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STANLEY




Billy Okal
Machine Learning Seminar 2012
Outline

• Task overview
• Robot and Team description
• Robot performance/results
• Challenges/critique
Task (in brief)

• Drive across a desert autonomously for ~140 miles
• Course (RDDF) given only 2 hours before race
• US $2 Million in prize money (for 2nd run in 2005)
• DARPA already mentioned that a 4WD pickup is
  physically capable of finishing the race
Stanley Team
 • Stanford University (headed by
        Thrun)
 • Volkswagen ERL
 • Mohr Davidow Research
 • Intel Research
 • Others

Images: Sebastian Thrun’s presentation
Basic Vehicle Specs
• Volkswagen Touareg R5, 4WD
• Variable height air suspension, auto and electronics
  locking differentials
• Added skid plates, front bumper
• Custom interface to throttle and brakes
• DC Motor Steering, Gears (drive, reverse, packing)
• CAN bus for communication
• Custom roof-rack for carrying sensors
Stanley (Nov, 04)




Images: Sebastian Thrun’s presentation
Sensors
• Five SICK Laser range finders different angles)
• Two 24 Hz Radar sensors (cover up to 200m)
• Color Camera for long range perception
• GPS positioning unit and compass
• IMU (Inertial measurement unit)
Computational Resources
• Six Pentium M computers (only 4 used in
  actual race)
• Gigabit Ethernet switch
• Custom power with backup batteries
• Special air ducts from AC for cooling
• Total power requirement of 500 W
• Linux OS
Software Architecture




Images: Sebastian Thrun’s presentation
Terrain classification            Planning




                                                Vision
                              Learning




Images: Sebastian Thrun’s presentation
Key concepts
• Modular architecture
• Probabilistic Error Models
• Data driven parameter learning ( e.g for
  terrain classification)
• UKF Position estimation
• ...
Beyond the Race
• Stanley now rests at a Museum
• Junior competed in 2007 Urban challenge
  (didn’t win :( )
• Sebastian now leads Google’s driverless cars
  campaign
• Modern “versions” of Stanley now are smaller,
  less expensive.
Challenges
• Stanley cannot pass a moving big object
• Stanley had ‘no global planner’. Solved in
  successors
• Only off-road use
• Only static race environments
• Critical time issues (500 msec delay caused
  problems)
References
• S. Thrun et. al. ; ‘Stanley: The Robot that won
  the DARPA Grand Challenge’, Journal of
  Field Robotics 23(9), pp 661-692
• S. Thrun, M. Montemerlo and A. Aron;
  ‘Probabilistic Terrain Analysis for high speed
  desert driving’, Robotics: Science and
  Systems (RSS) 2006
• Stanley web page: http://
  robots.stanford.edu/talks/stanley/

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Stanley : The robot that won grand challenge

  • 2. Outline • Task overview • Robot and Team description • Robot performance/results • Challenges/critique
  • 3. Task (in brief) • Drive across a desert autonomously for ~140 miles • Course (RDDF) given only 2 hours before race • US $2 Million in prize money (for 2nd run in 2005) • DARPA already mentioned that a 4WD pickup is physically capable of finishing the race
  • 4. Stanley Team • Stanford University (headed by Thrun) • Volkswagen ERL • Mohr Davidow Research • Intel Research • Others Images: Sebastian Thrun’s presentation
  • 5. Basic Vehicle Specs • Volkswagen Touareg R5, 4WD • Variable height air suspension, auto and electronics locking differentials • Added skid plates, front bumper • Custom interface to throttle and brakes • DC Motor Steering, Gears (drive, reverse, packing) • CAN bus for communication • Custom roof-rack for carrying sensors
  • 6. Stanley (Nov, 04) Images: Sebastian Thrun’s presentation
  • 7. Sensors • Five SICK Laser range finders different angles) • Two 24 Hz Radar sensors (cover up to 200m) • Color Camera for long range perception • GPS positioning unit and compass • IMU (Inertial measurement unit)
  • 8. Computational Resources • Six Pentium M computers (only 4 used in actual race) • Gigabit Ethernet switch • Custom power with backup batteries • Special air ducts from AC for cooling • Total power requirement of 500 W • Linux OS
  • 10. Terrain classification Planning Vision Learning Images: Sebastian Thrun’s presentation
  • 11. Key concepts • Modular architecture • Probabilistic Error Models • Data driven parameter learning ( e.g for terrain classification) • UKF Position estimation • ...
  • 12. Beyond the Race • Stanley now rests at a Museum • Junior competed in 2007 Urban challenge (didn’t win :( ) • Sebastian now leads Google’s driverless cars campaign • Modern “versions” of Stanley now are smaller, less expensive.
  • 13. Challenges • Stanley cannot pass a moving big object • Stanley had ‘no global planner’. Solved in successors • Only off-road use • Only static race environments • Critical time issues (500 msec delay caused problems)
  • 14. References • S. Thrun et. al. ; ‘Stanley: The Robot that won the DARPA Grand Challenge’, Journal of Field Robotics 23(9), pp 661-692 • S. Thrun, M. Montemerlo and A. Aron; ‘Probabilistic Terrain Analysis for high speed desert driving’, Robotics: Science and Systems (RSS) 2006 • Stanley web page: http:// robots.stanford.edu/talks/stanley/

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