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1028:PICARSO

Ian Hooi Samuel Oosterholt Sven Paschburg Joyce Phan Neil Yeoh Supervisor: A/Prof Ben Cazzolato. Programmable interface controller with Autonomous R obotic spraying operation. 1028:PICARSO. Seminar Outline. Hardware. System Process. Painting System. Project Outcomes.

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1028:PICARSO

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  1. Ian Hooi Samuel Oosterholt Sven Paschburg Joyce Phan Neil Yeoh Supervisor: A/Prof Ben Cazzolato Programmable interface controller withAutonomous Robotic spraying operation 1028:PICARSO

  2. Seminar Outline Hardware System Process Painting System Project Outcomes Design Problems Testing and Results Future Work Image Processing Design Specifications Control Software Sven Paschburg

  3. System Process • PICARSO: • Cable-driven robot • Process standard image formats • Reproduce images on vertical surface Control Software Image Processing Hardware Painting System Sven Paschburg

  4. Design Problems Image Output End-Effector Original Painted Processed Sven Paschburg

  5. Design Specifications MATLAB Motor 2 Motor 3 Image Processing Toolbox RS232 Cables Cables Vertical Wall End-Effector (Mathworks 2010) Graphical User Interface Design Environment (GUIDE) Motor 1 Sven Paschburg

  6. Hardware Hardware Design Problem Painting System Project Outcomes Design Specifications Testing and Results Future Work Image Processing System Architecture Control Software Samuel Oosterholt

  7. Hardware Goals 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 Samuel Oosterholt

  8. Hardware: Previous Work Hektor (Franke & Lehni 2002) Viktor (Lehni & Rich 2008) • Two actuators • ‘V’ Configuration • Relies on gravity • Four actuators • ‘X’ Configuration Hektor’s actuator configuration PICARSO’s actuator configuration Viktor’s actuator configuration • Image producing robots • Scalable workspace size • Cable driven Samuel Oosterholt

  9. Mechanical System • Full System Render of PICARSO system • Three motors • ‘Y’ configuration • Cables • Upper motors control position • Lower stabilises • Reduces cost Samuel Oosterholt

  10. Mechanical System • Motor Mount Base Plate Motor Motor Controller Spooling System Cable feeding system Samuel Oosterholt

  11. 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 Samuel Oosterholt

  12. Mechanical System • Motor Mount 180° • Lower mount uses fairleads • 180° sweep • 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 Samuel Oosterholt

  13. Drive System Hardware Specifications • Hardware selected from Maxon Motors • Numerous operation modes • Modular components • Discounted cost & support 250W EC45 Motors and Maxon EPOS2 70/10 Motor Controller Samuel Oosterholt

  14. Mounting for Testing • Canvas implemented • Simulate surface • Reduces ripple in wind • Not feasible to wall mount for testing • 3.6 × 3.2m easel (working area: 2.8 × 2.8m) • Constrained by testing environment PICARSO’s easel in the Vibrations Laboratory PICARSO’s easel in the FSAE shed PICARSO’s canvas Samuel Oosterholt

  15. Painting System Hardware Design Problem Painting System Project Outcomes Design Specifications Testing and Results Future Work Image Processing System Architecture Control Software 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 Neil Yeoh

  17. Painting Mechanisms • Three types of painting mechanisms *1 *2 *3 ** Image references at end of slides Neil Yeoh 17

  18. Spray System Schematic Automatic Pressure-fed Spray Gun Electrical Air Compressor Paint Line Air Regulator Compressed Air Line *7 Personal Computer *5 *4 *8 Air Line Paint Regulator Solenoid Electrical Signal Pressurised Paint Canister *9 *6 ** Image references at end of slides Neil Yeoh

  19. Spray Gun Choice • Anest Iwata • SGA-101 Automatic Pressure-fed Spray Gun Pattern Adjust Knob Fluid Adjust Knob Air Line Fitting Knob Nozzle Paint Line Neil Yeoh

  20. Spray Gun Settings Repeatability Spray Duration • Testing procedure • Ideal Settings identified: • Results: • Consistent < 3cm black circles Neil Yeoh

