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Evolving Cooperative Strategies for UAV Teams

Presented by: Joseph Vannucci. Evolving Cooperative Strategies for UAV Teams. GECCO ’05 Marc D. Richards Darrell Whitley J.Ross Beveridge Colorado State, Fort Collins. Control of Autonomous Agents Fall 2005 Stan Franklin. Outline. Introduction to UAV Agent

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Evolving Cooperative Strategies for UAV Teams

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  1. Presented by: Joseph Vannucci Evolving Cooperative Strategies for UAV Teams GECCO ’05 Marc D. Richards Darrell Whitley J.Ross Beveridge Colorado State, Fort Collins Control of Autonomous Agents Fall 2005 Stan Franklin Joseph Vannucci - University of Memphis

  2. Outline • Introduction to UAV Agent • Overview of Genetic Programming • Controlling UAVs with GPs Joseph Vannucci - University of Memphis

  3. Evolutionary Strategy- Generation for UAVs • What’s a UAV • A few UAV applications • A Strategy? Gnat Predator Global Hawk Joseph Vannucci - University of Memphis

  4. Environment • The authors wrote a simulator • Funded by NSF, not DARPA • 2-dimensional, double precision model • Never take off, never land • The “ground” is composed of: • Home-base position(s) • A grid of virtual beacons Joseph Vannucci - University of Memphis

  5. Environment (cont.) • Segments of the area can be designated as search areas • Segments can be designated no-fly-zones • Hostile enemies can present themselves • UAVs must avoid • UAVs can be destroyed Joseph Vannucci - University of Memphis

  6. Sensors • UAVs enjoy global map information • Basic sensors are • 2-d coordinates, rounded to the nearest meter • Locations of other UAVs • Locations of search areas • Locations of no-fly-zones • Locations of enemies • Mission-completeness status Joseph Vannucci - University of Memphis

  7. Motivations • Search the designated space • Avoid no-fly-zones • Avoid hostile enemies • Get back to home-base • Complete the mission and get home safe Joseph Vannucci - University of Memphis

  8. Effectors • Flight control • Max turn rate • Speed • Beacons, when located in a search area and swept by the UAVs sensors, are flagged as being searched. Joseph Vannucci - University of Memphis

  9. Control - Genetic Programming • So we now have an idea of what is does, now we find out why it does it. • Genetic Programming • Basically a Genetic Algorithm for parse trees • Most commonly and easily done in LISP • Iteratively generates programs to solve tasks • Evolution is based on how well each iteration does against the last • Theory is being heavily researched currently, could be a super-set of Genetic Algorithms Joseph Vannucci - University of Memphis

  10. Outline • Introduction to UAV Agent • Overview of Genetic Programming • Controlling UAVs with GPs Joseph Vannucci - University of Memphis

  11. Programs Joseph Vannucci - University of Memphis

  12. Programs (cont.) Joseph Vannucci - University of Memphis

  13. GPs are population based • Many different programs are tried out on a certain problem. The program that performs better is more “fit”. So each one is assigned a scalar fitness, usually between 0 and 1. • The programs are generated initially at random and then, based on certain criteria (fitness) pieces of the best programs are extracted and carried forward into future populations. Joseph Vannucci - University of Memphis

  14. Flowchart for GP Joseph Vannucci - University of Memphis

  15. ExampleParse Tree Joseph Vannucci - University of Memphis

  16. Exploration Recombination Example Parents Children Mutation: Small probability of throwing away a particular node and generating a new parse tree in its place. Joseph Vannucci - University of Memphis

  17. Exploitation • Selection: What parents to pick • Replacement: What offspring to put back into the population • Selection is primary concerned with fitness • Offspring is often concerned mostly with clustering (how close one program is to another in structure) Joseph Vannucci - University of Memphis

  18. Outline • Introduction to UAV Agent • Overview of Genetic Programming • Controlling UAVs with GPs Joseph Vannucci - University of Memphis

  19. Planning • The entire strategy is generated offline and no re-planning is done during flight. • The entire strategy is relative and reactive. • No explicit flight plans or directions are ever stored in the plan. • All UAVs currently share the same decision tree • Plans are robust enough to allow for differing starting and ending locations. Joseph Vannucci - University of Memphis

  20. Fitness • Minimization of mission time cost • Failed mission fitness is computed by proportion of search area swept. • Penalties for lost UAVs • Collisions • No-fly-zone • Discovered enemies Joseph Vannucci - University of Memphis

  21. Sample of available ‘moves’ Joseph Vannucci - University of Memphis

  22. Example Strategy Joseph Vannucci - University of Memphis

  23. In Nuce • GP control of multiple autonomous UAVs • 2-d environment with areas to search and avoid • Test simulations that fail repeatedly to generate a successful run • Genetic programming and genetic algorithms are very similar • Some details if needed Joseph Vannucci - University of Memphis

  24. Questions???????? Joseph Vannucci - University of Memphis

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