PICARSO_Seminar - University of Adelaide
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Transcript PICARSO_Seminar - University of Adelaide
Hardware
System
Process
Painting
System
Project
Outcomes
Design
Problems
Testing and
Results
Image
Processing
Future Work
Design
Specifications
Control
Software
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PICARSO:
Cable-driven robot
Process standard image formats
Reproduce images on vertical surface
Image
Processing
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Control
Software
Hardware
Sven Paschburg
Painting
System
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Image Output
Original
Processed
Painted
End-Effector
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MATLAB
Motor 3
Image Processing Toolbox
Motor 2
RS232
Cables
Cables
Vertical
Wall
End-Effector
(Mathworks 2010)
Graphical User Interface Design Environment
Motor 1 (GUIDE)
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Hardware
Design
Problem
Painting
System
Project
Outcomes
Design
Specifications
Testing and
Results
Image
Processing
Future Work
System
Architecture
Control
Software
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Samuel Oosterholt
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Goals:
Develop mechanical system
Scalable work space size
Up to 3×3m
Manipulate and stabilise the end-effector
Mounting the system:
in operation and
in testing
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Samuel Oosterholt
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Hektor (Franke & Lehni 2002)
Viktor (Lehni & Rich 2008)
Four actuators
Two
actuators
Image producing robots
‘X’
‘V’ Configuration
Scalable workspace
sizeon gravity
Relies
Cable driven
Hektor’s actuator
configuration
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PICARSO’s actuator
configuration
Samuel Oosterholt
Viktor’s actuator
configuration
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Full System
Three motors
‘Y’ configuration
Cables
Upper motors
control position
Lower stabilises
Reduces cost
Render of PICARSO system
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Samuel Oosterholt
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Motor Mount
Base Plate
Motor
Motor Controller
Spooling System
Cable feeding
system
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Samuel Oosterholt
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Motor Mount
Motors parallel to
painting surface
Plate mounts to
painting surface
Double spool and
bearing
Two cables
Reduce yaw
and pitch
Minimise
kickback
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Samuel Oosterholt
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Motor Mount
180°
Reorientated by
Cables:
eyebolts
and (Braid
Spiderwire
fishing line)
pulleys
Ø = 0.30mm
Tmax
= 13.6kg
Can
move
pulleys
Small
elasticity
and
eyebolts
Proximity to canvas
Stability
10m
of cable
7×7m workspace
Lower mount uses
fairleadsattach to
Cables
180° sweep
turnbuckles
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Samuel Oosterholt
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Hardware selected from
Maxon Motors
Numerous operation
modes
Modular components
Discounted cost & support
250W EC45 Motors
and Maxon EPOS2
70/10 Motor Controller
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Samuel Oosterholt
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Not feasible
Canvas
implemented
to wall mount for testing
Simulate
3.6
× 3.2msurface
easel (working area: 2.8 × 2.8m)
Reduces ripple
in wind environment
Constrained
by testing
PICARSO’s easel in the
Vibrations Laboratory
PICARSO’s easel in the
FSAE shed
PICARSO’s canvas
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Samuel Oosterholt
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Hardware
Design
Problem
Painting
System
Project
Outcomes
Design
Specifications
Testing and
Results
Image
Processing
Future Work
System
Architecture
Control
Software
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Neil Yeoh
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Goals:
Design an appropriate painting system
which:
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Produces circular patterns (< 3cm)
Is fast (> 1Hz) and light (< 3kg)
Does not cause instabilities
Houses suitable paint capacity
Is reliable, durable, and repeatable
Neil Yeoh
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Three types of painting mechanisms
*1
*2
Airbrush
Spray Can
*3
Spray Gun
Circular Patterns
( <3cm)
Fast ( >1Hz)
Light ( <3kg)
No Instabilities
Paint Capacity
Reliable
Durable
Repeatable
** Image references at end of slides
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Neil Yeoh
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Electrical Air
Compressor
Automatic
Pressure-fed
Spray Gun
Paint Line
Air Regulator
Compressed
Air Line
*7
Personal
Computer
*5
*4
Air Line
Paint Regulator
Solenoid
Pressurised
Paint Canister
*8
Electrical Signal
*9
*6
** Image references at end of slides
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Neil Yeoh
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Anest Iwata
SGA-101 Automatic Pressure-fed Spray Gun
Pattern Adjust Knob
Fluid Adjust Knob
Air Line
Fitting Knob
Nozzle
Paint Line
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Neil Yeoh
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Testing procedure
Ideal Settings identified:
Specification
Setting
Air Pressure
2.0 bar
Paint Pressure
0.5 – 1.0 bar
Spray Distance
5 – 10 cm
Fluid Adjust Knob
Half Turn
Spray Duration
0.2 – 1.0 s
Repeatability
Spray Duration
Results:
Consistent < 3cm black circles
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Neil Yeoh
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Neil Yeoh
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Large Sleeve
Sleeve Shaft
Bearing Spacer
Small Sleeve
Eyebolts
Bearings
Spray Gun
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Neil Yeoh
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Neil Yeoh
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Hardware
Design
Problem
Painting
System
Project
Outcomes
Design
Specifications
System
Architecture
Testing and
Results
Image
Processing
Future Work
Control
Software
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Ian Hooi
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Goals:
Develop image processing software to:
Transform input image to user specified
settings
Reproduce input image in binary form
Output in Raster (pixel-by-pixel) form
Extension Goal:
Output in Vector (line) form
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Ian Hooi
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1.
