PEG Breakout

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PEG Breakout

• Mike, Sarah, Thomas, Rob S., Joe, Paul, Luca,
  Bruno, Alec

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Whats 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

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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

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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

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Interface of different components

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

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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

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Action 2 Close the loop

• Computation of pursuers 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 pursuers algorithm but
  completely opposite
• Have algorithms compete on both side at the same
  time and compare

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Pursuer / Evader Development Kit

• Sensor Network Provides PE Location Estimates at
  gt 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

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Ideas to Pursue

• Speed Up Position Estimates to 5-10Hz OR
  Reengineer Cars to go Slow
• Car control with magnetometer giving cars
  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 (33 MAX)

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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

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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 lt 1
• Do a local finding by pursuer
• Sensor networks augment it
• Color detection on the evader
• Laser pointing
• Helicopter has a camera

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Design a policy

• Map one or more pursuer to the evader
• Narrow it to one evader
• Tracking controller that minimizes the distance

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Problem

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

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Calibration
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Leader Election
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Reliable Transport
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Error Model

• Using the sensor network to quantify expected
  capture time

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Separate network channel

• Pursuer and Evader

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Pursuer can ask network

• Where did the evader go?

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Control

• Sensing is distributed
• Stability of the system
• Introduce new constraints

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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

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Step 2

• Pursuers 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

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Control Evader

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

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Magnetometer

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

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How to go from one to many?
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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

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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)

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Define Interface for other components to plug in
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Kit 3
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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