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Bearcat Cub V: Advancements in Design for Robotics Competition

Learn about the Bearcat Cub V presented in 2012, including sensors, performance, design enhancements, and conclusions. Follow our team from the University of Cincinnati through the journey of participating in the IGVC for 20 years. Discover the improvements made to the vehicle, such as lane detection and obstacle avoidance systems. Find out how we overcame challenges and prepared for competition with upgraded safety features and enhanced software algorithms.

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Bearcat Cub V: Advancements in Design for Robotics Competition

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  1. The Bearcat Cub V Presented June 9, 2012 By William Hilton and Nicholas Vuotto

  2. Outline • Our Team • Introduction to the Cub • Sensors • Last year’s performance • Design enhancements • Wireless E-stop • Wired E-stop • Additional enhancements • Lane Detection • Obstacle Avoidance • Conclusions

  3. Our Team • University of Cincinnati • Participated all 20 years of IGVC • Currently all undergraduates 010000100101101010010010 010000100101101010010010 010100110100 010100110100 010000100101101010010010 010100110100

  4. Introduction to the Bearcat Cub • Incremental evolution of last year’s vehicle • Two deep cycle marine batteries for power • Segway wheels and gearboxes for motion • Sensors mounted on mast for intelligence • Dell laptop to control it all

  5. Sensors • SICK Lidar • Bumblebee stereovision • Two Sony camcorders • Novatel GPS • HMR Compass • Wheel encoders

  6. Last Year’s Performance • We qualified! • 1st run: software crashed • 2nd run: E-stop demonstration caused software crash & full reboot • Fuses in inverter blew preventing further attempts Lesson learned: more robustness!

  7. Wireless E-stop Remote Kill Switch with 2.5x factor of safety! 250 feet

  8. Wired E-stop New, schematically simpler system. Easier to reset & doesn’t crash code.

  9. Additions Since Report Since the written report was submitted, the following changes have been made: • Foam safety bumper • Upgraded safety light from parallel port to USB • Added 100A circuit breaker • And central fuse box • Code stability improvements

  10. Lane Detection • HSV thresholding for color detection • Hough transform to find best line fit

  11. Obstacle Detection • Uses LIDAR and stereovision • Obstacles create virtual repelling forces • Target creates virtual attractive force • Heading determined by summing the forces

  12. Conclusions • The same software algorithms as last year: • but better tested! • Upgrades to the electrical system and the frame to be: • More reliable • Safer • Competition, here we come!

  13. Thanks to • Our mentor, Professor Humpert • University of Cincinnati Center for Robotics Research • IGVC Committee Questions?

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