Optimization of a heterogeneous unmanned mission

1. Optimization of a heterogeneous unmanned mission Yves Boussemart Anna Massie Brian Mekdeci

2. Motivation • Unmanned Vehicles • Wide variety of uses: • Surveillance • Search & rescue • Mining • Dull, Dirty, Dangerous • Problem: • Given a mission • Optimal # of UVs? • Optimal operator strategies

3. 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

4. Model Disciplines Cognitive Psychology UV Operations Queuing Theory

5. 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

6. Model Diagram Design variables Mission Constraints Parameters Situational Awareness Optimization Target Human Server Performance Objectives

7. 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

8. Algorithm Tuning – Simulated Annealing Choose cooling schedule to optimize performance (exp. cooling,To=100, neq=5, nfrozen=3): dT = 0.75

9. Sensitivity Analysis

10. Multi-objective Optimization Pareto Front: Weighted Sum & Gradient Based Optimization (much faster than heuristic based) SfCost= (276/76500)=3.6E-3 Sfscore= 1.0

11. Post Optimality Analysis Yerkes-Dodson Score Cost Pareto

12. Multi-objective optimization • Full Factorial • 3061 Total points • 502 non-dominated solutions

13. Post Optimality Analysis • Re-plan strategy and Switching Strategy • 7*3 ANOVA to test effect on score and utilization

14. 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

15. 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

16. 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

17. 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

18. Thanks! Questions?

21. Number of HALEs Number of MALEs

22. Number of UUVs

23. 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)

24. Pareto

29. Pareto