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

Vision-Guided Robot Position Control

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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. - PowerPoint PPT Presentation

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Page 1: Vision-Guided Robot Position Control

SKYNETTony Baumgartner – Computer Science

Jeff Clements – Mechanical Engineer

Norman Pond – Electrical Engineer

Brock Shepard – Mechanical Engineer

Nicholas Vidovich – Computer Engineer

Advisors: Dr. Juliet Hurtig & Dr. J.D. Yoder

February 19, 2004

Vision-Guided RobotPosition Control

Page 2: Vision-Guided Robot Position Control

Problem Identification

Page 3: Vision-Guided Robot Position Control

Equipment Provided

• Desktop computer

• Two CCD (Charge-Coupled Device ) cameras

• ABB Robot

• Controller

Page 4: Vision-Guided Robot Position Control

RobotABB – Articulated IRB 140

Page 5: Vision-Guided Robot Position Control

Controller

Page 6: Vision-Guided Robot Position Control

Design Deliverables

Image Processing Completion 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

Page 7: Vision-Guided Robot Position Control

Vision Systems

Eye-On-Hand Configuration

• Limited view of work area

• Partial view of object

• Continually changes coordinate system mapping

Page 8: Vision-Guided Robot Position Control

Vision Systems

External Stereo Camera Configuration

• Distance fixed between cameras

• Coordinate system relation between camera space and real space fixed

• Full view of object

Page 9: Vision-Guided Robot Position Control

Software Block Diagram CaptureInitial

Capture Current

Compare Using

Difference

Ready?

Identify Object

/Find

Coordinates

Send Instructions

Controller Commands

Robot

Check Actions

Done?

Page 10: Vision-Guided Robot Position Control

Difference Algorithm Visuals

Page 11: Vision-Guided Robot Position Control

Object Recognition Algorithms

• Shape-Based Matching– Thresholding– Blob Detection– Pattern Recognition– Similarity Measure

Page 12: Vision-Guided Robot Position Control

Object Recognition VisualsNo Object in Scene

Page 13: Vision-Guided Robot Position Control

Object Recognition VisualsObject Enters Scene

Page 14: Vision-Guided Robot Position Control

Object Recognition VisualsBlob Show

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Object Recognition VisualsBlob Find

Page 16: Vision-Guided Robot Position Control

Other Object Recognition Visuals

Page 17: Vision-Guided Robot Position Control

Other Object Recognition Visuals

Page 18: Vision-Guided Robot Position Control

Current Status

• Object recognition is currently being developed

• Serial communications with robot controller and computer are being established to send and receive coordinate values

Page 19: Vision-Guided Robot Position Control

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

Page 20: Vision-Guided Robot Position Control

Future Implementation

After Completion of Controller/Computer Connection

• Develop algorithm that loops receiving values and transmitting values to move the robot

Page 21: Vision-Guided Robot Position Control

QUESTIONS

?

Page 22: Vision-Guided Robot Position Control

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