Transcript Challenge Appln

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
– Short Term (1yr): RC Car RoboMotes
– Long Term (2-3yrs): Macro Robots and UAVs
• ASAP
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
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
Interface of different components
• Position Estimation
– X,Y for Pursuer and Evader with delay and
error bound
• Cars Control
– Series of speed, angle commands
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
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
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
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)
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
Game: dynamic programming
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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
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Do a local finding by pursuer
Sensor networks augment it
Color detection on the evader
Laser pointing
Design a policy
• Map one or more pursuer to the evader
• Narrow it to one evader
• Tracking controller that minimizes the
distance
Problem
• Loss, delay,
– Delay corresponds to speed
– Failure model
• Retain your history
• Loss is lack of update
Calibration
Leader Election
Reliable Transport
Error Model
• Using the sensor network to quantify
expected capture time
Separate network channel
• Pursuer and Evader
Pursuer can ask network
• Where did the evader go?
Control
• Sensing is distributed
• Stability of the system
• Introduce new constraints
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
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
Control Evader
• Test the problem of both side the same time
• Two matches
– Same algorithm
• Control the evader and the pursuer
• Compare algorithms
Magnetometer
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No centering
Precision Navigation
PNI
Digital output
Set/reset
No drift
Measure absolute filed
Little resistor
How to go from one to many?
How to model your time delay?
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Jitter
Correct sensor network data
Model the sensor network
*** implement the car
Need to run and characterize the sensor
network
Kit Upgrade
• Multiple evader/multiple pursuer
• But single hop to the robot
• Drives the challenge of localization:
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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)
Define Interface for other
components to plug in
Kit 3
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