Data‐Driven Navigation of Ferromagnetic Soft Continuum Robots Based on Machine Learning

Abstract

Ferromagnetic soft continuum robots (FSCRs) have great potential in biomedical applications due to their miniaturization and remote control capabilities. However, to direct the FSCR accurately and effectively, it is critical to realize inverse kinematics control in navigation, which is difficult for existing mechanical models. Herein, with the path segmentation strategy, an automatic method to navigate the FSCR in different paths based on machine learning is developed. A data‐driven artificial neural network (ANN) model to guide the steering of the magnetically responsive tip is presented. Using parametric simulations as the training data, the ANN model shows good generalization performance to predict control parameters. Moreover, the basic framework of the learning model remains effective when the FSCR materials change, which shows high scalability and is important for adapting to various environments. The study presents a promising strategy for guiding FSCRs in the narrow and tortuous vasculature, which is essential for many biomedical operations.

Keywords

• machine learning
• navigation
• segmented control
• soft continuum robots