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PEG Breakout

PEG Breakout. Mike, Sarah, Thomas, Rob S., Joe, Paul, Luca, Bruno, Alec. What’s the goal?. Develop groundbreaking control Policies that bound the time to capture the evader Pursuer(s) to catch dumb and smart evader(s) in bounded time Proving it in the real world

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PEG Breakout

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  1. PEG Breakout Mike, Sarah, Thomas, Rob S., Joe, Paul, Luca, Bruno, Alec

  2. What’s the goal? • Develop groundbreaking control Policies that bound the time to capture the evader • Pursuer(s) to catch dumb and smart evader(s) in bounded time • Proving it in the real world • Short Term (1yr): RC Car RoboMotes • Long Term (2-3yrs): Macro Robots and UAVs • ASAP

  3. Pursuer Evader Game Overview • N pursuer chasing M Evader on a 2D grid • Pursuer: • Minimize the expected capture time • Evader: • Not captured by some time bound • Real time dynamic programming of this problem is intractable • Unreliable feedback with inherent errors on sensory data

  4. Narrowing down the problem • 1 pursuer and 1 evader • Scale speed of the cars to compensate for network delay • Retain history and prediction to cope with delay • Given jitter/delay model and maximum error bound on estimation, bound the time to capture the evader • 1 hop communication to the pursuer and evader

  5. Interface of different components • Position Estimation • X,Y for Pursuer and Evader with delay and error bound • Cars Control • Series of speed, angle commands

  6. Action 1: Sense and Estimate • On line position calibration to give error bound • Make time of flight estimation work • Modeling delay and error • need to run and characterize the sensor network

  7. Action 2: Close the loop • Computation of pursuer’s movement on MATLAB • Run with MATLAB simulation with traces • Send out commands to pursuer • Easy way to test out different algorithm in MATLAB • Control Evader • Same problem of pursuer’s algorithm but completely opposite • Have algorithms compete on both side at the same time and compare

  8. Pursuer / Evader Development Kit • Sensor Network Provides P&E Location Estimates at > 1 Hz • These estimates can be modulated with different precision and delay • Magnetometer on the car • Acoustic / Sounder on the car • Centralized car control scheme • Position Estimates go to the base station • Mica RoboMotes accept commands to move • MATLAB UI • Test out 5 different strategies per day

  9. Ideas to Pursue • Speed Up Position Estimates to 5-10Hz OR Reengineer Cars to go Slow • Car control with magnetometer giving car’s heading • Compass heading • Explore using sound and magnetic field to estimate position of pursuer/evader • Pursuer generates AC magnetic field • Needs a localization that supports multiple agents (3+3 MAX)

  10. Specification • Pursuer/Evader Overview • N number of pursuer • 2D mobile robot • Same capabilities • Minimize the expected capture time • Pursuer is within some range of the evader • Pursuer can go at different speed

  11. Game: dynamic programming • Not possible to compute in real time • Use heuristics • 8 cells around you • Creates a map • Simplest: cells that are on with probability one • Cells that are far away have some probability < 1 • Do a local finding by pursuer • Sensor networks augment it • Color detection on the evader • Laser pointing • Helicopter has a camera

  12. Design a policy • Map one or more pursuer to the evader • Narrow it to one evader • Tracking controller that minimizes the distance

  13. Problem • Loss, delay, • Delay corresponds to speed • Failure model • Retain your history • Loss is lack of update

  14. Calibration

  15. Leader Election

  16. Reliable Transport

  17. Error Model • Using the sensor network to quantify expected capture time

  18. Separate network channel • Pursuer and Evader

  19. Pursuer can ask network • Where did the evader go?

  20. Control • Sensing is distributed • Stability of the system • Introduce new constraints

  21. Demo • Step 1: • Move the pursuer • Calibrate Position estimation and error bound • Using magnetometer to track pursuer • Eventually, we have multiple • Localize pursuer with beacons • Modulating the magnetic field on the pusrsuer • Or use the sound • Time of flight will work • On line calibration on localization • data out of sensor network

  22. Step 2 • Pursuer’s computation • Where to compute • Depends on the algorithm • MATLAB simulation with traces and run with the same code in real • Step 2: • Algorithms make assumption of lossy updates • Give errors of the current estimate

  23. Control Evader • Test the problem of both side the same time • Two matches • Same algorithm • Control the evader and the pursuer • Compare algorithms

  24. Magnetometer • No centering • Precision Navigation • PNI • Digital output • Set/reset • No drift • Measure absolute filed • Little resistor

  25. How to go from one to many?

  26. How to model your time delay? • Jitter • Correct sensor network data • Model the sensor network • *** implement the car • Need to run and characterize the sensor network

  27. Kit Upgrade • Multiple evader/multiple pursuer • But single hop to the robot • Drives the challenge of localization: • Pursuer tracked by audio • Magnetometer is very unreliable for distance estimate • Proximity may be fine • Unless you use an AC magnetic field • Detect • Needs a localization that supports multiple agents (3 MAX)

  28. Define Interface for other components to plug in

  29. Kit 3

  30. Distributed Mapping • Map of objects • Map of probabilistic of where the evader is • Accelerometer • Coarse estimation of where you are from magentometer • Accelerometer gives high frenquency data • Many robots map out the space through localization of each other

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