Model-based Parameter Selection for a Steerable Continuum Robot - Applications to Bronchoalveolar Lavage (BAL).

Area of Science:

• Medical robotics
• Biomedical engineering
• Medical device design

Background:

• Bronchoalveolar lavage (BAL) is crucial for diagnosing lung conditions but faces navigation challenges in complex lung anatomy.
• Continuum robots offer potential for enhanced navigation in minimally invasive procedures like BAL.

Purpose of the Study:

• To develop a predictive model for the curvature of tendon-driven notched continuum robots.
• To create an algorithm for designing robots with desired nonuniform curvature for lung navigation.
• To incorporate friction and pre-curvature into shape prediction models for enhanced accuracy.

Main Methods:

• Modeling robot curvature based on arbitrary notch shapes and tendon tension.
• Developing an algorithm to optimize notch patterns for specific curvatures.
• Validating models by predicting robot deflection and shape with experimental data and a pulmonary phantom.

Main Results:

• The model accurately predicted the deflection of various notch shapes (2.32%-6.32% error).
• A simulated robot designed with the algorithm achieved target curvature with 1.52° RMSE.
• A refined model incorporating friction and pre-curvature predicted robot shape with 5.20° average RMSE.

Conclusions:

• The developed models and algorithms enable precise control over continuum robot shape for complex anatomical navigation.
• This technology has the potential to improve the efficacy and safety of procedures like bronchoalveolar lavage.
• Demonstrated navigation through a pulmonary phantom validates the practical applicability of the robotic system.