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 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

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 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? 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: –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