Smoothness optimization Based on measured 3D point cloud in robotic drilling

Gang Rao; Xiangdong Yang; Jing Xu; Ken Chen; Guanglie Zhang

doi:10.1109/CYBER.2017.8446163

Smoothness optimization Based on measured 3D point cloud in robotic drilling

Gang Rao
, Xiangdong Yang
, Jing Xu^*
, Ken Chen
, Guanglie Zhang

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In large assembly, the smoothness is important for fluid dynamic configuration and installation stability and which is partly depended on the perpendicularity of rivet installation. Thus, the normal direction of a drilling surface is required to be measured online other than extracted from CAD model because of deflection between CAD model and real product for tool error, assembly deformation and other factors. Besides the normal direction, the local surface smoothness after rivet installation should also be taken into account significantly before drilling. To this end, a smoothness optimization method based on measured 3D point is proposed. First the structured-light-based 3D sensor is used for measuring the drilling surface profile; Second, the tensor voting framework is utilized to processing raw point cloud for denoising; Third, a quadratic surface is fitted on the TVF output unciformed points set and a novel smoothness metric of the surface with rivet installation is defined by continuity and consistency based on local fitted quadratic surface; Finally, the surface smoothness after rivet installation is optimized. The proposed methods are verified by experiments conducted on several simulation scenarios and the result shows effectiveness of the proposed method.

Original language	English
Title of host publication	The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems
Publisher	IEEE
Pages	797-802
ISBN (Electronic)	978-1-5386-0490-8
ISBN (Print)	978-1-5386-0491-5
DOIs	https://doi.org/10.1109/CYBER.2017.8446163
Publication status	Published - Jul 2017
Externally published	Yes
Event	7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017) - Sheraton Princess Kaiulani, Honolulu, United States Duration: 31 Jul 2017 → 4 Aug 2017 http://ieee-cyber.org/2017/ https://ieeexplore.ieee.org/xpl/conhome/8424837/proceeding

Publication series

Name	IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER

Conference

Conference	7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017)
Abbreviated title	IEEE-CYBER 2017
Place	United States
City	Honolulu
Period	31/07/17 → 4/08/17
Internet address	http://ieee-cyber.org/2017/ https://ieeexplore.ieee.org/xpl/conhome/8424837/proceeding

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10.1109/CYBER.2017.8446163

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Rao, G., Yang, X., Xu, J., Chen, K., & Zhang, G. (2017). Smoothness optimization Based on measured 3D point cloud in robotic drilling. In The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems (pp. 797-802). Article 8446163 (IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER). IEEE. https://doi.org/10.1109/CYBER.2017.8446163

Rao, Gang ; Yang, Xiangdong ; Xu, Jing et al. / Smoothness optimization Based on measured 3D point cloud in robotic drilling. The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems. IEEE, 2017. pp. 797-802 (IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER).

@inproceedings{0f70ddc06bf44cc89c9761a0ca1154f7,

title = "Smoothness optimization Based on measured 3D point cloud in robotic drilling",

abstract = "In large assembly, the smoothness is important for fluid dynamic configuration and installation stability and which is partly depended on the perpendicularity of rivet installation. Thus, the normal direction of a drilling surface is required to be measured online other than extracted from CAD model because of deflection between CAD model and real product for tool error, assembly deformation and other factors. Besides the normal direction, the local surface smoothness after rivet installation should also be taken into account significantly before drilling. To this end, a smoothness optimization method based on measured 3D point is proposed. First the structured-light-based 3D sensor is used for measuring the drilling surface profile; Second, the tensor voting framework is utilized to processing raw point cloud for denoising; Third, a quadratic surface is fitted on the TVF output unciformed points set and a novel smoothness metric of the surface with rivet installation is defined by continuity and consistency based on local fitted quadratic surface; Finally, the surface smoothness after rivet installation is optimized. The proposed methods are verified by experiments conducted on several simulation scenarios and the result shows effectiveness of the proposed method.",

author = "Gang Rao and Xiangdong Yang and Jing Xu and Ken Chen and Guanglie Zhang",

year = "2017",

month = jul,

doi = "10.1109/CYBER.2017.8446163",

language = "English",

isbn = "978-1-5386-0491-5",

series = "IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER",

publisher = "IEEE",

pages = "797--802",

booktitle = "The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems",

address = "United States",

note = "7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017), IEEE-CYBER 2017 ; Conference date: 31-07-2017 Through 04-08-2017",

url = "http://ieee-cyber.org/2017/, https://ieeexplore.ieee.org/xpl/conhome/8424837/proceeding",

