Journal of Mechanical Science and Technology

, Volume 32, Issue 2, pp 805–810 | Cite as

Hybrid IPSO-automata algorithm for path planning of micro-nanoparticles through random environmental obstacles, based on AFM

  • M. H. Korayem
  • S. Nosoudi
  • S. Khazaei Far
  • A. K. Hoshiar
Article
  • 1 Downloads

Abstract

Nanomanipulation plays a significant role in nanotechnology research. The process of Atomic force microscopy (AFM) based manipulation is complex and time-consuming, which can be improved using a path-planning algorithm to reduce its manipulation time and time complexity. Due to real-time monitoring limitation in AFM based manipulations, Virtual reality (VR) environments have been developed. One such developed VR environment, however, is limited to point to point manipulation and lacks any path information. Therefore, we propose using a hybrid Improved particle swarm optimization (IPSO), a cellular automata-based algorithm for path planning during manipulation of micro/nanoparticles. In this technique, the critical time-force diagram, representing the AFM based manipulation dynamic is considered as a constraint, and is subsequently used to find the best path. The main path is divided into several segments and is optimized. Used as an algorithm for manipulation, this technique provides a more precise path in the AFM-based manipulation. Finally, the ability of this technique was compared to the other path planner algorithms based on its efficiency in reducing time-complexity parameters.

Keywords

Atomic force microscopy Nanoparticles Path planning Manipulation Improved particle swarm optimization (IPSO) Automata 

