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A novel hand-eye calibration method of picking robot based on TOF camera

Aiming at the stability of hand-eye calibration in fruit picking scene, a simple hand-eye calibration method for picking robot based on optimization combined with TOF (Time of Flight) camera is proposed. This method needs to fix the TOF depth camera at actual and calculated coordinates of the peach...

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Autores principales: Zhang, Xiangsheng, Yao, Meng, Cheng, Qi, Liang, Gunan, Fan, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9888730/
https://www.ncbi.nlm.nih.gov/pubmed/36733593
http://dx.doi.org/10.3389/fpls.2022.1099033
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author Zhang, Xiangsheng
Yao, Meng
Cheng, Qi
Liang, Gunan
Fan, Feng
author_facet Zhang, Xiangsheng
Yao, Meng
Cheng, Qi
Liang, Gunan
Fan, Feng
author_sort Zhang, Xiangsheng
collection PubMed
description Aiming at the stability of hand-eye calibration in fruit picking scene, a simple hand-eye calibration method for picking robot based on optimization combined with TOF (Time of Flight) camera is proposed. This method needs to fix the TOF depth camera at actual and calculated coordinates of the peach the end of the robot, operate the robot to take pictures of the calibration board from different poses, and record the current photographing poses to ensure that each group of pictures is clear and complete, so as to use the TOF depth camera to image the calibration board. Obtain multiple sets of calibration board depth maps and corresponding point cloud data, that is, “eye” data. Through the circle center extraction and positioning algorithm, the circle center points on each group of calibration plates are extracted, and a circle center sorting method based on the vector angle and the center of mass coordinates is designed to solve the circle center caused by factors such as mirror distortion, uneven illumination and different photographing poses. And through the tool center point of the actuator, the coordinate value of the circle center point on the four corners of each group of calibration plates in the robot end coordinate system is located in turn, and the “hand” data is obtained. Combined with the SVD method, And according to the obtained point residuals, the weight coefficients of the marker points are redistributed, and the hand-eye parameters are iteratively optimized, which improves the accuracy and stability of the hand-eye calibration. the method proposed in this paper has a better ability to locate the gross error under the environment of large gross errors. In order to verify the feasibility of the hand-eye calibration method, the indoor picking experiment was simulated, and the peaches were identified and positioned by combining deep learning and 3D vision to verify the proposed hand-eye calibration method. The JAKA six-axis robot and TuYang depth camera are used to build the experimental platform. The experimental results show that the method is simple to operate, has good stability, and the calibration plate is easy to manufacture and low in cost. work accuracy requirements.
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spelling pubmed-98887302023-02-01 A novel hand-eye calibration method of picking robot based on TOF camera Zhang, Xiangsheng Yao, Meng Cheng, Qi Liang, Gunan Fan, Feng Front Plant Sci Plant Science Aiming at the stability of hand-eye calibration in fruit picking scene, a simple hand-eye calibration method for picking robot based on optimization combined with TOF (Time of Flight) camera is proposed. This method needs to fix the TOF depth camera at actual and calculated coordinates of the peach the end of the robot, operate the robot to take pictures of the calibration board from different poses, and record the current photographing poses to ensure that each group of pictures is clear and complete, so as to use the TOF depth camera to image the calibration board. Obtain multiple sets of calibration board depth maps and corresponding point cloud data, that is, “eye” data. Through the circle center extraction and positioning algorithm, the circle center points on each group of calibration plates are extracted, and a circle center sorting method based on the vector angle and the center of mass coordinates is designed to solve the circle center caused by factors such as mirror distortion, uneven illumination and different photographing poses. And through the tool center point of the actuator, the coordinate value of the circle center point on the four corners of each group of calibration plates in the robot end coordinate system is located in turn, and the “hand” data is obtained. Combined with the SVD method, And according to the obtained point residuals, the weight coefficients of the marker points are redistributed, and the hand-eye parameters are iteratively optimized, which improves the accuracy and stability of the hand-eye calibration. the method proposed in this paper has a better ability to locate the gross error under the environment of large gross errors. In order to verify the feasibility of the hand-eye calibration method, the indoor picking experiment was simulated, and the peaches were identified and positioned by combining deep learning and 3D vision to verify the proposed hand-eye calibration method. The JAKA six-axis robot and TuYang depth camera are used to build the experimental platform. The experimental results show that the method is simple to operate, has good stability, and the calibration plate is easy to manufacture and low in cost. work accuracy requirements. Frontiers Media S.A. 2023-01-17 /pmc/articles/PMC9888730/ /pubmed/36733593 http://dx.doi.org/10.3389/fpls.2022.1099033 Text en Copyright © 2023 Zhang, Yao, Cheng, Liang and Fan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhang, Xiangsheng
Yao, Meng
Cheng, Qi
Liang, Gunan
Fan, Feng
A novel hand-eye calibration method of picking robot based on TOF camera
title A novel hand-eye calibration method of picking robot based on TOF camera
title_full A novel hand-eye calibration method of picking robot based on TOF camera
title_fullStr A novel hand-eye calibration method of picking robot based on TOF camera
title_full_unstemmed A novel hand-eye calibration method of picking robot based on TOF camera
title_short A novel hand-eye calibration method of picking robot based on TOF camera
title_sort novel hand-eye calibration method of picking robot based on tof camera
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9888730/
https://www.ncbi.nlm.nih.gov/pubmed/36733593
http://dx.doi.org/10.3389/fpls.2022.1099033
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