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ECE5320 Mechatronics Assignment#01: Literature Survey on Sensors and Actuators

Machine Vision Introduction . ECE5320 Mechatronics Assignment#01: Literature Survey on Sensors and Actuators . Prepared by: William Bourgeous Dept. of Electrical and Computer Engineering Utah State University E-mail: williamk@cc.usu.edu Phone: ( 435) 750-0147 . Outline .

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ECE5320 Mechatronics Assignment#01: Literature Survey on Sensors and Actuators

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  1. Machine Vision Introduction ECE5320 MechatronicsAssignment#01: Literature Survey on Sensors and Actuators Prepared by: William Bourgeous Dept. of Electrical and Computer Engineering Utah State University E-mail: williamk@cc.usu.edu Phone: (435) 750-0147

  2. Outline • Basic working principle • Benefits • Major applications • Basic system types • Capabilities • Limitations • Selected Examples • Selection and Purchasing • Reference list • To probe further

  3. Machine Vision • In the past, Human eyes did what no machines could do: • locating and positioning work, tracking the flow of parts, and inspecting output for quality and consistency. • “Today, the requirements of many manufacturing processes have surpassed the limits of human eyesight.” • “Machine Vision provides manufacturing equipment with the gift of sight.” Courtesy of: Cognex

  4. Machine Vision Working Principle • In a typical machine vision application: • A video camera positioned on the production line captures an image of the part to be inspected and sends it to the machine vision computer. • The computer then uses sophisticated image analysis software to extract information from images and generate decisions about those images, such as: (next) Courtesy of: Cognex

  5. Machine Vision Working Principle (cont) • Where is it? • Locate objects accurately even within complex or confusing scenes. • How good is it? • Inspect objects to ensure quality and consistency. • What is it? • Identify objects by analyzing their shapes or by reading serial numbers on their surfaces. • What size is it? • Make measurements. Courtesy of: Cognex

  6. Machine Vision Working Principle (cont) Courtesy of: dvtsensors

  7. Machine Vision System Components: The key system components are: • Lighting • Camera • Part Sensor • Imaging • Processing • Inspection Software • Communications/Networking Courtesy of:datx

  8. Machine Vision Benefits • No “fixturing” of parts required • Completely Visual, No touching or bumping • Streamline operations • Can be integrated with overall system “Eyes get tired. People make mistakes. I have a high degree of confidence with machine vision systems that are properly set up.” - Vincent Conforti, Donnelly Electronics Courtesy of: Data Translation

  9. Capabilities • Machine vision is used in various industrial and medical applications. Examples include: • Electronic component analysis • Signature identification • Optical character recognition • Handwriting recognition • Object recognition • Pattern recognition • Materials inspection • Currency inspection • Medical image analysis Courtesy of: Whatis.com

  10. Limitations • Machine vision is currently limited by a combination of the following three items: • Processing Power • Communication Lines • Camera Resolution • Integration Cost and Time Courtesy of: Whatis.com

  11. System Types • Triangulation • Laser Radar • Sonar • Stereo Vision (2D) • (2 and ½ D) • Hybrid Systems • 3D Vision 3D Vision is the most advanced and applicable technology at this time. Laser Radar Triangulation

  12. Object Recognition, Measurement, Condition • System can locate objects based on their edge characteristics • Advantages: • Orientation independent • Scale independent • Touching or overlapping parts • Lighting variation • Repeatability Courtesy of: adept

  13. One Camera 3D System • Algorithms use a single still image from a compact CCD video camera mounted on the robot end-effector to calculate the full 3D location of the part (i.e., x, y, z position and roll, pitch and yaw angles). • This information is transmitted to the robot controller over a high-speed communication line. The robot controller uses this to guide • The robot's hand and intercept each part correctly for grasping or performing other robotic processes. Courtesy of: Braintech

  14. One Camera 3D System (Cont) Courtesy of: Braintech

  15. Typical (3D) Application Courtesy of: Braintech

  16. Ford Example System Objectives: • Identify randomly placed engine block • Identify orientation with respect to robot end effector. • Accurately position robot to “grab” part • Correctly maneuver and deposit part at destination, possibly in motion. • Complete tasks with a Single Camera.

  17. Ford Example (cont) Courtesy of: Braintech

  18. PCB Connector Examination Example • Challenge: • An electronics manufacturer needed an accurate, cost-effective method to inspect the orientation of connectors on a printed circuit board (PCB). • Machine Vision Solution: • A good image showing the correct orientation of the connectors on the PCB was stored. A picture of the production PCB is obtained via frame grabber and compared to the original. The image passed or failed based on the match against the good image.

  19. Getting Started • Learn about the Technology • Identify Application • Match the Technology to the Application • Identify Vendors Who Can Provide Solutions • Execute a Feasibility Demonstration • Write the Specification • Request Quotations • Evaluate Responses • Purchase • Integrate • Maintenance

  20. Reputable Suppliers • Braintech • Cognex • Adept • Data Translation • National Instruments • Applied Machine Vision (AMV)

  21. To explore further • Automated Imaging Association AIA http://www.machinevisiononline.org • Braintech http://www.braintech.com • Society of Mechanical Engineers SME http://www.sme.org/cgi-bin/communities.pl?/ communities/mva/industrylinks.htm&&&SME& • Intel Open Source Computer Vision Library http://www.intel.com/research/mrl/research/opencv/

  22. Reference list • Machine Vision Online • 3D Machine Vision as a Shop Floor Metrology Toolhttp://www.machinevisiononline.org/public/articles/ General_Electric.pdf • Single Camera 3DTM (SC3DTM) http://www.machinevisiononline.org/public/articles/Babak_Habibi_July03.pdf • Geometric Object Recognition in Robotics http://www.sme.org/downloads/mva/Pelton.pdf • Machine Vision Today http://www.sme.org/downloads/mva/Mancini.pdf

  23. The End Questions? Prepared by: William Bourgeous Dept. of Electrical and Computer Engineering Utah State University E-mail: williamk@cc.usu.edu Phone: (435) 750-0147

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