1028:picarsosven paschburg2 system process design problems design specifications hardware painting...

1028:PICARSO

Ian HOOI

Samuel OOSTERHOLT

Sven PASCHBURG

Joyce PHAN

Neil YEOH

Supervisor: A/Prof Ben CAZZOLATO

PROGRAMMABLE INTERFACE CONTROLLER WITH

AUTONOMOUS ROBOTIC SPRAYING OPERATION

Sven Paschburg 21028:PICARSO

Seminar Outline

System Process

Design Problems

Design Specifications

Hardware

Painting System

Image Processing

Control Software

Testing and Results

Project Outcomes

Future Work


System Process

Hardware Painting System

Image Processing

Control Software

PICARSO:Cable-driven robotProcess standard image formatsReproduce images on vertical surface

Sven Paschburg 4

Processed

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Design Problems

Image Output

End-Effector

Original Painted



Cables

End-Effector

Motor 1

Motor 2Motor 3

Vertical Wall

CablesImage Processing Toolbox RS232

Graphical User Interface Design Environment(GUIDE)

(Mathworks 2010)

MATLAB

61028:PICARSO Samuel Oosterholt

Hardware

Design Problem


System Architecture

Hardware

Painting System

Image Processing

Control Software

Testing and Results

Project Outcomes

Future Work

Samuel Oosterholt 7

Hardware Goals

Goals: Develop mechanical system

Scalable work space sizeUp to 3×3m

Manipulate and stabilise the end-effectorMounting the system:

in operation andin testing

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Samuel Oosterholt 81028:PICARSO

Hardware: Previous Work

Image producing robots Scalable workspace size

Cable driven

Hektor (Franke & Lehni 2002) Viktor (Lehni & Rich 2008)

Hektor’s actuator configuration

Viktor’s actuator configuration

PICARSO’s actuator configuration

Two actuators ‘V’ Configuration Relies on gravity

Four actuators ‘X’ Configuration


Mechanical System

Three motors

‘Y’ configuration

Cables

Upper motors control position

Lower stabilises Reduces cost

Full System

Render of PICARSO system


Mechanical System

Base Plate

Motor

Motor Controller

Spooling System

Cable feeding system

Motor Mount


Mechanical System

Motor Mount

Motors parallel to painting surface

Plate mounts to painting surface

Double spool and bearing

Two cables Reduce yaw

and pitch Minimise

kickback

12

Cables: Spiderwire (Braid

fishing line) Ø = 0.30mm Tmax = 13.6kg Small elasticity

10m of cable 7×7m workspace

Cables attach to turnbuckles

Reorientated by eyebolts and pulleys

Can move pulleys and eyebolts Proximity to canvas Stability


Mechanical System

180°Motor Mount

Lower mount uses fairleads 180° sweep

13

Drive System Hardware Specifications

Hardware selected from Maxon MotorsNumerous operation

modesModular componentsDiscounted cost & support


250W EC45 Motors and Maxon EPOS2

70/10 Motor Controller

14

Mounting for Testing

Canvas implementedSimulate surfaceReduces ripple in wind


PICARSO’s easel in the Vibrations Laboratory

PICARSO’s easel in the FSAE shed

PICARSO’s canvas

Not feasible to wall mount for testing 3.6 × 3.2m easel (working area: 2.8 × 2.8m) Constrained by testing environment

http://www.facebook.com/photo.php?op=3&view=global&subj=151526334863543&pid=4893988&id=516498415&oid=151526334863543

151028:PICARSO Neil Yeoh

Painting System

Design Problem


System Architecture

Hardware

Painting System

Image Processing

Control Software

Testing and Results

Project Outcomes

Future Work

Neil Yeoh 16

Painting System Goals

Goals: Design an appropriate painting system

which: Produces circular patterns (< 3cm) Is fast (> 1Hz) and light (< 3kg) Does not cause instabilities Houses suitable paint capacity Is reliable, durable, and repeatable

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Circular Patterns ( <3cm)

Fast ( >1Hz)

Light ( <3kg)

No Instabilities

Paint Capacity

Reliable

Durable

Repeatable

Spray Can Airbrush Spray Gun*1 *2 *3

Painting Mechanisms

Three types of painting mechanisms

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** Image references at end of slides

Neil Yeoh 181028:PICARSO

Spray System Schematic

Paint Line

Air Line

Electrical Signal

Compressed Air Line

Electrical Air Compressor

Air Regulator

Automatic Pressure-fedSpray Gun

Personal Computer

SolenoidPressurised

Paint Canister

Paint Regulator

*4

*5

*6

*8

*7

*9

** Image references at end of slides

Neil Yeoh 19

Spray Gun Choice

Anest IwataSGA-101 Automatic Pressure-fed Spray Gun

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Fluid Adjust Knob

Fitting Knob

Paint Line

Pattern Adjust Knob

Air Line

Nozzle


Spray Gun Settings

Testing procedure Ideal Settings identified:

