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Optimization of a heterogeneous unmanned mission. Yves Boussemart Anna Massie Brian Mekdeci. Motivation. Unmanned Vehicles Wide variety of uses: Surveillance Search & rescue Mining Dull, Dirty, Dangerous Problem: Given a mission Optimal # of UVs? Optimal operator strategies.
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Optimization of a heterogeneous unmanned mission Yves Boussemart Anna Massie Brian Mekdeci
Motivation • Unmanned Vehicles • Wide variety of uses: • Surveillance • Search & rescue • Mining • Dull, Dirty, Dangerous • Problem: • Given a mission • Optimal # of UVs? • Optimal operator strategies
Problem Formulation • Multiple heterogeneous UVs, single human • Queuing problem • Human is server • Events are when UVs need attention • Service is when human interacts • Discrete event simulator
Model Disciplines Cognitive Psychology UV Operations Queuing Theory
Optimization • Objectives (J) • Performance • Cost • Utilization • Design vector (x) • # of vehicles • MALE, HALE,UUV • Operator strategy • Switching • Priorities • Re-plan • Parameters(p) • Mission • Scoring methods • Time • Vehicle Spec • Arrival rates • Service times • Costs • Constraints (g) • Queuing • Maximum # of vehicles
Model Diagram Design variables Mission Constraints Parameters Situational Awareness Optimization Target Human Server Performance Objectives
Single Objective Formulation Gradient Based (SQP) • Simulated Annealing • JScore: 276.004 • X*: • NH=19 • NM=5 • NU=1 • RS=88.06 • SS=1 [UAV > UV] • Time~220 seconds • JScore: 159.39 • X*: • NH=20 • NM=12 • NU=12 • RS=53.0 • SS=1 [UAV>UV] • Time~20 seconds VS
Algorithm Tuning – Simulated Annealing Choose cooling schedule to optimize performance (exp. cooling,To=100, neq=5, nfrozen=3): dT = 0.75
Multi-objective Optimization Pareto Front: Weighted Sum & Gradient Based Optimization (much faster than heuristic based) SfCost= (276/76500)=3.6E-3 Sfscore= 1.0
Post Optimality Analysis Yerkes-Dodson Score Cost Pareto
Multi-objective optimization • Full Factorial • 3061 Total points • 502 non-dominated solutions
Post Optimality Analysis • Re-plan strategy and Switching Strategy • 7*3 ANOVA to test effect on score and utilization
Post Optimality Analysis • Number of HALEs, MALEs, and UUVs • 5*5*5 ANOVA to test score, utilization and cost • All independent variables significant for all three dependent variables All Points Pareto Frontier Points
Design trade off • Number 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 Strategy • Having a higher replan time of ~20 seconds does not significantly increase the score, utilization or cost
Lessons Learned • Neither the gradient based (170) nor the simulated annealing (276) algorithm was able to find the absolute maximum score (298) • 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 • Difficulty using interval and categorical data
Conclusions • Can optimize a model for human-system interaction in the context of unmanned vehicle supervision • Can forecast the capacity of a human given certain mission parameters • Larger number of vehicles increased the cost linearly, but the cognitive capabilities of an operator limited how high utilization and score could increase
Thanks! Questions?
Number of HALEs Number of MALEs
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)