Yuan / Liu / Wang AFM-Based Observation and Robotic Nano-manipulation

1. Auflage 2020
ISBN: 978-981-15-0508-9
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 189 Seiten

This book highlights the latest advances in AFM nano-manipulation research in the field of nanotechnology. There are numerous uncertainties in the AFM nano-manipulation environment, such as thermal drift, tip broadening effect, tip positioning errors and manipulation instability. This book proposes a method for estimating tip morphology using a blind modeling algorithm, which is the basis of the analysis of the influence of thermal drift on AFM scanning images, and also explains how the scanning image of AFM is reconstructed with better accuracy. Further, the book describes how the tip positioning errors caused by thermal drift and system nonlinearity can be corrected using the proposed landmark observation method, and also explores the tip path planning method in a complex environment. Lastly, it presents an AFM-based nano-manipulation platform to illustrate the effectiveness of the proposed method using theoretical research, such as tip positioning and virtual nano-hand.

Shuai Yuan, Ph.D., graduated from Shenyang Institute of Automation, Chinese Academy of Sciences, and is currently an Associate Professor at Shenyang Jianzhu University. He has presided over a number of projects, such as the National Natural Youth Science Foundation of China, National Post-doctoral Special Foundation of China, National Post-doctoral General Foundation of China, Natural Science Foundation of Liaoning Province, and Liaoning College and University basic Scientific Research Fund. He has also participated in the National High Technology Research and Development Program of China, the key project of the National Natural Science Foundation of China, and multiple projects of provincial or municipal Natural Science Funds. Prof. Yuan has published more than 50 papers in national and international scientific journals and conferences, and a textbook. He has served as a chairman for international conferences including IEEE-CYBER (2016), IEEE-Nanomedicine (2016), IEEE-CYBER (2017), and IEEE-WRC (2018), and also a reviewer for conferences and international journals such as IEEE-ROBIO and IEEE-CYBER. At present, he is investigating micro/nano manipulation, image processing and pattern recognition, and robot navigation and control.
Lianqing Liu received the B.S. degree in industry automation from Zhengzhou University, Zhengzhou, China in 2002, and the Ph.D. degree from the Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China in 2009.
He is currently serving as a Professor for the Shenyang Institute of Automation, Chinese Academy of Sciences. His current research interests include nanorobotics, intelligent control, and biosensors. Dr. Liu was awarded the Early Government/Industrial Career Award by the IEEE Robotics and Automation Society in 2011 etc.
Zhidong Wang received the B.S. degree from the Beijing University of Aeronautics and Astronautics, Beijing, China in 1987, and the M.Sc. and Ph.D. degrees in engineering from the Graduate School of Engineering, Tohoku University, Sendai, Japan in 1992 and 1995, respectively.He is currently a Professor with the Department of Advance Robotics, Chiba Institute of Technology, Chiba, Japan. His current research interests include human-robot interaction and cooperation systems, distributed autonomous robot systems, micro/nano robotics, and application of intelligent robot technologies for the disabled.
Ning Xi received the D.Sc. degree in systems science and mathematics from Washington University in St. Louis, St. Louis, MO, USA in 1993, and the B.S. degree in electrical engineering from the Beijing University of Aeronautics and Astronautics, Beijing, China.Currently, he is the Chair Professor of Robotics and Automation in the Department of Industrial and Manufacturing System, and the Director of Emerging Technologies Institute of the University of Hong Kong. Before joining the University of Hong Kong, he was the University Distinguished Professor, John D. Ryder Professor of Electrical and Computer Engineering and Director of Robotics and Automation Laboratory at Michigan State University in U.S. He also served as the founding head of the Department of Mechanical and Biomedical Engineering at City University of Hong Kong (2011-2013). His research interests include robotics, manufacturing automation, micro/nano manufacturing, nano sensors and devices, and intelligent control and systems. 

Autoren/Hrsg.