Results:Consistent < 3cm black circles

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

Spray Duration

Rep

eata

bilit

y


Painting System: Spray Gun Housing


Spray Gun Housing

Small Sleeve

Large Sleeve

BearingsEyebolts

Sleeve Shaft

Bearing Spacer

Spray Gun

231028:PICARSO Neil Yeoh

Spray Gun Housing

241028:PICARSO Ian Hooi

Image Processing

Design Problem


System Architecture

Hardware

Painting System

Image Processing

Control Software

Testing and Results

Project Outcomes

Future Work

Ian Hooi 25

Image Processing Goals

Goals: Develop image processing software to:

Transform input image to user specified settings

Reproduce input image in binary formOutput in Raster (pixel-by-pixel) form

Extension Goal:Output in Vector (line) form

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Ian Hooi 26

Image Transformations Procedure

1. Input image

2. Resized to appropriate resolution

3. Stretching and refitting

4. Greyscale formScaled from 0-1 where 0 is black, 1 is white

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Resized GreyscaleOriginal

27

Fill and Edge Binary Images

1028:PICARSO Ian Hooi

Fill Image Edge Image

Fill Images: Direct conversion from greyscale binary

Edge Images: outlines of the image

Fill Image Edge Image

Ian Hooi 28

Binary Image Threshold Settings

Converted to binary formThreshold filter

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Original Binary: Threshold = 0.25

Binary: Threshold = 0.5 Binary: Threshold = 0.75

Original Binary: Threshold = 0.25

Binary: Threshold = 0.5 Binary: Threshold = 0.75

Ian Hooi 29

Output Type: Raster vs. Vector

Raster: pixel by pixel approach Vector: line based approach

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10 20 30 40 50 60

20

30

40

50

60

70

Optimal

Raster Based Output

10 20 30 40 50 60

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25

30

35

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45

50

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60

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Vector Based Output

10 20 30 40 50 60

20

30

40

50

60

70

Optimal

Raster Based Output

10 20 30 40 50 60

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25

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35

40

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Vector Based Output

Ian Hooi 30

Raster Based Approach

Image output in binary form1 represents white0 represents black

Read and processed by Control Software Slow to paint

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1020

3040

5060

20 30 40 50 60 70

Optim

al

1020

3040

5060

20 25 30 35 40 45 50 55 60 65 70

=

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

Ian Hooi 31

Vector Based Approach

Aim: Raster Vector Searching Algorithm based on Portrayer

(Benedettelli 2008) and Erik’s XY Plotter (2007)

Adjacent pixels chains Control software

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=10 20 30 40 50 60

20

30

40

50

60

70

Optimal

10 20 30 40 50 60

20

25

30

35

40

45

50

55

60

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70

321028:PICARSO Joyce Phan

Control Software

Design Problem


System Architecture

Hardware

Painting System

Image Processing

Control Software

Testing and Results

Project Outcomes

Future Work

Joyce Phan 33

Control Software Goals

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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Control Software Flow Diagram

ImageProcessing

Output

Inverse Kinematics

Motor Commands

CableLengths

PositioningCommands

CartesianCo-ordinates

Spray Gun Commands

xy

L1 , L2 , L3

1 0 0 1

on off

turns

Motor Turn Units

ControlSoftware

OutputCommands


Vector Mode

Positioning Commands

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 ProcessingOutput

Raster Mode


Inverse Kinematics


Communication

PC Master

SolenoidRS232

Digital Output

RS232

RS232

Motor Controller 1 Slave

Motor Controller 2 SlaveMotor Controller 3 Slave

• Instructions from Master to Slaves via 3 Parallel RS232 links

• Outputs controlled in Maxon RS232 Communication Protocol

Joyce Phan 38

Motor Operation Modes

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Motor 3 Motor 2

Motor 1

Position Mode Position Mode

Current Mode

• Position Mode• Driven in steps

• Current Mode• Provides tension• Minimises instabilities

39

Click icon to add picture


Graphical User Interface (GUI)

• Easy access to user settings during operation


Testing and Results

Design Problem


System Architecture

Hardware

Painting System

Image Processing

Control Software

Testing and

Results

Project Outcomes

Future Work

Sven Paschburg 41

Testing and Results

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

1028:PICARSO


Testing and Results

Kinematics test bedTri-motor Y-configuration proof of concept

Scaled System - µAngelo

Oblique angle picture of µAngelo

Front view picture of µAngelo

Picture of µAngelo’send-effector


Testing and Results

Raster painting functionalityScalable across a vertical surface

• Up to 3×3m workspace areaComplete a picture in 1 hour

Full Scale System - PICARSO

Accuracy & PrecisionReliabilityWorkspace Resolution

Pixel SizeStabilitySpeed

Test Metrics

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100 mm

50 mm10 mm

25 mm1028:PICARSO

Testing and ResultsRaster Painting Functionality

Specifications Achievement

Accuracy ± 10 mm

Precision ± 5 mm

Workspace Resolution

10 – 100 mm

Workspace Resolution (ideal)

