NDLI: A probabilistic approach for on-line positioning in nano manipulations

Content Provider	IEEE Xplore Digital Library
Author	Shuai Yuan Lianqing Liu Zhidong Wang Ning Xi Yuechao Wang Zaili Dong Zhiyu Wang
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, China (Shuai Yuan; Lianqing Liu; Zhidong Wang; Ning Xi; Yuechao Wang; Zaili Dong; Zhiyu Wang)
Abstract	Nanomanipulation and nanoassembly using atom force microscopy (AFM) is a potential and promising technology for nanomanufacturing. Precise position of the tip of AFM is important to increase the accuracy and efficiency on fabricate complex nanostructures. However at the nano-scale, it is difficult to acquire the tip position expressed by the coordinate in real time due to PZT nonlinearity and thermal drift through the general measure. In this paper, a probabilistic approach incorporating a Kalman filter based localization algorithm is introduced into the on-line estimation of the tip position expressed by probability distribution known as probability density function. A probabilistic motion model of AFM tip is introduced that consists of a PZT dynamic model based on the Prandtl-Ishlinskii (PI) model, and motion error distribution obtained from calibration experiments. An observation model by using a local scanning algorithm is proposed and the change of uncertainty distribution on scanning landmarks, e.g. nano-particles, near the target position is analyzed. Some experiment results are included for showing the motion error distribution and a simulation result is presented to illustrate the validity of the proposed method.
Starting Page	450
Ending Page	455
File Size	952917
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5553794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Probabilistic logic Mathematical model Accuracy Uncertainty Equations Position measurement Probability AFM Kalman Filter Nanomanipulation PI
Content Type	Text
Resource Type	Article

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