1. UAV Navigation by Expert System for Contaminant Mapping George S. Young Yuki Kuroki, Sue Ellen Haupt

2. Goals Background 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 AlgorithmBackground 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 Constraints Number of sensors & time to solutionConstraints Mission Identify a total of 4 parameters (source strength, source location (x,y) and wind direction) describing the release using mobile sensorsMission Identify a total of 4 parameters (source strength, source location (x,y) and wind direction) describing the release using mobile sensors

3. DispersionmodelDispersionmodel GaussianplumeGaussianplume Gaussian puff puffGaussian noisenoise Identical twin experiment System Components Model inverter inverterModel GeneticAlgorithmGeneticAlgorithm Nelder-Mead downhill simplex Nelder-Mead Observing system systemObserving Fixedconcentration sensor sensorFixedconcentration AutonomousaircraftAutonomousaircraft

4. Dispersion model Gaussian Puff An instantaneous release Gaussian Puff An instantaneous release 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 C: the concentration, Q: the emission mass  t: 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 mass  t: the length of time of the release itself t: the time since the release U: the wind speed  : the standard deviations h: source height

5. Hybrid Genetic Algorithm (GA) Mutation Mate Selection Mating Optimization with a GA Evaluate cost Converge? Initialize population Solution no Yes Exchange information Between parents Combine best of last generation Nelder Meade Downhill Simplex Fine-tune GA solution

6. GA 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# 2. Concerns? Minimizing CPU time Minimizing CPU time Increasing accuracy Increasing accuracy 2. Concerns? Minimizing CPU time Minimizing CPU time Increasing accuracy Increasing accuracy 3. Best combination? Population size = 40 Population size = 40 Mutation rate = 0.32 Mutation rate = 0.32 Iteration counts = 640 Iteration counts = 640 3. Best combination? Population size = 40 Population size = 40 Mutation rate = 0.32 Mutation rate = 0.32 Iteration counts = 640 Iteration counts = 640

7. Experimental 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

8. Autonomous 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 virutal Why virutal Test in a fully controlled environment Test UAV naviagtion algorthims without societal risk Why virutal Why virutal Test in a fully controlled environment Test UAV naviagtion algorthims without societal risk

9. Information 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

10. Expert System Design PlumePuffDifference – 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

11. Plume Expert System Plume decision logic pass1 actual source sensor pass2 -700700 Route 2 -700700 Route 1 300m Route 3 -700700

12. Puff Expert System Puff decision logic Origin Sensor pass1 pass2 Pass1 Max Conc N yy a n   Mean wind direction (-7000,7000)

13. Flight Track – Plume Example

14. Flight Track – Puff Example

15. Testing Architecture Identical twin experiment Create data Noise Contaminate 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

16. Plume Results 41020504102050 WindConcentration XY 0.2m 0.3m 0.05  0.02 [kg/s]

17. Puff Results 41020504102050 WindConcentration XY 3m 55m 0.3  0.02 [kg/s]

18. Conclusions ExperimentalSetupGaussian Puff UAV Discussion Idential twin 1 fixed sensor Single UAV or 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 Gaussian plume UAV plume UAV UAV Ensemble Expert system naviagaion Solves single- sensor source characterization

19. Future 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

20. Acknowledgements 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

21. Questions?