Vicki Niu, MacLean Freed, Ethan Takla, Ida Chow and Jeffery Wang Lincoln High School, Portland, OR gmail.com

Transcription

1 Vicki Niu, MacLean Freed, Ethan Takla, Ida Chow and Jeffery Wang Lincoln High School, Portland, OR gmail.com

2 Outline Learning STEM through robotics Our journey from FIRST LEGO League to FIRST Tech Challenge Robotics as a case study for engineering quality What we learned

3 Learning STEM through Robotics Through FIRST, we have been working together since 2005, when we were just 4 th graders We ve learned about science, technology, engineering and mathematics (STEM) through robotics competitions. We learned to work together as a team. Here, we are going to share the story of how we approach engineering quality in the robotic world.

4 2010: FLL World Festival in Atlanta

5 2012 FTC Oregon State Championship

6 LEGO Robots

7 A LEGO Program

8 What we learned from FLL Navigation based on time vs. sensor based navigation Efficiency of actions due to time limit Creative use of basic parts Using mathematics within programming

9 FIRST TECH Challenge Experience High school program Started freshman year Chance to expand on what was learned in the previous program Oregon has ~100 teams

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11 Competition Video

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14 Why is robotics a good case study? Must adapt to a changing environment Collisions with other robots

15 Why is robotics a good case study? Must adapt to a changing environment Mobile field components

16 Why is robotics a good case study? Must adapt to a changing environment Varying field surfaces, lighting, power Robot hardware and software are easily adaptable Restraints placed on both hardware and software capabilities

17 Hardware Restrictions Robot design process Limitation of resources Manipulation of materials Testing (durability under stress)

18 Advantages of TETRIX Manipulation Customization Varied uses Compatibility

19 CAD Analyzes designs Saves prototype costs Uses software to test real-life situations Smoother construction

20 Ultrasonic Yaw (Degrees) Sensor Reading (cm) Sensor unreliability Sensors have error, lack accuracy Values 40 can deviate from true reading Gyroscope integrated drift Actual Yaw vs. Hitechnic Compass Sensor Yaw Actual Yaw Yaw from Hitechnic Compass Sensor Sample Actual Number Distance Until Wall (cm)

21 Insufficient sensor information Missions are complex, require detailed information Sensors can be reliable but provide data that isn t useful

22 Degrees Milligauss (y axis) Ultrasonic Sensor Reading (cm) Needed sensor information Orientation Compass sensor Magnetometer Position 200 Ultrasonic sensor Hitechnic Compass Sensor Yaw vs. Robot Pitch Magnetometer Y Axis vs. X axis Sample Number Milligauss (x axis) Pitch Hitechnic Compass Sensor Yaw Actual Distance Until Wall (cm)

23 Attitude and heading reference system Gyro XYZ + - Rotation Matrix Kinematics & Normalization Attitude PI Controller Drift Detection Magnetometer Accelerometer Compensate for gyro drift with direction cosine matrix (DCM) using fused magnetometer and accelerometer data Consolidated into AHRS Fuses accelerometer, magnetometer, and gyro data Return roll, pitch, yaw

24 Degrees Attitude and heading reference system Sample Number AHRS Yaw AHRS Pitch Hitechnic Compass Sensor Yaw

25 Image tracking using cameras Need position data as well as orientation data Use two cameras to attain visual data using a semiglobal block matching (SGBM) algorithm

26 Semi-global block matching Calibrate cameras to account for distortion or offset Two cameras, left and right, take images simultaneously

27 Summary Through the case study of robotics competition, we examined what we learned in engineering quality. Teamwork is critical to achieving engineering quality. Having fun helps too! Mechanical design. Overcoming sensor limitations Exploring navigation techniques, e.g. AHRS, 3D camera tracking.

28 Acknowledgements Richard Vireday Aaron Akzin Lincoln High School ORTOP Community FIRST Fourmost Products

29 References FIRST LEGO League, Food Factor Challenge, [Online], Available: FIRST Tech Challenge, Bowled Over! Challenge, [Online], Available: FIRST Tech Challenge, Bowled Over! Game Manual, [Online], Available: Manual_Rev%285%29.pdf RobotC, RobotC NXT Curriculum, [Online], Available: V. M. B. K. Pirabakaran, PID Autotuning Using Neural Networks and Model Reference Adaptive Control, Proceedings of the 15th IFAC World Congress, 2002, [Online]. Available: [Accessed: 15-Jun-2012]. W.-yong Han, J.-wook Han, and C.-goo Lee, Development of a Self-tuning PID Controller based on Neural Network for Nonlinear Systems, Control, pp , F. Lin, R. D. Brandt, and G. Saikalis, Self-tuning of PID controllers by adaptive interaction, Proceedings Of The American Control Conference, vol. 5, no. June. American Autom. Control Council, pp , D. Brutzman, From virtual world to reality: designing an autonomous underwater robot, Y. Kuroda, K. Aramaki, and T. Ura, AUV test using real/virtual synthetic world, Proceedings of Symposium on Autonomous Underwater Vehicle Technology, pp S. O. H. Madgwick, An efficient orientation filter for inertial and inertial / magnetic sensor arrays, Report xio and University of Bristol UK, vol. 2011, p , Oliver J. Woodman, An introduction to inertial navigation, University of Cambridge, p , 2007.

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