Vicki Niu, MacLean Freed, Ethan Takla, Ida Chow and Jeffery Wang - - PowerPoint PPT Presentation

Vicki Niu, MacLean Freed, Ethan Takla, Ida Chow and Jeffery Wang Lincoln High School, Portland, OR Nanites4092 @ gmail.com

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

Learning STEM through Robotics

 Through FIRST, we have been working together since

2005, when we were just 4th 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.

2010: FLL World Festival in Atlanta

2012 FTC Oregon State Championship

LEGO Robots

A LEGO Program

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

“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

Competition Video

Why is robotics a good case study?

 Must adapt to a changing environment

 Collisions with other robots

Why is robotics a good case study?

 Must adapt to a changing environment

 Mobile field components

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

Hardware Restrictions

 Robot design process  Limitation of resources  Manipulation of materials  Testing

 (durability under stress)

Advantages of TETRIX

 Manipulation  Customization  Varied uses  Compatibility

CAD

 Analyzes

designs

 Saves prototype

costs

 Uses software

to test real-life situations

 Smoother

construction

Sensor unreliability

 Sensors have error, lack accuracy  Values can deviate from “true” reading

 Gyroscope integrated drift

5 10 15 20 25 30 35 40 10 20 30 40 Ultrasonic Sensor Reading (cm) Actual Distance Until Wall (cm) 100 200 300 400 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 Yaw (Degrees) Sample Number

Actual Yaw vs. Hitechnic Compass Sensor Yaw

Actual Yaw Yaw from Hitechnic Compass Sensor

Insufficient sensor information

 Missions are complex, require detailed information  Sensors can be reliable but provide data that isn’t

useful

Needed sensor information

 Orientation

 Compass sensor  Magnetometer

 Position

 Ultrasonic sensor

• 100

100 200 1 40 79 118 157 196 235 274 313 352 391 430 469 508 Degrees Sample Number

Hitechnic Compass Sensor Yaw vs. Robot Pitch

Pitch Hitechnic Compass Sensor Yaw

0.2 0.4 0.6

• 0.8
• 0.6
• 0.4
• 0.2

0.2 Milligauss (y axis) Milligauss (x axis)

Magnetometer Y Axis vs. X axis

5 10 15 20 25 30 35 40 10 20 30 40 Ultrasonic Sensor Reading (cm) Actual Distance Until Wall (cm)

Attitude and heading reference system

 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

Rotation Matrix – Kinematics & Normalization Drift Detection Accelerometer Magnetometer PI Controller Attitude Gyro XYZ +

Attitude and heading reference system

• 100
• 50

50 100 150 200 1 61 121 181 241 301 361 421 481 Degrees Sample Number AHRS Yaw AHRS Pitch Hitechnic Compass Sensor Yaw

Image tracking using cameras

 Need position data as well as orientation data  Use two cameras to attain visual data using a semi-

global block matching (SGBM) algorithm

Semi-global block matching

 Calibrate cameras to account for distortion or offset  Two cameras, left and right, take images

simultaneously

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.

Acknowledgements

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

References

FIRST LEGO League, “Food Factor Challenge,” [Online], Available: http://firstlegoleague.org/sites/default/files/Challenge/FoodFactor/FLL2011_Complete_Challenge.pdf FIRST Tech Challenge, “Bowled Over! Challenge,” [Online], Available: http://www.usfirst.org/sites/default/files/uploadedFiles/Robotics_Programs/FTC/Game_Info/2011/Complete-Build-Guide.pdf FIRST Tech Challenge, “Bowled Over! Game Manual,” [Online], Available: http://www.usfirst.org/sites/default/files/uploadedFiles/Robotics_Programs/FTC/Game_Info/2011/Bowled-Over-Game- Manual_Rev%285%29.pdf RobotC, “RobotC NXT Curriculum,” [Online], Available: http://www.robotc.net/education/curriculum/nxt/

• V. M. B. K. Pirabakaran, “PID Autotuning Using Neural Networks and Model Reference Adaptive Control,” Proceedings of the 15th

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• 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. 3676-3681, 2000.

• D. Brutzman, “From virtual world to reality: designing an autonomous underwater robot,” 1992.
• Y. Kuroda, K. Aramaki, and T. Ura, “AUV test using real/virtual synthetic world,” Proceedings of Symposium on Autonomous

Underwater Vehicle Technology, pp. 365-372.

• 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. 1--32, 2010. Oliver J. Woodman, “An introduction to inertial navigation,” University of Cambridge, p. 1--37, 2007.

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