基于机器学习的汽车电动座椅位姿调整方案研究

doi:10.3969/j.issn.1000-1158.2019.05.07

Abstract
Figure/Table
References (14)
Related Citation (15)

Download: PDF (1582 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To solve the coupling phenomenon in the adjustment process of auto electric seat before delivery, a method of seat pose adjustment based on machine learning is proposed. According to the internal mechanical structure of the seat, the position between specific points on the seat surface is linearly dependent. The location information of these points is collected by laser range sensor, based on the information, the piecewise ridge regression model, which is derived from the further training of ridge regression model by linear classifier is established and the adjustment scheme is given. The feasibility of the proposed method is verified by experiments, and the experimental results show that, according to the prediction of the piecewise ridge regression model, each switch only needs to be moved once to make the seat posture meet the factory standard and the error of seat posture adjustment can be reduced, so the proposed method can significantly improve the efficiency of seat posture adjustment.

Key words： metrology position and orientation adjustment segmented ridge regression machine learning

Received: 20 December 2017 Published: 01 September 2019

PACS:

TB92

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Bao-hua
	YAO Yue-xuan
	SUN Yu-kun

Cite this article:

LIU Bao-hua,YAO Yue-xuan,SUN Yu-kun. The Research of Auto Electric Seat's Position and Orientation Adjustment Method Based on Machine Learning[J]. Acta Metrologica Sinica, 2019, 40(5): 780-785.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.05.07 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I5/780

［1］Sunnanbo A. Laser Feedback Control for Roboticsin Aircrah Assembly［D］. Ostergotlan, Sweden: Linkopings University, 2003.
［2］Gonzalez-galvan E J, Cruz-Ramirez S R, Seelinger M J, et al. An Efficient Multi-camera, Multi-target Scheme for the Three dimensional Control of Robots using Uncalibrated Vision［J］. Robotics and Computer Integrated Manufacturig, 2003, 19(5): 387-400.
［3］熊有伦, 丁汉, 刘恩沧. 机器人学［M］. 北京: 机械工业出版社, 1993.
［4］赵俊伟, 党珊珊, 陈国强, 等. 一种新型3-PRS并联机构的位姿误差分析［J］. 机械传动, 2016, 40(7): 146-151.
Zhao J W,Dang S S,Chen G Q,et al.Posture erro ranalysis of a new 3-PRS parallel mechanism ［J］. Journal of Mechanical Transmission, 2016,40(7):146-151.
［5］端木强. 6R机器人及其末端位姿编程求解［J］. 现代机械, 2003, (1): 70-71.
Duan M Q. Programming and solving of 6R robot and its terminal pose［J］. Modern Machinery, 2003, (1): 70-71.
［6］张建亮,周建忠,罗敬文,等.汽车座椅调角器激光焊接工艺研究［J］.应用激光,2015,32(2):216-220.
Zhang J L, Zhou J Z, Luo J W, et al. Study on laser welding technology of automobile seat recliner［J］. Applied Laser, 2015, 35(2): 216-220.
［7］张振久, 胡泓. 基于激光跟踪仪的转台系统几何误差检测［J］. 中国激光, 2012, 39(11): 180-186.
Zhang Z J, Hu H, Measurement of geometric error of rotary stage system based on laser tracker［J］. Chinese Journal of Lasers, 2012, 39(11): 180-186.
［8］杨凡. 激光跟踪仪测距精度评定方法研究［J］. 测绘通报, 2013, (S1): 273-275.
Yang F. Laser tracker ranging accuracy evaluation onmethod research［J］. Bulletin of Surveying and Mapping, 2013, (S1): 273-275.
［9］何庆稀, 游震洲, 孔向东. 一种基于位姿反馈的工业机器人定位补偿方法［J］. 中国机械工程, 2016, 27(7): 872-876.
He Q X, You Z Z, Kong X D. An industrial robot positioning compensation method based on pose feedback ［J］. China Mechanical Engineering, 2016, 27(7): 872-876.

［10］张俐,江春. 基于纵向对称面重合的机身位姿求解方法［J］. 计量学报, 2017,38(4):385-390.
Zhang L,Jiang C. A New Method of Aircraft Position and Pose Based on the Coincidence of the Symmetry Plane［J］. Acta Metrologica Sinica,2017,38(4):385-390.
［11］任瑜,张丰,郭志敏,等. 一种通用的工业机器人位姿检测方法［J］. 计量学报,2018,39(5):615-621.
Ren Y,Zhang F,Guo Z M,et al. A general pose testing method of industrial robot［J］.Acta Metrologica Sinica,2018,39(5):615-621.
［12］何晓群. 应用回归分析［M］. 北京: 中国人民大学出版社, 2011.
［13］朱尚伟, 李景华. 岭回归估计的向量参数方法［J］. 应用概率统计, 2018, 34(5): 501-514.
Zhu S W, Li J H. Vector parameter method for ridge regression estimation［J］. Applied Probability Statics, 2008, 34(5): 501-514.
［14］Hoerl A E, Kennard R W. Ridge Regression: Biased Estimation for Nonorthogonal Problems［J］. Technometrics,1970,12(1):55-67.