2.
3.
4.
Input image
Resized to appropriate resolution
Stretching and refitting
Greyscale form
Scaled from 0-1 where 0 is black, 1 is white
Original
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Resized
Ian Hooi
Greyscale
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Fill Images: Direct conversion from
greyscale binary
Edge Images: outlines of the image
Edge Image
Fill Image
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Converted to binary form
Threshold filter
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Original
Binary: Threshold = 0.25
Binary: Threshold = 0.5
Binary: Threshold = 0.75
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Raster: pixel by pixel approach
Vector: line based approach
Raster Based Output
Vector Based Output
Optimal
70
70 70
65 65
60
60 60
55 55
50
50 50
45 45
40
40 40
35 35
30
30 30
25 25
20
20 20
10
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30
40
50
60
Ian Hooi
10 10
20 20
30 30
40 40
50 50
60 60
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Image output in binary form
1 represents white
0 represents black
Read and processed by Control Software
Slow to paint
70
60
50
40
30
20
1
1
1
0
1
0
1
0
1
1
1
0
1
1
1
1
0
1
1
1
0
0
0
1
0
0
0
1
1
10
0
20
30
Optimal
40
=
50
60
70
65
60
55
50
45
40
35
30
25
20
10
20
30
40
50
60
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Ian Hooi
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Aim: Raster Vector
Searching Algorithm based on Portrayer
(Benedettelli 2008) and Erik’s XY Plotter (2007)
Adjacent pixels chains Control software
Optimal
70
60
50
40
=
30
20
10
20
10
20
30
40
50
60
70
65
60
55
50
45
40
35
30
25
20
30
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50
60
Ian Hooi
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Hardware
Design
Problem
Painting
System
Project
Outcomes
Design
Specifications
Testing and
Results
Image
Processing
Future Work
System
Architecture
Control
Software
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Joyce Phan
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Goals:
Software for Raster Mode
Convert Image Processing output to:
• Control motors
• Control spray gun
Extension Goals:
Software for Vector Mode
Graphical User Interface (GUI)
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Joyce Phan
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Image
Processing
Output
1 0
0 1
Control
Software
Positioning
Commands
x
y
Cartesian
Co-ordinates
Inverse
Kinematics
L1 , L 2 , L 3
Cable
Lengths
turns
Output
Commands
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Joyce Phan
on
off
Motor Turn
Units
Motor
Commands
Spray Gun
Commands
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0
1
1
1
0
1
0
1
0
1
1
1
0
1
1
1
1
0
1
1
1
0
0
0
1
0
0
0
1
1
Image Processing
Output
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Raster Mode
Joyce Phan
Vector Mode
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Joyce Phan
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Motor Controller 3 Slave
Motor Controller 2 Slave
Solenoid
RS232
PC Master
RS232
RS232
Digital Output
• Instructions
from Master to
Slaves via 3
Parallel RS232
links
• Outputs
controlled in
Maxon RS232
Communication
Protocol
Motor Controller 1 Slave
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Position Mode
Motor 3
Position Mode
Motor 2
• Position Mode
• Driven in steps
• Current Mode
• Provides tension
• Minimises instabilities
Motor 1
Current Mode
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Joyce Phan
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• Easy access to user settings during operation
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Joyce Phan
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Hardware
Design
Problem
Painting
System
Testing
and
Results
Design
Specifications
Image
Processing
Project
Outcomes
Future Work
System
Architecture
Control
Software
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Goals:
Scaled system – µAngelo
Full scale system – PICARSO
Image processing software
Control software
Graphical User Interface (GUI)
Extension Goals:
Vector-based painting
Touch screen interface
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Scaled System - µAngelo
Kinematics test bed
Tri-motor Y-configuration proof of concept
Front view picture of
µAngelo
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Oblique angle picture
of µAngelo
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Picture of µAngelo’s
end-effector
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Full Scale System - PICARSO
Raster painting functionality
Scalable across a vertical surface
• Up to 3×3m workspace area
Complete a picture in 1 hour
Test Metrics
Accuracy & Precision
Pixel Size
Reliability
Stability
Workspace Resolution
Speed
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Raster Painting Functionality
10 mm
100 mm
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50 mm
Specifications
Achievement
Accuracy
± 10 mm
Precision
± 5 mm
Workspace
Resolution
10 – 100 mm
Workspace
Resolution (ideal)
10 – 25 mm
25 mm
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Raster Painting Functionality
Parameter
Achievement
Pixel Size (min.)
