Vision-Guided Motion. Presented by Tom Gray

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1 Vision-Guided Motion Presented by Tom Gray

2 Overview Part I Machine Vision Hardware Part II Machine Vision Software Part II Motion Control Part IV Vision-Guided Motion The Result

3 Harley Davidson Example

4 Vision-Guided Motion Overview Capture Image Locate Object Determine XYΘ Transform XYΘ Send Data Make Move

5 Overview Part I Machine Vision Hardware Part II Machine Vision Software Part II Motion Control Part IV Vision-Guided Motion The Result

6 Part I Machine Vision Hardware Components of a SmartSensor How a CCD works Image Acquisition: Environmental Protection Triggers Lighting Lenses

7 SmartSensor Components CCD or CMOS for image capture RAM for memory storage FLASH for non-volatile storage Circuit Board for Components Image Processor Communications/IO Ports

8 CCD Technology CCD - Charged Coupled Device An array of diodes that turn Photons into Electrons More photons produce more electric charge

9 CCD Manufacturing

10 CCD Structure

11 CCD Conveyor Analogy

12 CCD Layers

13 CCD Charge Shifting

14 CCD vs. CMOS CMOS sensors connect standard transistors and wires to every pixel. Each pixel value is read independently CMOS sensors have lower light sensitivity CMOS sensors are slower and more susceptible to noise. CMOS sensor can be produced on standard silicon lines and are thus cost effective.

15 CCD - Mixing Colored Light Red, Green and Blue light combine to form every color in the spectrum.

16 CCD - Capturing Color The light is filtered before it hits the CCD The most expensive systems use 3 CCDs A rotating filter can allow only one CCD A Bayer filter improves speed and cost

17 Image Acquisition Environment Triggers Lighting Lenses

18 Acquisition - Environment Controllable Temperature Wash-Down Maintainable Grease Dust Difficult Smoke Flying Debris

19 Acquisition - Triggers Hardwired I/O Almost every vision system requires a sensor to trigger the inspection Communications Commands from Motion Controllers, PLCs and PCs can also trigger inspections

20 Acquisition - Lighting The goal of lighting is to increase the contrast of the features you want to inspect Successful lighting involves a combination of up front design and experimentation Fortunately light generally travels in straight lines.

21 Lighting - Direct

22 Lighting - Darkfield

23 Lighting - Backlit

24 Lighting - Diffuse

25 Lighting Co-Axial DOL

26 Lighting Polarized/Filtered

27 Acquisition - Lenses Lenses selection is primarily driven by: Field of View/Resolution Object Distance Depth of Focus Lens sizing charts help with field of view and object distance Telecentric, Aspherical or Zoom lenses add extra capability

28 Calculating Resolution 2 in / 640 =.0031

29 Lens Sizing Chart

30 Overview Part I Machine Vision Hardware Part II Machine Vision Software Part II Motion Control Part IV Vision-Guided Motion The Result

31 Part II Machine Vision Software Binary Thresholding Sub-pixel Values Intensity, Gradient, Centroid Image Processing Tools: Intensity Edge Finding Precision Measurement Blob Analysis Object Location Color Matching.

32 Binary Thresholding

33 Sub-Pixel Values - Intensity Linear Interpolates to find an edge at an intensity level Adjusting the lighting can effect the edge value

34 Sub-Pixel Values - Gradients Fit parabola to gradient values More resistant to small lighting changes x = p + g g p p 1 2 g p g p 1 g p+ 1 X = Edge Location p = Pixel Position g p = Gradient between p and p+1

35 Sub-Pixel Values - Centroid The center of an object can also be located to subpixel precision with a simple centroid calculation. _ x _ y = = 1 N 1 N pixels pixels N pixels N pixels x y pixel pixel 1/10 to 1/100 of a pixel can be achieved

36 Algorithm Binary Threshold of pixels Count the percent of light pixels Compare with an acceptable value Applications Determine if the lens cap is on Determine that a coating has been applied Intensity