Yuan, Shuai

Liu, Lianqing

Wang, Zhidong

Xi, Ning

Inhaltsverzeichnis

1;Preface;5
2;Acknowledgements;7
3;Contents;8
4;1 Introduction;12
4.1;Abstract;12
4.2;1.1 Introduction of Nanotechnology;12
4.2.1;1.1.1 Development and Application of Nanotechnology;13
4.2.2;1.1.2 Characteristics of Nanotechnology;18
4.2.3;1.1.3 The Key Nanotechnology: Nano-observation and Manipulation;19
4.3;1.2 Primary Nano-observation Methods;20
4.3.1;1.2.1 Optical Microscopic Observation;20
4.3.2;1.2.2 SEM/TEM Based Observation;20
4.3.3;1.2.3 STM Based Observation;22
4.3.4;1.2.4 AFM Based Observation;23
4.4;1.3 Primary Nano-manipulation Methods;28
4.4.1;1.3.1 Self-assembly Based Nano-manipulation;28
4.4.2;1.3.2 Optical Tweezer Based Nano-manipulation;28
4.4.3;1.3.3 DEP Based Nano-manipulation;30
4.4.4;1.3.4 SEM Based Nano-manipulation;31
4.4.5;1.3.5 AFM Based Nano-manipulation;32
4.5;1.4 Application Characteristics and Problems of AFM Based Nano-manipulation;34
4.6;References;37
5;2 AFM Based Robotic Nano-manipulation;43
5.1;Abstract;43
5.2;2.1 AFM Introduction;43
5.2.1;2.1.1 Analysis of AFM Atomic Force-Distance Curve;44
5.2.2;2.1.2 Three Work Modes of AFM;45
5.3;2.2 AFM Based Robotic Nano-manipulation;46
5.3.1;2.2.1 Static Image Based Offline Nano-manipulation;46
5.3.2;2.2.2 Augmented Reality Based Robotic Nano-manipulation;48
5.3.3;2.2.3 Local Scan Based Nano-manipulation Using Landmark Observation;50
5.4;2.3 Stochastic Approach for AFM Based Robotic Nano-manipulation;52
5.4.1;2.3.1 Precision Analysis of AFM Tip Driver;53
5.4.2;2.3.2 Real-Time Tip Localization Analysis in Task Space;53
5.4.3;2.3.3 AFM Based Nano-manipulation Using Virtual Nano-hand;55
5.5;References;56
6;3 AFM Image Reconstruction Using Compensation Model of Thermal Drift;58
6.1;Abstract;58
6.2;3.1 Reconstruction Theory of AFM Thermal-Drift Image;58
6.2.1;3.1.1 Newton Iteration Method;59
6.2.2;3.1.2 Image Interpolation Method;60
6.2.2.1;3.1.2.1 Application of Image Interpolation;61
6.2.2.2;3.1.2.2 Nearest Neighbor Interpolation Method;64
6.2.2.3;3.1.2.3 Bilinear Interpolation Method;64
6.2.2.4;3.1.2.4 Newton Interpolation Method;65
6.2.2.5;3.1.2.5 Bi-cubic and B-spline Interpolation;66
6.2.3;3.1.3 Thermal Drift Correction Method for Scanning Image;68
6.3;3.2 Reconstruction Method for Thermal Drift Image;70
6.3.1;3.2.1 Compensation Model for Thermal Drift;70
6.3.2;3.2.2 Thermal Drift Offset Vector;72
6.3.2.1;3.2.2.1 First-Order Offset Vector;73
6.3.2.2;3.2.2.2 Second-Order Offset Vector;74
6.3.3;3.2.3 Offset Vector Calculation;75
6.3.3.1;3.2.3.1 Calculation of Offset Vectors in Characteristic Regions;76
6.3.3.2;3.2.3.2 Calculation of Offset Vectors in Non-characteristic Regions;77
6.3.4;3.2.4 Integral Image Reconstruction;79
6.4;3.3 Simulation and Experimental Analysis;81
6.4.1;3.3.1 Simulation and Analysis of Thermal Drift Image;81
6.4.2;3.3.2 Experiment and Analysis of Reconstruction of Thermal Drift Image;82
6.4.2.1;3.3.2.1 Morphological Changes of Nanoparticles;83
6.4.2.2;3.3.2.2 Verification of Thermal Drift Velocity;87
