
3
Multiple heterogeneous UVs, single humanQueuing problem
Human is serverEvents are when UVs need attentionService is when human interacts
Discrete event simulator
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Model Disciplines
4
Cognitive Psychology
UV Operations
Queuing Theory
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Optimization
5
Objectives (J)PerformanceCostUtilization
Design vector (x)# of vehicles
MALE, HALE,UUVOperator strategySwitching
PrioritiesRe-plan
• Parameters(p)– Mission
– Scoring methods– Time
– Vehicle Spec– Arrival rates– Service times– Costs
• Constraints (g)• Queuing• Maximum # of
vehicles
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Model Diagram
6
Mission
Situational Awarenes
s
Human Server
Performance
Parameters
Design variables
Constraints
OptimizationTarget
Objectives
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Single Objective FormulationGradient Based (SQP)
• JScore: 159.39
• X*:– NH=20– NM=12– NU=12– RS=53.0– SS=1 [UAV>UV]
• Time~20 seconds
• JScore: 276.004
• X*:– NH=19– NM=5– NU=1– RS=88.06– SS=1 [UAV > UV]
• Time~220 seconds
Simulated Annealing
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Algorithm Tuning – Simulated Annealing
Choose cooling schedule to optimize performance (exp. cooling,To=100, neq=5, nfrozen=3): dT = 0.75
Performance vs dT
175
200
225
250
275
300
0 0.2 0.4 0.6 0.8 1
dT
Sco
re
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Sensitivity Analysis
NH NM NU RS SS PERF
Upper Bounds 20 20 20 100.0 -
Lower Bounds 1 1 1 1.0 1 6.0
Initial Vector (x0) 1 1 1 10.58 3 12.27
Basis (x*) 20 12 15 53.0 1 159.39
∆ # of HALEs 22 12 15 53.0 1 171.55
∆ #of MALEs 20 13 15 53.0 1 161.47
∆ #of UUVs 20 12 17 53.0 1 156.2
∆ Re-plan Strategy 20 12 15 59.0 1 161.45
∆ Switching Strategy 20 12 15 53.0 2 67.62
∆ Switching Strategy 20 12 15 53.0 3 101.5
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Multi-objective OptimizationPareto Front: Weighted Sum & Gradient Based
Optimization(much faster than heuristic based)
1 s.t.,min 3
1cos
21 i
itscore
mo sf
Cost
sf
ScoreJ SfCost= (276/76500)=3.6E-3
Sfscore= 1.0
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Post Optimality AnalysisYerkes-Dodson
Pareto
Cost
Score
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Multi-objective optimizationFull Factorial
3061 Total points502 non-
dominated solutions
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Post Optimality AnalysisRe-plan strategy and
Switching Strategy7*3 ANOVA to test effect on
score and utilization
Pareto Front Design Points All Design Points
RS SS RS SS
Score F 3.644 6.793 0.132 8.19
p 0.002 0.001 0.992 <0.001
Utilization F 10.937 2.261 35.098 7.119
p <0.001 0.105 <0.001 0.001
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Post Optimality AnalysisNumber of HALEs, MALEs, and UUVs
5*5*5 ANOVA to test score, utilization and costAll independent variables significant for all
three dependent variables
All Points Pareto Frontier Points
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Design trade offNumber of Vehicles:
Higher number leads to higher score, cost and utilization
Switching strategy:Using a priority strategy of UAVs over UUVs
allows a higher score, while maintaining similar cost and utilization
Replan StrategyHaving a higher replan time of ~20 seconds
does not significantly increase the score, utilization or cost
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Lessons Learned1. Neither the gradient based (170) nor the
simulated annealing (276) algorithm was able to find the absolute maximum score (298)
2. Matlab had a finite # of times that it could call our java program – making it the largest constraint on the SA and full factorial analysis
3. Difficulty using interval and categorical data
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ConclusionsCan optimize a model for human-system
interaction in the context of unmanned vehicle supervision
Can forecast the capacity of a human given certain mission parametersLarger number of vehicles increased the cost
linearly, but the cognitive capabilities of an operator limited how high utilization and score could increase
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Thanks!
Questions?
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Number of MALEs
Number of HALEs
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Number of UUVs
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• Re-plan strategy and Switching Strategy– 7*3 ANOVA to test effect on score and utilization
• Pareto Front– Score: Significant Difference for both RS (F=3.644, p=.002) and SS
(F=6.793, p=.001)– Cost: Significant Difference for both RS (F=3.982, p=.001) and SS
(F=6.668, p=.001)– Utilization: Significant Difference for both RS (F=3.644, p=.002) and
SS (F=6.793, p=.001)• Non-Pareto Front
– Score: Significant Difference for SS (F=8.190, p<.001) but not SS (F=0.132, p=.992)
– Cost: Significant Difference for both RS (F = 6.789, p<.001) and SS (F=149.14, p<.001)
– Utilization: Significant Difference for both RS (F=35.098, p<.001) and SS (F = 7.119, p=.001)
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Pareto
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Pareto