37 Edge Finding/Counting Algorithm Determine Pixel Values along a line Count an edge each time the values cross the threshold Application Connector Quality Short-Shot Detection

38 Precision Measurement Algorithm Perform Edge Detection at multiple locations Exclude outliers and average the values Application Rivet hole location Knife blade quality

39 Blob Analysis Algorithm Binary Threshold Image Preprocessing Group touching pixels Filter and sort results Application Candy Bar Sorting Plywood Knot Check

40 Object Location Algorithm Find Edge Points Create Edge Segments Compare with learned Segments Application Pick and place robot Label location

41 Color Matching Algorithm Teach multiple colors in RGB space Detect an average color in an area Compare with trained list Application Print Registry Gatorade Color Check

42 Machine Vision Software Demo

43 Overview Part I Machine Vision Hardware Part II Machine Vision Software Part II Motion Control Part IV Vision-Guided Motion The Result

44 Part III Motion Control Architectures: Standalone, PCbased, Integrated Information Flow: Motion Controller, Drive/Amplifier, Motor, Mechanics. Feedback Loops: Torque, Velocity, Position, Application Level

45 Architecture - PC-Based Encoder Carbide Tips Grinding Wheel 15:1 gearbox Drive Drive 1st Machine 2nd Machine 3rd Machine CPU Multiple cards in each computer

46 Architecture - Stand Alone Programmable Motion Controller Cutting tool Bit Drives

47 Architecture - PLC Based Leadscrew Overhead Gantry Empty Tray Row of 10 Batteries Photo Sensor Tray Armature Conveyor Chain Drive PLC PMC/Drive

48 Architecture - Integrated Belt & Pulley Measuring Wheel Cutting Wheel Encoder Servo Motor/Drive/Controller

49 Info Flow - Motion Input Stored Program Commands Serial/Ethernet Commands Output +/- 10 V signal (servo) 5V TTL pulses (stepper) Controller

50 Info Flow - Drive/Amplifier Input +/- 10 V signal 5V TTL pulses Output Commutated Current to motor windings

51 Info Flow - Motor Input Commutated current to motor windings Output Rotary or linear motion

52 Info Flow - Mechanics Input Rotary or Linear Motion from motor Output Rotary or Linear Motion with mechanical advantage.

53 Input Info Flow - Feedback Encoder Pulses Resolver Position Output Quadrature signal Analog Position Signal Device

54 Feedback - Torque Loop Command signal + - K Amp Amp

55 Feedback - Velocity Loop K FF Velocity Command - + E - K P + + E K Amp Amp K Tach

56 Feedback - Position Loop K FF + E - K PP K PI + + E - K VP T Amp K PD K Tach

57 Feedback - Application Logic K FF Application Logic + - E K PP K PI E K PD K VP TAmp K Tach External Inputs

58 Overview Part I Machine Vision Hardware Part II Machine Vision Software Part II Motion Control Part IV Vision-Guided Motion The Result

59 Part IV Vision-Guided Motion Communications: Ethernet, Serial, Hardwired I/O Coordinate Transformations/Mapping Vision-Guided Motion Review Candy bar demonstration

60 Communications The Vision Sensor must be able to send coordinates to the motion controller The Motion controller must be able to accept commands This means drivers Ethernet Serial I/O

61 Coordinate Transforms The vision pixel coordinates must be converted to real world coordinates Done by: Vision Sensor Additional PC Motion Controller

62 Vision-Guided Motion Review Capture Image Locate Object Determine XYΘ Transform XYΘ Send Data Make Move

63 Demonstration

64 Thank You Parker Compumotor Michael Schreiber DVT Brent Carlson NRCC Simon Tulluch - INGT

ME375 Lab Project. Bradley Boane & Jeremy Bourque April 25, 2018

ME375 Lab Project. Bradley Boane & Jeremy Bourque April 25, 2018 ME375 Lab Project Bradley Boane & Jeremy Bourque April 25, 2018 Introduction: The goal of this project was to build and program a two-wheel robot that travels forward in a straight line for a distance