8 mm
Pixel Size (ideal)
10 mm
0.2 s
0.25 s
0.5 s
1.0 s
8 mm
10 mm
16 mm
20 mm
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Raster Painting Functionality
Specifications
Achievement
Stability
± 25 mm (z – axis)
z
pitch
roll
yaw
Bottom view of the end-effector
Side view of the end-effector
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Scalable across a vertical surface
Specifications
Achievement
Workspace Area 0.5 × 0.5 m to
7.0 × 7.0 m
A picture showing the ability of the
end-effector to move around the
workspace
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Goals:
Scaled system – µAngelo
Full scale system – PICARSO
Image processing software
Control software
Graphical User Interface (GUI)
Oblique angle picture
of painted
µAngelo‘fills’ image
A 1.8 × 1.8m
of the Mona Lisa
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Extension Goals:
Analog communication
Complete a picture in 1 hour
Vector-based painting
Touch screen interface
Future years:
Colour painting
Wireless communication
Commercial product
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Benedettelli, D. 2008, NXT Portrayer
Robot, viewed 11th April 2010
<http://robotics.benedettelli.com/portrayer.h
tm>
Convict Episcopal de Luxembourg, 2007,
Erik’s XY-Plotter, viewed 11th April 2010,
<http://www.convict.lu/Jeunes/ultimate_stuf
f/Erik_s_xx_plotter/E_xy_plotter.htm>.
Franke, U & Lehni, J 2002, Hektor, viewed
29 December 2009,
<http://www.hektor.ch/Book/Hektor.pdf/>.
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1.
2.
3.
4.
5.
6.
7.
8.
9.
Spray can - http://comps.fotosearch.com/comp/UNN/UNN113/spraycan_~u14072509.jpg
Airbrush http://imgs.tootoo.com/e1/00/e10006f419523aa3bb036a77fb5038dd.jpg
Spray gun - http://image.made-in-china.com/2f0j00DaTQeBfgIEMJ/HVLPSpray-Gun-RP8021-2S-H827-S-.jpg
Air Compressor - http://toolmonger.com/wpcontent/uploads/2006/05/aircompressor.jpg
Festo Air Regulator - http://www.festo.com/rep/nlbe_be/assets/LR_2008_1314u.jpg
Pressurised Paint Canister http://www.smitsgroup.co.nz/images/objectimages/PT2-Paint-Tank.jpg
Festo Solenoid Valve http://www.luconda.com/artikeldetails/27/29/xx/bilder/2093457-1-Festo-CPE18M1H-3GL-1-4.jpg
Iwata Spray Gun - http://www.anestiwata.co.jp/english/products/paint/prd/fog/auto/images/ceramic_ph01.jpg
Personal Computer - http://www.ubergizmo.com/photos/2008/12/dell-xps-13.jpg
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PICARSO
Mechanical &
Electrical
Hardware
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Software
Painting
System
Image
Processing
Samuel Oosterholt
Control
Software
54
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Joyce Phan
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Joyce Phan
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Joyce Phan
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Ian Hooi
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PC Master
EPOS2 Slave (Node 1)
EPOS2 Slave (Node 2)
NI PCI-6221 card NI SB 68-LP
(Analogue Inputs) Breakout Board
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Joyce Phan
EPOS2 Slave (Node 3)
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Joyce Phan
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Joyce Phan
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Joyce Phan
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Image Processing Software
Raster processing functionality
Process an image in less than 60s
Vector processing functionality
Processing Settings
Options
Image Input
JPEG, PNG, GIF, BMP, TIFF
Image Resolution
10 – 500 pixels
Image Threshold
0–1
Conversion Type
Fills, Edges
Palette Type
Binary (Black & White)
Sizing
Square, Original Aspect Ratio
Image Processing Time
<3s
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High Level Control Software
Raster functionality
Human Machine Interface (HMI)
Vector functionality
Touch screen interface
Specifications
Options
Control Mode
Raster Painting,
Vector Painting,
Manual Positioning,
Spray Control
Platform
Mathworks MATLAB 2007 +
Operating System
Windows XP, Vista, 7
Interface Type
Graphical (Touch Screen)
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Low Level Control Software
Position the motors
Control the motor velocity and acceleration
Read back the current position
Actuate the spray gun solenoid
Specifications
Options
Control Functions
Position Control,
Set Max. Velocity & Acceleration,
Read Current Position,
Activate Digital Output
Platform
Mathworks MATLAB 2007 +
Operating System
Windows XP, Vista, 7
Positioning Accuracy (theoretical) 2.11 × 10-3 mm
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Complete a picture in 1 hour
A 1.8 × 1.8m painted ‘fills’ image
of the Mona Lisa
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Parameter
Specification
Speed
2 – 250 mms-1
Acceleration
2 – 150 mms-2
Picture Time (av.)
2.5 hrs (edge image)
A 1 × 1m painted ‘edge’ image
of the University of Adelaide logo
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