Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation
The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM) algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time v...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2012/803912 |
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doaj-0af699a0dddf4aecb380c25de6aa9e122020-11-25T01:03:41ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472012-01-01201210.1155/2012/803912803912Fuzzy Control and Connected Region Marking Algorithm-Based SEM NanomanipulationDongjie Li0Weibin Rong1Lining Sun2Bo You3Yu Zou4Wanzhe Xiao5State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin 150080, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, ChinaThe interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM) algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE), and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments.http://dx.doi.org/10.1155/2012/803912 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dongjie Li Weibin Rong Lining Sun Bo You Yu Zou Wanzhe Xiao |
spellingShingle |
Dongjie Li Weibin Rong Lining Sun Bo You Yu Zou Wanzhe Xiao Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation Mathematical Problems in Engineering |
author_facet |
Dongjie Li Weibin Rong Lining Sun Bo You Yu Zou Wanzhe Xiao |
author_sort |
Dongjie Li |
title |
Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation |
title_short |
Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation |
title_full |
Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation |
title_fullStr |
Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation |
title_full_unstemmed |
Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation |
title_sort |
fuzzy control and connected region marking algorithm-based sem nanomanipulation |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2012-01-01 |
description |
The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM) algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE), and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments. |
url |
http://dx.doi.org/10.1155/2012/803912 |
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