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Path planning in three dimensional live environment with randomly moving obstacles for viscoelastic bio-particle.

Moharam Habibnejad Korayem1, Zahra Rastegar1

  • 1Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.

Microscopy Research and Technique
|May 11, 2021
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Summary

This study optimizes atomic force microscopy (AFM) path planning in complex environments. The research minimizes tool error and applied force for accurate biological applications, considering various constraints.

Keywords:
genetic algorithmmaximum applied forcepath planningrandom obstaclesthree dimensional live environmenttool accuracyviscoelastic biological particle

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Area of Science:

  • Biophysics
  • Nanotechnology
  • Robotics

Background:

  • Atomic Force Microscopy (AFM) offers versatile capabilities for various environments and scales.
  • Accurate tool performance is critical for success in biological applications.

Purpose of the Study:

  • To provide an optimized path for AFM operation in a live environment with random fixed and moving obstacles.
  • To minimize a cost function combining tool error, maximum applied force, and particle deformation.

Main Methods:

  • Path planning for a viscoelastic particle considering constraints like critical force, time, and maximum applied force.
  • Incorporation of fixed and moving obstacles with random profiles and distributions.
  • Comparison of routing results with previous works to validate path correctness.

Main Results:

  • The optimized path was successfully determined for a viscoelastic particle in a simulated live environment.
  • Path planning time varied based on constraints: 117.657s (critical force/time), 118.240s (max applied force), and 120.540s (all constraints).
  • The applied force constraint was found to be more influential than others, increasing path planning time.

Conclusions:

  • The developed path optimization method is effective for AFM operations in complex, obstacle-filled environments.
  • Constraint analysis reveals the significant impact of applied force on path planning efficiency.
  • The study validates the accuracy and reliability of AFM path planning in simulated biological settings.