6.4.2.3;3.3.2.3 Experimental Results of Global Reconstruction;88
6.5;References;90
7;4 AFM Image Reconstruction Algorithm Based on Tip Model;91
7.1;Abstract;91
7.2;4.1 Theoretical Basis of AFM Tip Blind Modeling Reconstruction;91
7.2.1;4.1.1 Basic Concepts of Mathematical Morphology;91
7.2.2;4.1.2 Mathematical Description of Tip Imaging Process;94
7.2.3;4.1.3 Tip Morphology Estimation Algorithm;96
7.3;4.2 A Method for Improving the Speed of Tip Modeling Calculation;100
7.3.1;4.2.1 Pre-estimation of Tip Morphology;100
7.3.2;4.2.2 Improvement of Algorithm Core;103
7.4;4.3 Method for Improving the Accuracy of Tip Modeling;107
7.4.1;4.3.1 Definition of Denoising Threshold;107
7.4.2;4.3.2 Estimation of Denoising Threshold;109
7.5;4.4 The Experiment of AFM Image Reconstruction;110
7.5.1;4.4.1 Tip Topography Estimation;110
7.5.2;4.4.2 Scanning Image Reconstruction of Carbon Nano-tubes and Nano-particles;112
7.6;References;114
8;5 Stochastic Approach Based AFM Tip Localization;115
8.1;Abstract;115
8.2;5.1 Research of AFM Tip Localization;115
8.2.1;5.1.1 Stochastic Approach Based AFM Tip Localization Strategy;115
8.2.2;5.1.2 Nano-manipulation Coordinate System Defined on AFM;119
8.3;5.2 Analysis of Landmark Observation Model Based on Kalman Filter;121
8.3.1;5.2.1 Landmark Definition;121
8.3.2;5.2.2 Analysis of Landmark Observation;122
8.3.3;5.2.3 Analysis of Horizontal Observation of Landmark;123
8.3.4;5.2.4 Optimal Estimation of Tip Position Based on Kalman Filter;125
8.4;5.3 Establishment of Tip Motion Model;129
8.4.1;5.3.1 PI Based Motion Model;129
8.4.2;5.3.2 Creep Model of PZT;130
8.4.3;5.3.3 System Thermal Drift Model;131
8.5;5.4 Simulation Experiment of Tip Localization Based on Landmark Observation;133
8.6;References;135
9;6 Path Planning of Nano-Robot Using Probability Distribution Region;136
9.1;Abstract;136
9.2;6.1 Path Planning for Landmark Observation Using Probability Distribution Region;137
9.3;6.2 Tip Path Planning in Task Space;143
9.3.1;6.2.1 Basic Path Planning in Single Landmark Environment;143
9.3.2;6.2.2 Path Planning in Multi-landmark Environment;148
9.4;6.3 Simulation and Experimental Verification;150
9.4.1;6.3.1 Path Planning Based on Dijkstra Method;150
9.4.2;6.3.2 Path Planning Based on Ant Colony Algorithm;151
9.5;6.4 Landmark Dynamic Configuration;155
9.5.1;6.4.1 Definition of Landmark Domain;157
9.5.2;6.4.2 Virtual Nano-hand Method;159
9.5.3;6.4.3 Nano-manipulation Simulation Based on Virtual Nano-hand;160
9.6;References;162
10;7 AFM-Based Nano-manipulation Platform;163
10.1;Abstract;163
10.2;7.1 Hardware and Software Implementation of System;163
10.2.1;7.1.1 Hardware Platform;164
10.2.2;7.1.2 Software Implementation;165
10.3;7.2 AFM Tip Localization;167
10.3.1;7.2.1 Framework of Tip Localization System;167
10.3.2;7.2.2 Model Parameters Calibration and Experimental Verification;168
10.3.3;7.2.3 Accuracy Improvement of Tip Localization;180
10.4;7.3 AFM Nano-manipulation;184
10.4.1;7.3.1 Virtual Nano-hand Nano-manipulation;184
10.4.2;7.3.2 Demonstration of AFM Nano-manipulation;186
11;Index;188