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Camera-aided SCARA arm calibration

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Date Issued:
1994
Summary:
The focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically continuous model. By repeatedly calibrating the camera, the pose of the robot end-effector are collected at various robot measurement configurations. A least squares technique is then applied to estimate the geometric error parameters of the SCARA arm using the measured robot poses. In order to improve the robustness of the method, a new approach is proposed to calibrate the hand-mounted camera. The calibration algorithm is designed to deal with the case in which the camera sensor plane is nearly-parallel to the camera calibration board. Practical issues regarding robot calibration in general and SCARA arm calibration in particular are also addressed. Experiment studies reveal that the proposed camera-aided approach is a viable means for accuracy enhancement of SCARA arms.
Title: Camera-aided SCARA arm calibration.
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Name(s): Wu, Wen-chiang.
Florida Atlantic University, Degree grantor
Zhuang, Hanqi, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1994
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 107 p.
Language(s): English
Summary: The focus of this thesis is the kinematic calibration of a SCARA arm with a hand-mounted camera. Kinematic calibration can greatly improve the accuracy of SCARA arms, which are widely used in electronic assembly lines. Vision-based robot calibration has the potential of being a fast, nonintrusive, low-cost, and autonomous approach. In this thesis, we apply a vision-based technique to calibrate SCARA arms. The robot under investigation is modeled by the modified complete and parametrically continuous model. By repeatedly calibrating the camera, the pose of the robot end-effector are collected at various robot measurement configurations. A least squares technique is then applied to estimate the geometric error parameters of the SCARA arm using the measured robot poses. In order to improve the robustness of the method, a new approach is proposed to calibrate the hand-mounted camera. The calibration algorithm is designed to deal with the case in which the camera sensor plane is nearly-parallel to the camera calibration board. Practical issues regarding robot calibration in general and SCARA arm calibration in particular are also addressed. Experiment studies reveal that the proposed camera-aided approach is a viable means for accuracy enhancement of SCARA arms.
Identifier: 15075 (digitool), FADT15075 (IID), fau:11853 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.E.)--Florida Atlantic University, 1994.
Subject(s): Robots--Calibration
Manipulators (Mechanism)--Calibration
Robots--Error detection and recovery
Image processing
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15075
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.