Nanorobotics pp 115-135 | Cite as

Virtual Tooling for Nanoassembly and Nanomanipulations

  • Zhidong Wang
  • Lianqing Liu
  • Jing Huo
  • Zhiyu Wang
  • Ning Xi
  • Zaili Dong


Atomic force microscopy (AFM) (Binning et al., Phys Rev Lett 56:930–933, 1986) has been used as a nanomanipulation tool recently because it not only has high resolution scanning ability but also can be controlled as an end-effector in the nanoenvironment (Junno et al., Appl Phys Lett 66:3627–3629, 1995). There are several challenging problems including controller design with relatively large thermal drift and other uncertainties, real-time positioning and manipulation control with sensor feedback, and nanosensing and manipulation planning. In the last decade, many researchers are working on these problems and some methods have been proposed that solved these problems partially (Chen et al., IEEE Trans Autom Sci Eng 3:208–217, 2006; Li et al., IEEE Trans Nanotechnol 4:605–614, 2005; Resch et al., Langmuir 14:6613–6616, 1998; Hansen et al., Nanotechnology 9:337–342, 1998; Sitti, IEEE ASME Trans Mechatron 9:343–348, 2004). However, the problem caused by single tip interaction is still hindering its efficiency especially in handling nanoparticles/nano-objects to form patterns or nanostructures.

Tools are usually designed and used for object handling or other tasks especially for having higher efficiency and accuracy or coping with various uncertainties on task performing. We proposed a concept of virtual nanohand which mimicking multi-fingered hand and controlling an AFM tip to form a virtual tool for achieving stable nano manipulation and nanoassembly. The nanohand strategy is implemented by moving the AFM tip to a set of predefined trajectories in relative high frequency. It allows us easily to design and apply various virtual tools for coping with requirements in nanomanipulation. This virtual tooling strategy is a solution with good potential on realizing high efficiency nanoassembly and nanomanipulation.


Atomic Force Microscopy Position Error Viscous Friction Particle Center Instant Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Maier SA, Kik PG, Atwater HA, Meltzer S, Harel E, Koel BE, Requicha AA (2003) Local detection of electromagnetic energy transport below the diffraction limit in metal nanoparticle plasmon waveguides. Nat Mater 2:229–232CrossRefGoogle Scholar
  2. 2.
    Tong L, Zhu T, Liu Z (2008) Atomic force microscope manipulation of gold nanoparticles for controlled Raman enhancement. Appl Phys Lett 92:023109CrossRefGoogle Scholar
  3. 3.
    Makaliwe JH, Requicha AA (2001) Automatic planning of nanoparticle assembly tasks. In: Proceedings of the 2001 IEEE international symposium on assembly and task planning (ISATP2001), pp 288–293Google Scholar
  4. 4.
    Li GY, Xi N, Yu MM, Fung WK (2004) Development of augmented reality system for AFM-based nanomanipulation. IEEE ASME Trans Mechatron 9:358–363CrossRefGoogle Scholar
  5. 5.
    Chen HP, Xi N, Li GY (2006) CAD-guided automated nanoassembly using atomic force microscopy-based nonrobotics. IEEE Trans Autom Sci Eng 3:208–217CrossRefGoogle Scholar
  6. 6.
    Zhang JB, Xi N, Li GY, Chan HY, Wejinya UC (2006) Adaptable end effector for atomic force microscopy based nanomanipulation. IEEE Trans Nanotechnol 5:628–642CrossRefGoogle Scholar
  7. 7.
    Liu LQ, Luo YL, Xi N, Wang YC, Zhang JB, Li GY (2008) Sensor referenced real-time videolization of atomis force microscopy for nanomanipulations. IEEE ASME Trans Mechatron 13:76–85CrossRefGoogle Scholar
  8. 8.
    Wang ZD, Hirata Y, Kosuge K (2003) Control multiple mobile robots for object caging and manipulation. In: Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems, pp 1751–1756Google Scholar
  9. 9.
    Hou J, Wang ZD, Liu LQ, Yang YL, Dong ZL, Wu CD (2010) Modeling and analyzing nano-particle pushing with an AFM by using nano-hand strategy. In: Proceedings of the 2010 5th IEEE international conference on nano/micro engineered and molecular systems NEMS, pp 518–523.Google Scholar
  10. 10.
    Hou J, Wu CD, Liu LQ, Wang ZD, Dong ZL (2010) Modeling and analyzing nano-rod pushing with an AFM. In: Proceedings of the 10th IEEE conference on nanotechnology IEEE-NANO 2010, pp 329–334Google Scholar
  11. 11.
    Kim S, Ratchford DC, Li XQ (2009) Atomic force microscope nanomanipulation with simultaneous visual guidance. ACS Nano 3:2989–2994CrossRefGoogle Scholar
  12. 12.
    Resch R, Baur C, Bugacov A, Koel BE, Madhukar A, Requicha AA, Will P (1998) Building and manipulating three-dimensional and linked two-dimensional structures of nanoparticles using scanning forcemicroscopy. Langmuir 14:6613–6616CrossRefGoogle Scholar
  13. 13.
    Tafazzoli A, Sitti M (2004) Dynamic behavior and simulation of nanoparticle sliding during nanoprobe-based positioning. In: Proceedings of the IMECE’04 2004 ASME international mechanical engineering congress, pp 1–8Google Scholar
  14. 14.
    Li GY, Xi N, Chen HP, Pomeroy C, Prokos M (2005) Videolized atomic force microscopy for interactive nanomanipulation and nanoassembly. IEEE Trans Nanotechnol 4:605–614CrossRefGoogle Scholar
  15. 15.
    Binning G, Quate CF, Gerber C (1986) Atomic force microscope. Phys Rev Lett 56:930–933CrossRefGoogle Scholar
  16. 16.
    Junno T, Deppert K, Montelius L, Samuelson L (1995) Controlled manipulation of nanoparticles with an atomic force microscope. Appl Phys Lett 66:3627–3629CrossRefGoogle Scholar
  17. 17.
    Hansen LT, Kuhle A, Sorensen AH, Bohr J, Lindelof PE (1998) A technique for positioning nanoparticles using an atomic force microscope. Nanotechnology 9:337–342CrossRefGoogle Scholar
  18. 18.
    Sitti M (2004) Atomic force microscope probe based contrlled pushing for nanotribological characterization. IEEE ASME Trans Mechatron 9:343–348CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Zhidong Wang
    • 1
  • Lianqing Liu
    • 2
  • Jing Huo
    • 3
  • Zhiyu Wang
    • 2
  • Ning Xi
    • 4
  • Zaili Dong
    • 2
  1. 1.Department of Advanced RoboticsChiba Institute of TechnologyNarashinoJapan
  2. 2.State Key Laboratory of RoboticsShenyang Institute of Automation, CASShenyangChina
  3. 3.College of Information Science and EngineeringNortheastern UniversityShenyangChina
  4. 4.Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

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