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Sensors 2013, 13(12), 16565-16582;

A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems

State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
Author to whom correspondence should be addressed.
Received: 23 September 2013 / Revised: 18 November 2013 / Accepted: 26 November 2013 / Published: 3 December 2013
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1203 KB, uploaded 21 June 2014]


A vision-based robot self-calibration method is proposed in this paper to evaluate the kinematic parameter errors of a robot using a visual sensor mounted on its end-effector. This approach could be performed in the industrial field without external, expensive apparatus or an elaborate setup. A robot Tool Center Point (TCP) is defined in the structural model of a line-structured laser sensor, and aligned to a reference point fixed in the robot workspace. A mathematical model is established to formulate the misalignment errors with kinematic parameter errors and TCP position errors. Based on the fixed point constraints, the kinematic parameter errors and TCP position errors are identified with an iterative algorithm. Compared to the conventional methods, this proposed method eliminates the need for a robot-based-frame and hand-to-eye calibrations, shortens the error propagation chain, and makes the calibration process more accurate and convenient. A validation experiment is performed on an ABB IRB2400 robot. An optimal configuration on the number and distribution of fixed points in the robot workspace is obtained based on the experimental results. Comparative experiments reveal that there is a significant improvement of the measuring accuracy of the robotic visual inspection system. View Full-Text
Keywords: self-calibration; industrial robot; visual sensor; TCP self-calibration; industrial robot; visual sensor; TCP
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Yin, S.; Ren, Y.; Zhu, J.; Yang, S.; Ye, S. A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems. Sensors 2013, 13, 16565-16582.

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