  21. Painting System: Spray Gun Housing Neil Yeoh

  22. Spray Gun Housing Large Sleeve Sleeve Shaft Bearing Spacer Small Sleeve Eyebolts Bearings Spray Gun Neil Yeoh

  23. Spray Gun Housing Neil Yeoh

  24. Image Processing Hardware Design Problem Painting System Project Outcomes Design Specifications Testing and Results Future Work Image Processing System Architecture Control Software Ian Hooi

  25. Image Processing Goals 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 Ian Hooi

  26. Image Transformations Procedure • Input image • Resized to appropriate resolution • Stretching and refitting • Greyscale form • Scaled from 0-1 where 0 is black, 1 is white Original Resized Greyscale Ian Hooi

  27. Fill and Edge Binary Images • Fill Images: Direct conversion from greyscale  binary • Edge Images: outlines of the image Edge Image Edge Image Fill Image Fill Image Ian Hooi

  28. Binary Image Threshold Settings • Converted to binary form • Threshold filter Original Original Binary: Threshold = 0.25 Binary: Threshold = 0.25 Binary: Threshold = 0.5 Binary: Threshold = 0.5 Binary: Threshold = 0.75 Binary: Threshold = 0.75 Ian Hooi

  29. Output Type: Raster vs. Vector • Raster: pixel by pixel approach • Vector: line based approach Vector Based Output Vector Based Output Raster Based Output Raster Based Output Ian Hooi

  30. Raster Based Approach • Image output in binary form • 1 represents white • 0 represents black • Read and processed by Control Software • Slow to paint 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 = Ian Hooi

  32. Control Software Hardware Design Problem Painting System Project Outcomes Design Specifications Testing and Results Future Work Image Processing System Architecture Control Software 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) Joyce Phan

  34. Control Software Flow Diagram Control Software Image Processing Output Positioning Commands 1 0 0 1 x y Cartesian Co-ordinates Inverse Kinematics L1 , L2 , L3 Cable Lengths Motor Turn Units turns Motor Commands Output Commands on off Spray Gun Commands Joyce Phan

  35. Positioning Commands 0 1 1 1 0 1 0 1 0 1 Vector Mode 1 1 0 1 1 Raster Mode Image Processing Output 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 Joyce Phan

  36. Inverse Kinematics Joyce Phan

  37. Communication Motor Controller 3 Slave Motor Controller 2 Slave • Instructions from Master to Slaves via 3 Parallel RS232 links • Outputs controlled in Maxon RS232 Communication Protocol Solenoid RS232 PC Master RS232 Digital Output RS232 Motor Controller 1 Slave Joyce Phan

  38. Motor Operation Modes Position Mode Position Mode Motor 3 Motor 2 • Position Mode • Driven in steps • Current Mode • Provides tension • Minimises instabilities Motor 1 Current Mode Joyce Phan

  39. Graphical User Interface (GUI) • Easy access to user settings during operation Joyce Phan

  40. Testing and Results Hardware Design Problem Painting System Project Outcomes Design Specifications Testing and Results Future Work Image Processing System Architecture Control Software 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 Sven Paschburg

  42. Testing and Results • Scaled System - µAngelo • Kinematics test bed • Tri-motor Y-configuration proof of concept Front view picture of µAngelo Oblique angle picture of µAngelo Picture of µAngelo’s end-effector Sven Paschburg

  43. Testing and Results • 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 • Reliability • Workspace Resolution • Pixel Size • Stability • Speed Sven Paschburg

  44. Testing and Results • Raster Painting Functionality 10 mm 50 mm 25 mm 100 mm Sven Paschburg

  45. Testing and Results • Raster Painting Functionality 0.2 s 0.25 s 0.5 s 1.0 s 8 mm 10 mm 16 mm 20 mm Sven Paschburg

  46. Testing and Results • Raster Painting Functionality z pitch roll y x yaw Bottom view of the end-effector Side view of the end-effector Sven Paschburg

  47. Testing and Results • Scalable across a vertical surface A picture showing the ability of the end-effector to move around the workspace Sven Paschburg

  48. Project Outcomes Goals: • Scaled system – µAngelo • Full scale system – PICARSO • Image processing software • Control software • Graphical User Interface (GUI) Oblique angle picture of µAngelo A 1.8 × 1.8m painted ‘fills’ image of the Mona Lisa 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 Sven Paschburg

  50. Questions and Comments? Sven Paschburg

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