image tracing laser system jason duarte azmat latif stephen sundell tim weidner

Image Tracing Laser System

Jason DuarteAzmat LatifStephen SundellTim Weidner

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

Problem Statement:

Position Laser Pointer to trace various figures

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Introduction

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

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

Model Development

Lagrange-Euler Model

Simplified Model

Position (rad)Velocity (rad/s)Acc (rad/s 2)

Torque

1

panposthetadot satthetaddot sat

fv1

Viscous

Sign

u/Jeff

MInverse

1s

Integrator1

1s

Integrator

-K-

Gain2

fc1

Coulomb

1

voltagepan

Friction Identification

Steady state velocities measured and plotted vs. torque Forward/reverse frictions averaged to one value

Positive Negative

Viscous (N*m*s/rad)

0.0003 0.0004

Coulomb (N*m)

0.0638 -0.0410

Tilt Axis Friction Identification

y = 0.0019x + 0.0584

y = 0.0017x - 0.0569

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

-15 -10 -5 0 5 10 15

Steady State Load Angular Velocity (rad/sec)

Ap

plie

d T

orq

ue

(N

-m)

Pan Axis Friction Parameters

Positive Negative

Viscous (N*m*s/rad)

0.0007 0.0006

Coulomb (N*m)

0.0584 -0.0569

Tilt Axis Friction Parameters

Model Validation

Simulation Results Decouple links Set voltages at .05V

increments Observe velocities Compare to

experimental results

Model Validation Cont…

Experimental results Identical to simulation Tilt axis has more

readings due to viscous friction

PID Control

(always running)

Follow Trajectory

Repeat RT Host

Initialize CameraOrient Camera

Set Zero LocationCalibrate Camera

Snap Picture

Inverse Kinematics

Trajectory Generation

Shut Down Camera

Release References

PC Host

Program Block Diagram

Laser Control

Laser (Beam of Light Technologies)4.5V, 50mA, 650nm

Digital Port (NI 9401)5V, 2mA output

Relay (R40-11D2-5)5V activation2A max current throughput

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)

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

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

Forward Kinematics

xR =

22

22

cossin0

sincos0

001

yR =

cos0sin

010

sin0cos

1

11

1

0

0

n

xR yR n t =

21

2

21

sincos

sin

cossin

t=

dy

x

Inverse Kinematics

)(2tan1 d

xa

)

cos(2tan 1

2 d

ya

D ista n ce = 3 ft

n

t

Trajectory Generation

Why do we need it?

Specify path and speed.

How did we get it?

NI-Motion Assistant

Position Points Obtained Position Profile

Position Points Obtained Position Profile

Controller

PID Controller Continually running on RT Host

No external manipulationSystem stable at all times

Global Variables used for input/outputChange settings without stopping controllerSeparate vi to change Globals

Can be started/stopped without affecting controller

Video

Assessment

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

Possible Future Enhancements

Vision feedback Velocity feedback Smoother tracing at all speeds Tracing more complex images

Questions?