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Vision-Guided Robot Position Control

Vision-Guided Robot Position Control. Tony Baumgartner – Computer Science Jeff Clements – Mechanical Engineer Norman Pond – Electrical Engineer Brock Shepard – Mechanical Engineer Nicholas Vidovich – Computer Engineer. SKYNET. Advisors: Dr. Juliet Hurtig & Dr. J.D. Yoder. February 19, 2004.

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Vision-Guided Robot Position Control

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  1. Vision-Guided RobotPosition Control Tony Baumgartner – Computer Science Jeff Clements – Mechanical Engineer Norman Pond – Electrical Engineer Brock Shepard – Mechanical Engineer Nicholas Vidovich – Computer Engineer SKYNET Advisors: Dr. Juliet Hurtig & Dr. J.D. Yoder February 19, 2004

  2. Problem Identification

  3. Equipment Provided • Desktop computer • Two CCD (Charge-Coupled Device ) cameras • ABB Robot • Controller

  4. Robot ABB – Articulated IRB 140

  5. Controller

  6. Design Deliverables Image ProcessingCompletion Date • Difference Algorithm 12/19/03 • Blob Detection 02/20/04 • Object Recognition 03/17/04 • User Interface 03/17/04 Overall • CPU/Robot Communication 03/17/04 • Gripper Implementation 03/17/04 • Testing 04/15/04

  7. Vision Systems Eye-On-Hand Configuration • Limited view of work area • Partial view of object • Continually changes coordinate system mapping

  8. Vision Systems External Stereo Camera Configuration • Distance fixed between cameras • Coordinate system relation between camera space and real space fixed • Full view of object

  9. Compare Using Difference Capture Initial Capture Current Ready? Done? Identify Object / Find Coordinates Controller Commands Robot Check Actions Send Instructions Software Block Diagram

  10. Difference Algorithm Visuals

  11. Object Recognition Algorithms • Shape-Based Matching • Thresholding • Blob Detection • Pattern Recognition • Similarity Measure

  12. Object Recognition VisualsNo Object in Scene

  13. Object Recognition VisualsObject Enters Scene

  14. Object Recognition VisualsBlob Show

  15. Object Recognition VisualsBlob Find

  16. Other Object Recognition Visuals

  17. Other Object Recognition Visuals

  18. Current Status • Object recognition is currently being developed • Serial communications with robot controller and computer are being established to send and receive coordinate values

  19. Future Implementation After Completion of Object Recognition • Develop a coordinate system that can be converted from pixels to millimeters • Develop calculations of where the object actually is in the camera view compared with the actual space

  20. Future Implementation After Completion of Controller/Computer Connection • Develop algorithm that loops receiving values and transmitting values to move the robot

  21. QUESTIONS ?

  22. References <1> ABB Product Specification Sheet (2003) <2> Lin, C.T., Tsai D.M. (2002) Fast normalized cross correlation for defect detection. Machine Vision. Yuan-Ze University,1-5. <3> Phil Baratti “Robot Precision” (personal communication, November 4 2003) <4> Stegar, C.., 2001. Similarity measures for occlusion, clutter, and illumination invariant object recognition. In: B. Radig and S. Florczyk(eds), Mustererkennung 2001, Springer Munchen, pp. 145-154. <5> Stegar, C., Ulrich, M. 2002 Performance Evaluation of 2D Object Recognition Techniques. Technical Report: Technische Universitat Munchen, 1-15. <6> Robots and Manufacturing Automation, pg. 220-222. <7> http://www.prip.tuwien.ac.at/Research/RobotVision/vs.html

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