2021
DOI: 10.1109/access.2021.3049964
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Efficient TCP Calibration Method for Vision Guided Robots Based on Inherent Constraints of Target Object

Abstract: Tool Center Point (TCP) calibration and target object calibration are essential to guarantee the accuracy of Vision Guided Robot (VGR) systems. After calibration, the robot can know the object's position and orientation from the vision system and then move the TCP to a target point. However, conventional calibration methods are time-consuming and often resort to external tools. We propose a universal method based on the inherent constraints of the target points and use it to simultaneously calibrate the TCP an… Show more

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Cited by 11 publications
(2 citation statements)
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“…In this approach, a global minimum is guaranteed by using only a 1D line search of a convex function. Yang et al developed a tool center point (TCP) calibration method without the need for external tools [25]. The efficiency and accuracy of TCP calibration were achieved by establishing a constraint model and minimizing reprojection error.…”
Section: Related Workmentioning
confidence: 99%
“…In this approach, a global minimum is guaranteed by using only a 1D line search of a convex function. Yang et al developed a tool center point (TCP) calibration method without the need for external tools [25]. The efficiency and accuracy of TCP calibration were achieved by establishing a constraint model and minimizing reprojection error.…”
Section: Related Workmentioning
confidence: 99%
“…One major challenge for an autonomous agricultural robot in an orchard is row following. Recently, vision sensors have been widely used in agricultural robot navigation since their low cost, high efficiency, and capability to provide huge information [9][10][11][12][13][14][15][16][17]. In our previous study, a row-following system based on traditional machine vision for an apple orchard was designed, of which navigation was divided into multiple subtasks, such as image binarization, boundaries detection, guidance path generation, coordinate transformation, and low-level motor control [18].…”
Section: Introductionmentioning
confidence: 99%