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UAV Navigation by Expert System for Contaminant Mapping

UAV Navigation by Expert System for Contaminant Mapping. George S. Young Yuki Kuroki, Sue Ellen Haupt. Goals. Background Source and wx information needed for contaminant modeling Long et al.(2008) demonstrated the use of Gaussian puff to back-

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UAV Navigation by Expert System for Contaminant Mapping

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  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 Algorithm • Constraints • Number of sensors & time to solution • Mission • Identify a total of 4 parameters (source strength, source location (x,y) • and wind direction) describing the release using mobile sensors

  3. Dispersion model Model inverter Observing system Genetic Algorithm Fixed concentration sensor Identical twin experiment Gaussian plume noise Autonomous aircraft Gaussian puff Nelder-Mead downhill simplex System Components

  4. Gaussian plume • A timeaveraged continuous emission • wind speed, eddy diffusivity are const • Mass is conserved • Gaussian Puff • An instantaneous release 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 Dispersion model

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

  6. 3. Best combination? • Population size = 40 • Mutation rate = 0.32 • Iteration counts = 640 GA Tuning • What we did? • Determine best combination • of GA parameters • 2. Concerns? • Minimizing CPU time • Increasing accuracy Pseudo-Runtime= pop*it#

  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? • Equipping the UAV with GPS & concentration sensor • Avoid the cost of a dense array of fixed sensors • Why autonomous? • AI required for rapid decision making • Ensemble of manned aircraft would be too expensive • Why virutal • Test in a fully controlled environment • Test UAV naviagtionalgorthims without societal risk

  9. Information Flow • UAV AI needs observed & modeled concentration • fields to navigate • GA needs UAV wind & concentration observations • to locate source • Forward model needs wind and source locaton to • predict concentration field

  10. Expert System Design Amount of data needed • Plume Puff Difference • How many passes through plume? • How much separation in space? • Why the difference? • How many passes through puff? • How much separation in time?

  11. pass1 actual source sensor pass2 Route 1 Route 2 Route 3 300m -700 700 -700 700 Plume Expert System • Plume decision logic 700 -700 700

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

  13. Flight Track – Plume Example

  14. Flight Track – Puff Example

  15. Collect data Ensemble size Hybrid GA optimizing Testing Architecture • Monte Carlo testing of UAV non-collaborative ensemble • Pseudo-random initial population and sensor location Identical twin experiment Create data Noise Contaminate data • Ensemble median to back calculate source and wind dir. • Monte Carlo mean of ensemble median will be shown

  16. 0.05 0.02 [kg/s] 4 4 10 10 20 20 50 50 0.2m 0.3m Plume Results Wind Concentration X Y

  17. 0.3 0.02 [kg/s] 4 4 10 10 20 20 50 50 55m 3m Puff Results Wind Concentration X Y

  18. UAV Ensemble • Expert system • naviagaion Solves single-sensor source characterization Conclusions Experimental Setup Gaussian plume UAV Gaussian Puff UAV Discussion • 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 • Idential twin • 1 fixed sensor • Single UAV • or • UAV ensemble • No cooperation

  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?

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