1. Image Tracing Laser System Jason Duarte Azmat Latif Stephen Sundell Tim Weidner

2. Overview Introduction Objectives and Specifications Design Approach  Model Development  Friction Identification  Model Validation  Design Flow  Image Analysis  Inverse Kinematics  Trajectory Generation  Controller Video Assessment Possible Future Enhancements

3. Problem Statement: Position Laser Pointer to trace various figures Similar Designs: Spray Painting Laser Cutting Team 5 2004: Signature Writing System Introduction

4. Objectives Visually identify an image using a webcam Extract shapes using LabView Vision Module Generate a trajectory to trace shape at desired speed Design a controller to follow the trajectory

5. Updated System and Specs Camera and laser mount orientation changed to keep laser on the tilt axis Image size changed to 2’x1.5’ because of camera viewing angle restrictions Tracing speed of 12 in/sec Pan-Tilt SystemTilt Body

6. Model Development Lagrange-Euler Model Simplified Model

7. Friction Identification Steady state velocities measured and plotted vs. torque Forward/reverse frictions averaged to one value PositiveNegative Viscous (N*m*s/rad) 0.00030.0004 Coulomb (N*m) 0.0638-0.0410 Pan Axis Friction Parameters PositiveNegative Viscous (N*m*s/rad) 0.00070.0006 Coulomb (N*m) 0.0584-0.0569 Tilt Axis Friction Parameters

8. Model Validation Simulation Results  Decouple links  Set voltages at.05V increments  Observe velocities  Compare to experimental results

9. Model Validation Cont… Experimental results  Identical to simulation  Tilt axis has more readings due to viscous friction

10. PID Control (always running) Follow Trajectory Repeat RT Host Initialize Camera Orient Camera Set Zero Location Calibrate CameraSnap Picture Inverse Kinematics Trajectory Generation Shut Down Camera Release References PC Host Program Block Diagram

11. Laser Control Laser (Beam of Light Technologies)  4.5V, 50mA, 650nm Digital Port (NI 9401)  5V, 2mA output Relay (R40-11D2-5)  5V activation  2A max current throughput

12. Image Processing (Calibration) Get grid with WebCam  Use grab to continually take images  Find center of image then stop  Image consists of dots evenly spaced Calibrate image for pix/in.  Use calibration vi (2in. between dots)  Image now stores pixel to world transform Send calibrated image as reference  Image calibration is used with other images (tracing)

13. Image Processing (Tracing) Get an image from WebCam  Use grab function for continual viewing  Need a clear image with no breaks or random points Use threshold to filter unwanted data  Inverts colors in image  Useful data in white Search image for data points  Find starting point  Traverse around image  Find white path with black next to it Store data points in array  Send data to real-world transform  Output data to array for Inverse Kinematics

14. Inverse Kinematics What is Inverse Kinematics? Map from world space to joint space. Why do we need it? We work in joint space. Most tasks are specified in world space. How do we get it? Forward Kinematics: Map from joint space to world space

16. Trajectory Generation Why do we need it? Specify path and speed. How did we get it? NI-Motion Assistant

17. Position Points ObtainedPosition Profile

18. Position Points ObtainedPosition Profile

19. Controller PID Controller Continually running on RT Host  No external manipulation  System stable at all times Global Variables used for input/output  Change settings without stopping controller  Separate vi to change Globals Can be started/stopped without affecting controller

20. Video

21. Assessment Physical system met requirements PID controller proved to be sufficient Reliable image processing algorithm Successful trajectory generation software

22. Possible Future Enhancements Vision feedback Velocity feedback Smoother tracing at all speeds Tracing more complex images

23. Questions?