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Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations
Yuan S(袁帅); Wang ZD(王志东); Liu LQ(刘连庆); Xi N(席宁); Wang YC(王越超)
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
ISSN1545-5955
2017
Volume14Issue:4Pages:1643-1654
Indexed BySCI ; EI
EI Accession number20172203708656
WOS IDWOS:000412500600009
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China [61175103, 61305125] ; National Post Doctor Foundation [2013M530955, 2014T70265] ; Discipline Content Education Project [XKHY2-66] ; National 863 Project [2012AA020100] ; Chinese Academy of Sciences State Foreign Expert Bureau International Partnership Program for Creative Research Teams
KeywordAfm-based Nanomanipulation Afm Tip Localization Feature-based Localization Kalman Filter
AbstractIn atomic force microscopy (AFM)-based nanomanipulation, the tip position uncertainties still exist due to the parameter inaccuracies in the open-loop compensation of the piezo scanner, the noise in the closed-loop control and thermal drift. These spatial uncertainties are very challenging to be directly estimated owing to the lack of real-time feedback, and its effects are more significant in performing an automatic nanomanipulation/assembly task than macro world manipulations. In this paper, we propose a stochastic framework for feature-based localization and planning in nanomanipulations to cope with these uncertainties. In the proposed framework, some features in the sample surface are identified to calculate their positions in statistics, and detected by using the AFM tip as the sensor itself through a local scan-based motion. In the localization, the Kalman filter is used through incorporating the tip motion model and the local scan-based observation model to estimate the on-line tip position in the task space. The simulation and experiments about tip positioning are carried out to illustrate the validity and feasibility of the proposed algorithm. Then, positioning tip for effective nanomanipulation is presented by using several experiments. Finally, a carbon nanotube is followed to show that the proposed method can provide a great potential for improving the position accuracy. Note to Practitioners-Atomic force microscopy (AFM)-based nanomanipulation has become a promising approach in developing devices and structures at nanoscale. One of the prerequisites for the effective and successful nanomanipulation is that the AFM tip position relative to the interest region can be controlled accurately. This paper proposes a stochastic approach for feature-based localization and planning method to solve these problems. The uncertainties of the tip position are decreased by using the Kalman filter method in the localization procedure. Fifty times of experiments are represented to illustrate the effectiveness and efficiency of the tip positioning method. Then, basic nanomanipulations in the vertical direction without and with SAFLP method are performed for representing significance of positioning the tip. Furthermore, assembling nanostructures and following the carbon nanotube are carried out by using SAFLP. These experiments indicate that accurate positioning tip in AFM-based observation and manipulation can provide valid nanomanipulation in N/MEMS assembly, and the following nano-objects such as nanotube in real-time observation, which will promote automation implementation in nanomanipulation.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectAutomation & Control Systems
WOS KeywordATOMIC-FORCE MICROSCOPY ; DRIFT COMPENSATION ; MANIPULATION ; SYSTEM ; HYSTERESIS ; NANOSCALE ; TRACKING
WOS Research AreaAutomation & Control Systems
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21104
Collection机器人学研究室
Corresponding AuthorWang ZD(王志东)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, 110168, China
3.Graduate School of Chinese Academy of Sciences, Beijing, 100001, China
4.Department of Advanced Robotics, Chiba Institute of Technology, Chiba, 275-0016, Japan
5.Department of Industrial and Manufacturing Systems Engineering, Emerging Technologies Institute, Faculty of Engineering, University of Hong Kong, Hong Kong, Hong Kong
Recommended Citation
GB/T 7714
Yuan S,Wang ZD,Liu LQ,et al. Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2017,14(4):1643-1654.
APA Yuan S,Wang ZD,Liu LQ,Xi N,&Wang YC.(2017).Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,14(4),1643-1654.
MLA Yuan S,et al."Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 14.4(2017):1643-1654.
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