10 – 25 mm


Testing and Results

Parameter Achievement

Pixel Size (min.) 8 mm

Pixel Size (ideal) 10 mm

Raster Painting Functionality

0.2 s 0.25 s 0.5 s 1.0 s

8 mm 10 mm 16 mm 20 mm


Testing and Results


Stability ± 25 mm (z – axis)

Raster Painting Functionality

x

z

y

Side view of the end-effector

Bottom view of the end-effectoryaw

pitch

roll


Testing and ResultsScalable across a vertical surface


Workspace Area 0.5 × 0.5 m to7.0 × 7.0 m

A picture showing the ability of the end-effector to move around the workspace

Sven Paschburg 48

Goals: Scaled system – µAngelo Full scale system – PICARSO Image processing software Control software Graphical User Interface (GUI)

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Project Outcomes

A 1.8 × 1.8m painted ‘fills’ image of the Mona Lisa

Oblique angle picture of µAngelo

Sven Paschburg 49

Future Work

Extension Goals: Analog communication Complete a picture in 1 hour Vector-based painting Touch screen interface

Future years: Colour painting Wireless communication Commercial product

1028:PICARSO


Questions and Comments?

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References

Benedettelli, D. 2008, NXT Portrayer Robot, viewed 11th April 2010 <http://robotics.benedettelli.com/portrayer.htm>

Convict Episcopal de Luxembourg, 2007, Erik’s XY-Plotter, viewed 11th April 2010, <http://www.convict.lu/Jeunes/ultimate_stuff/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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Sven Paschburg 52

Painting System References

1. Spray can - http://comps.fotosearch.com/comp/UNN/UNN113/spray-can_~u14072509.jpg

2. Airbrush - http://imgs.tootoo.com/e1/00/e10006f419523aa3bb036a77fb5038dd.jpg

3. Spray gun - http://image.made-in-china.com/2f0j00DaTQeBfgIEMJ/HVLP-Spray-Gun-RP8021-2S-H827-S-.jpg

4. Air Compressor - http://toolmonger.com/wp-content/uploads/2006/05/aircompressor.jpg

5. Festo Air Regulator - http://www.festo.com/rep/nl-be_be/assets/LR_2008_1314u.jpg

6. Pressurised Paint Canister - http://www.smitsgroup.co.nz/images/objectimages/PT2-Paint-Tank.jpg

7. Festo Solenoid Valve - http://www.luconda.com/artikeldetails/27/29/xx/bilder/2093457-1-Festo-CPE18-M1H-3GL-1-4.jpg

8. Iwata Spray Gun - http://www.anest-iwata.co.jp/english/products/paint/prd/fog/auto/images/ceramic_ph01.jpg

9. Personal Computer - http://www.ubergizmo.com/photos/2008/12/dell-xps-13.jpg

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53

Further Examples and Information


Hardware

HardwarePainting System

Image Processing

Control Software

PICARSO

Mechanical &Electrical

Software


End-Effector


Higher Level Software Flow Diagram


Raster Functionality


Vector Functionality

Ian Hooi 591028:PICARSO

Vector Algorithm Demonstration

Joyce Phan 601028:PICARSO

PICARSO Analog Communication

PC MasterEPOS2 Slave (Node 1)

EPOS2 Slave (Node 2)

EPOS2 Slave (Node 3)

NI PCI-6221 card(Analogue Inputs)

NI SB 68-LPBreakout Board

61



Control Software: Main Menu GUI

62



Control Software: Paint Raster Image GUI

63



Control Software: Paint Vector Image GUI


100mm Pixel Spacing


50mm Pixel Spacing


25 mm Pixel Spacing


10 mm Pixel Spacings


Testing and Results

Raster processing functionalityProcess an image in less than 60sVector processing functionality

Image Processing Software

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 < 3 s


Testing and Results

Raster functionalityHuman Machine Interface (HMI)Vector functionalityTouch screen interface

High Level Control Software

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)


Testing and Results

Position the motorsControl the motor velocity and accelerationRead back the current positionActuate the spray gun solenoid

Low Level Control Software

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


Testing and ResultsComplete a picture in 1 hour

Parameter Specification

Speed 2 – 250 mms-1

Acceleration 2 – 150 mms-2

Picture Time (av.) 2.5 hrs (edge image)

A 1.8 × 1.8m painted ‘fills’ image of the Mona Lisa

A 1 × 1m painted ‘edge’ image of the University of Adelaide logo

1028:picarsosven paschburg2 system process design problems design specifications hardware painting...

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