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References

  1. [1]
    A. K. Hoshiar and H. Raeisi Fard, A study of the nonlinear primary resonances of a micro-system under electrostatic and piezoelectric excitations, Proceedings of the Institution of Mechanical Engineers, Part C: J. of Mechanical Engineering Science, 229 (10) (2015) 1904–1917.Google Scholar
  2. [2]
    H. Raeisifard, M. N. Bahrami, A. Yousefi-Koma and H. R. Fard, Static characterization and pull-in voltage of a microswitch under both electrostatic and piezoelectric excitations, European J. of Mechanics-A/Solids, 44 (2014) 116–124.MathSciNetCrossRefGoogle Scholar
  3. [3]
    H. Raeisifard, M. Zamanian, M. N. Bahrami, A. Yousefi-Koma and H. R. Fard, On the nonlinear primary resonances of a piezoelectric laminated micro system under electrostatic control voltage, J. of Sound and Vibration, 333 (21) (2014) 5494–5510.CrossRefGoogle Scholar
  4. [4]
    A. K. Hoshiar, T. A. Le, F. U. Amin, M. O. Kim and J. Yoon, Studies of aggregated nanoparticles steering during magnetic-guided drug delivery in the blood vessels, J. of Magnetism and Magnetic Materials, 427 (2017) 181–187.CrossRefGoogle Scholar
  5. [5]
    M. Santhanakumar, R. Adalarasan and M Rajmohan, Parameter design for cut surface characteristics in abrasive waterjet cutting of Al/SiC/Al2O3 composite using grey theory based RSM, J. of Mechanical Science and Technology, 30 (1) (2016) 371–379.CrossRefGoogle Scholar
  6. [6]
    S. Ramesh, B. Bhuvaneswari, G. S. Palani, D. M. Lal and N. R. Iyer, Effects on corrosion resistance of rebar subjected to deep cryogenic treatment, J. of Mechanical Science and Technology, 31 (1) (2017) 123–132.CrossRefGoogle Scholar
  7. [7]
    M. H. Korayem and A. K. Hoshiar, 3D kinematics of cylindrical nanoparticle manipulation by an atomic force microscope based nanorobot, Scientia Iranica, 21 (6) (2014) 1907–1919.Google Scholar
  8. [8]
    M. H. Korayem and A. K. Hoshiar, Dynamic 3D modeling and simulation of nanoparticles manipulation using an AFM nanorobot, Robotica, 32 (4) (2014) 625–641.CrossRefGoogle Scholar
  9. [9]
    A. H. Korayem, A. K. Hoshiar and M. H. Korayem, Modeling and simulation of critical forces in the manipulation of cylindrical nanoparticles, International J. of Advanced Manufacturing Technology, 79 (9–12) (2015) 1505–1517.CrossRefGoogle Scholar
  10. [10]
    A. Varol, I. Gunev and C. Basdogan, A virtual reality toolkit for path planning and manipulation at nano-scale, 14th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, IEEE, March (2006) 485–489.CrossRefGoogle Scholar
  11. [11]
    M. Ammi and A. Ferreira, Path planning of an AFM-based nanomanipulator using virtual force reflection, International Conference on Intelligent Robots and Systems, IEEE/RSJ, 1 (2004) 577–582.Google Scholar
  12. [12]
    Z. Gao and A. Lécuyer, Path-planning and manipulation of nanotubes using visual and haptic guidance, VECIMS, May (2009) 1–5.Google Scholar
  13. [13]
    K. Charalampous et al., Autonomous robot path planning techniques using cellular automata, Robots and Lattice Automata, Springer International Publishing (2015) 175–196.Google Scholar
  14. [14]
    Y. Naranjani and J. Q. Sun, A multi-objective path planning algorithm for mobile robots based on cellular automata, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, ASME (2015) 175–196.Google Scholar
  15. [15]
    J. Santoso, B. Riyanto and W. Adiprawita, Dynamic path planning for mobile robots with cellular learning automata, J. of ICT Research and Applications (2016) 1–14.Google Scholar
  16. [16]
    C. Calvo, J. A. Villacorta-Atienza, V. I. Mironov, V. Gallego and V. A. Makarov, Waves in isotropic totalistic cellular automata: Application to real-time robot navigation, Advances in Complex Systems, Aug., 19 (04n05) (2016) 1650012.MathSciNetCrossRefGoogle Scholar
  17. [17]
    N. Yu and C. Ma, Mobile robot map building based on cellular automata, Third Pacific-Asia Conference on Circuits, Communications and System (PACCS), IEEE, July (2011) 1–4.Google Scholar
  18. [18]
    A. K. Hoshiar, M. Kianpour, M. Nazarahari and M. H. Korayem, Path planning in the AFM nanomanipulation of multiple spherical nanoparticles by using a coevolutionary Genetic algorithm, Manipulation, Automation and Robotics at Small Scales (MARSS), International Conference, July (2016) 1–6.Google Scholar
  19. [19]
    M. H. Korayem, A. K. Hoshiar and M. Nazarahari, A hybrid co-evolutionary genetic algorithm for multiple nanoparticle assembly task path planning, International J. of Advanced Manufacturing Technology (2016) 3527–3543.Google Scholar
  20. [20]
    A. K. Hoshiar and H. Raeisi Fard, A simulation algorithm for path planning of biological nanoparticles displacement on a rough path, J. of Nanoscience and Nanotechnology, 17 (8) (2017) 5578–558.CrossRefGoogle Scholar
  21. [21]
    S. Chowdhury, W. Jing, P. Jaron and D. J. Cappelleri, Path planning and control for autonomous navigation of single and multiple magnetic mobile microrobots, ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, August (2015) V004T09A040-V004T09A040.Google Scholar
  22. [22]
    G. B. Ferreira, P. A. Vargas and G. M. Oliveira, An improved cellular automata-based model for robot pathplanning, Conference Towards Autonomous Robotic Systems, Springer International Publishing, September (2014) 25–36.Google Scholar
  23. [23]
    A. Tafazzoli and M. Sitti, Dynamic behavior and simulation of nanoparticle sliding during nanoprobe-based positioning, Methods (2004) 19, 32.Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • M. H. Korayem
    • 1
  • S. Nosoudi
    • 2
  • S. Khazaei Far
    • 2
  • A. K. Hoshiar
    • 3
  1. 1.Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical EngineeringIran University of Science and TechnologyNarmak, TehranIran
  2. 2.Mechatronic Engineering DepartmentIslamic Azad University, Science and Research BranchTehranIran
  3. 3.Faculty of Industrial and Mechanical EngineeringIslamic Azad University, Qazvin BranchQazvinIran

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