Dynamic Trajectory Planning for Atomic Force Microscopy Nanopositioning: An Enhanced A-Star Framework Addressing...

Area of Science:

• Nanotechnology
• Surface Science
• Materials Science

Background:

• Atomic Force Microscopy (AFM) is crucial for nanoscale surface characterization and mechanical property measurement.
• Dynamic displacement deviation limits AFM's precision in nanoscale positioning, impacting mechanical property accuracy.
• Accurate localization of surface target points is essential for reliable AFM measurements.

Purpose of the Study:

• To address the limitations of dynamic displacement deviation in AFM nanoscale positioning.
• To propose an integrated enhanced A* framework for contour-aware motion trajectory planning.
• To ensure nanometer-level target localization accuracy on complex biological cell surfaces.

Main Methods:

• Developed an enhanced A* framework for contour-aware motion trajectory planning.
• Utilized AFM tip repositioning with prior topographic data for path planning.
• Evaluated Manhattan, Chebyshev, and Euclidean heuristic metrics in AFM grid modeling.

Main Results:

• The Manhattan heuristic achieved 96% ± 4% accuracy, significantly outperforming Euclidean (70% ± 4%) and Chebyshev (56% ± 8%) methods (p < 0.001).
• Reduced target localization errors by 30% in constrained environments by mitigating path cost overestimation.
• Resolved the trade-off between path smoothness (CV = 0.28) and positioning precision via adaptive cost-weighting.

Conclusions:

• The proposed approach enables precise nanoscale positioning for ultramicroscopic topography and physical characteristic capture.
• Provides a robust framework for quantitative nanomechanical characterization of heterogeneous materials.
• Enhances AFM's capability for accurate surface mechanical property determination.