Calibration and decoupling of multi-axis robotic Force/Moment sensors

doi:10.1016/j.rcim.2017.08.008

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院合肥智能机械研究所

	Calibration and decoupling of multi-axis robotic Force/Moment sensors
	Liang, Qiaokang 1,2; Wu, Wanneng 1; Coppola, Gianmarc 3; Zhang, Dan 4; Sun, Wei 1,2; Ge, Yunjian 5; Wang, Yaonan 1,2
刊名	ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
	2018-02-01
卷号	49 期号:无页码:301-308
关键词	Robotic Sensory System Multi-axis Force/moment Sensors Calibration And Decoupling Extreme Learning Machine
DOI	10.1016/j.rcim.2017.08.008
文献子类	Article
英文摘要	force (Fx, Fy, and Fz), as well as the moments (Mx, My and Mz). This enables them to be frequently used in many robotic applications. Accurate, time-effective calibration and decoupling procedures are critical to the implementation of these sensors. This paper compares the effectiveness of decoupling methods based on Least-Squares (LS), BP Neural Network (BPNN), and Extreme Learning Machine (ELM) methods for improving the performance of multi-axis robotic F/M sensors. In order to demonstrate the effectiveness of the decoupling methods, a calibration and decoupling experiment was performed on a five-axis robotic F/M sensor. The experiments demonstrate that the ELM based decoupling method is superior to LS and BPNN based methods. The presented theoretical and experimental demonstrations provide a comprehensive description of the calibration and decoupling procedures of multi -axis robotic F/M sensors. This work reveals that the ELM method is an appropriate and high performing decoupling procedure for multi -axis robotic F/M sensors. (C) 2017 Elsevier Ltd. All rights reserved.
WOS关键词	6-AXIS FORCE/TORQUE SENSOR ; FORCE SENSOR ; TOUCH ; TRANSDUCER ; MACHINE ; DESIGN
WOS研究方向	Computer Science ; Engineering ; Robotics
语种	英语
WOS记录号	WOS:000412957500027
资助机构	National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; National Nature Science Foundation of China(NSFC 61673163) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045) ; Hunan provincial natural science foundation of China(2016JJ3045)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/33785]
专题	合肥物质科学研究院_中科院合肥智能机械研究所
作者单位	1.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China 2.Hunan Univ, Natl Engn Lab Robot Vision Percept & Control, Changsha 410082, Hunan, Peoples R China 3.Univ Ontario, Inst Technol, Fac Engn & Appl Sci, Oshawa, ON L1H 7K4, Canada 4.York Univ, Dept Mech Engn, Toronto, ON M3J 1P3, Canada 5.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Liang, Qiaokang,Wu, Wanneng,Coppola, Gianmarc,et al. Calibration and decoupling of multi-axis robotic Force/Moment sensors[J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,2018,49(无):301-308.
APA	Liang, Qiaokang.,Wu, Wanneng.,Coppola, Gianmarc.,Zhang, Dan.,Sun, Wei.,...&Wang, Yaonan.(2018).Calibration and decoupling of multi-axis robotic Force/Moment sensors.ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,49(无),301-308.
MLA	Liang, Qiaokang,et al."Calibration and decoupling of multi-axis robotic Force/Moment sensors".ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 49.无(2018):301-308.