File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Stochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations

TitleStochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations
Authors
Issue Date2017
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8856
Citation
IEEE Transactions on Automation Science and Engineering, 2017, v. 14 n. 4, p. 1643-1654 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/262190
ISSN
2019 Impact Factor: 4.938
2015 SCImago Journal Rankings: 1.832
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYuan, S-
dc.contributor.authorWang, Z-
dc.contributor.authorLiu, L-
dc.contributor.authorXi, N-
dc.contributor.authorWang, Y-
dc.date.accessioned2018-09-28T04:54:51Z-
dc.date.available2018-09-28T04:54:51Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Automation Science and Engineering, 2017, v. 14 n. 4, p. 1643-1654-
dc.identifier.issn1545-5955-
dc.identifier.urihttp://hdl.handle.net/10722/262190-
dc.description.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.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8856-
dc.relation.ispartofIEEE Transactions on Automation Science and Engineering-
dc.rights©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleStochastic Approach for Feature-Based Tip Localization and Planning in Nanomanipulations-
dc.typeArticle-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.description.naturepostprint-
dc.identifier.doi10.1109/TASE.2017.2698003-
dc.identifier.hkuros292771-
dc.identifier.volume14-
dc.identifier.issue4-
dc.identifier.spage1643-
dc.identifier.epage1654-
dc.identifier.isiWOS:000412500600009-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats