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Roomba Team Cleaner. Timothy Wong Zainul Charbiwala. Flow. Introduction and Motivation Components Simulation Real World Algorithms Demo Conclusion and Future Work Thanks!. Introduction and Motivation. Introduction. What is Roomba TeamCleaner ? Co-ordinated Autonomous Cleaning
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Roomba Team Cleaner • Timothy Wong • Zainul Charbiwala
Flow • Introduction and Motivation • Components • Simulation • Real World • Algorithms • Demo • Conclusion and Future Work • Thanks!
Introduction • What is Roomba TeamCleaner ? • Co-ordinated Autonomous Cleaning • Co-ordination through short range radio • Knowing where all Roombas are • Autonomous algorithms • Processing location information to figure out where next to clean
Motivation • Efficiency • Co-ordinated cleaning would finish the job faster if more resources are available • Convenience • Not required to configure virtual walls to partition multiple Roombas to different areas • Extensibility to time-critical situations • Quickly exploring an emergency site
Simulation • RoombaClass • TeamCleaner Algorithm • RoombaSim
RoombaClass • Model of Roomba behavior • Moving in a straight line at a specific velocity • When bumping into obstacles, turn around a random amount • When gone too far in a straight line, turn around a random amount • Provides accurate movement model • Provides accurate location
RoombaSim and TCAlgo • RoombaSim • Provides a framework to test various co-ordination algorithms with a notion of simulated time • Uses RoombClass to model the Roomba behavior • TeamCleaner Algorithm • Generic placeholder for any co-ordination algorithm with appropriate exports and hooks
Real World • Localization • Odometry • Cricket • Image Processing • Roomba Control • ‘Remote’ Control • Mote Control
Localization • Odometry • Cricket • Image Processing
Odometry • Roomba provides odometry data from its wheel encoders • Sensors give out distance and angle since last read • Full localization possible using only odometry if the starting point is known
Odometry (contd...) • Issues with odometry from Roomba • Angle information goes completely off when the Roomba bumps into an obstacle • Distance readings are not accurate, are within 10-15% of actual value - probably due to wheel slipping • Using virtual walls to avoid bumping does not help
Cricket • Using difference in propagation time of ultrasonic waves and radio waves to measure distance • Uses Robust Quadrilateral Approach of Cricket • Convenient and intuitive usage semantics
Cricket (contd...) • Issues with using Cricket • Coverage • Data Inconsistency • Latency • Echo from obstacles • Mounting dependency • Startup order dependency • May require data from multiple Cricket to be combined to get a better estimate
Image Processing • Find difference images • Filter out uninteresting differences • Compute location based on coordinates of colored patch and mapping from image co-ordinates to grid co-ordinates • Compute direction based on the previous position • Uses the Java Advanced Imaging API
Image Processing (contd…) • Issues with Image Processing: • Heavy initial tuning required • Lighting conditions should not change • Require to do additional continuity tests if color coding is not to be used • Extending technique to use multiple cameras with overlapping coverage is non-trivial
‘Remote’ Control • Controlling the Roomba from a Host PC • RoombaCommSOS • Extending the RoombaCommSerial ‘protocol stack’ from Tod Kurt to use MicaZ motes with SOS • Issues with Remote Control • Packet loss • High Latency • Loss of autonomy (seemingly...)
Mote Control • Controlling the Roomba from the MicaZ itself • Required changes to SOS UART driver • Porting of Java based RoombaComm driver to SOS • No UART buffering
Algorithms • (Somewhat) Random Traversal • Dirty Neighbor • Uncleaned Neighbor • Dirtiest Neighbor • Longest Line of Dirty Neighbours
Performance Random Dirty nbor Uncleaned nbor Dirtiest nbor Most dirty nbors Coverage Vs Time
Demo • RoombaDisplay • Simulation • Real World
Conclusion • Roomba is not a generic robotics platform • SOS’ event driven model is not too convenient for programming • Atmel AVR is difficult to debug ‘in system’ on a mobile platform • Avrora is a great tool but has its limitations
Conclusion • Localization is a hard problem to solve • Cricket does not solve the problem • Latency and loss issues using MicaZ radio • Difficult to capture real world behavior accurately in a simulation model !
Future Work • Localization: Using technique similar to an optical mouse • Localization: Using an Inertial Measurement Unit • Localization: Using RSSI to indicate closeness • Algorithms: Virtual Partitioning • Simulation: Adding an obstacle mask
Thanks! • NESL • Thomas, Ram, Simon, Roy, Anna, Jonathan