}

Rao, G, Yang, X, Xu, J, Chen, K & Zhang, G 2017, Smoothness optimization Based on measured 3D point cloud in robotic drilling. in The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems., 8446163, IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER, IEEE, pp. 797-802, 7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017), Honolulu, Hawaii, United States, 31/07/17. https://doi.org/10.1109/CYBER.2017.8446163

Smoothness optimization Based on measured 3D point cloud in robotic drilling. / Rao, Gang; Yang, Xiangdong; Xu, Jing et al.
The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems. IEEE, 2017. p. 797-802 8446163 (IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Smoothness optimization Based on measured 3D point cloud in robotic drilling

AU - Rao, Gang

AU - Yang, Xiangdong

AU - Xu, Jing

AU - Chen, Ken

AU - Zhang, Guanglie

PY - 2017/7

Y1 - 2017/7

N2 - In large assembly, the smoothness is important for fluid dynamic configuration and installation stability and which is partly depended on the perpendicularity of rivet installation. Thus, the normal direction of a drilling surface is required to be measured online other than extracted from CAD model because of deflection between CAD model and real product for tool error, assembly deformation and other factors. Besides the normal direction, the local surface smoothness after rivet installation should also be taken into account significantly before drilling. To this end, a smoothness optimization method based on measured 3D point is proposed. First the structured-light-based 3D sensor is used for measuring the drilling surface profile; Second, the tensor voting framework is utilized to processing raw point cloud for denoising; Third, a quadratic surface is fitted on the TVF output unciformed points set and a novel smoothness metric of the surface with rivet installation is defined by continuity and consistency based on local fitted quadratic surface; Finally, the surface smoothness after rivet installation is optimized. The proposed methods are verified by experiments conducted on several simulation scenarios and the result shows effectiveness of the proposed method.

AB - In large assembly, the smoothness is important for fluid dynamic configuration and installation stability and which is partly depended on the perpendicularity of rivet installation. Thus, the normal direction of a drilling surface is required to be measured online other than extracted from CAD model because of deflection between CAD model and real product for tool error, assembly deformation and other factors. Besides the normal direction, the local surface smoothness after rivet installation should also be taken into account significantly before drilling. To this end, a smoothness optimization method based on measured 3D point is proposed. First the structured-light-based 3D sensor is used for measuring the drilling surface profile; Second, the tensor voting framework is utilized to processing raw point cloud for denoising; Third, a quadratic surface is fitted on the TVF output unciformed points set and a novel smoothness metric of the surface with rivet installation is defined by continuity and consistency based on local fitted quadratic surface; Finally, the surface smoothness after rivet installation is optimized. The proposed methods are verified by experiments conducted on several simulation scenarios and the result shows effectiveness of the proposed method.

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U2 - 10.1109/CYBER.2017.8446163

DO - 10.1109/CYBER.2017.8446163

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-5386-0491-5

T3 - IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER

SP - 797

EP - 802

BT - The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems

PB - IEEE

T2 - 7th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017)

Y2 - 31 July 2017 through 4 August 2017

ER -

Rao G, Yang X, Xu J, Chen K, Zhang G. Smoothness optimization Based on measured 3D point cloud in robotic drilling. In The 7th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems. IEEE. 2017. p. 797-802. 8446163. (IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER). doi: 10.1109/CYBER.2017.8446163

Smoothness optimization Based on measured 3D point cloud in robotic drilling

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