Computational Predictive Model for Full Body Controlled Soft Continuum Magnetic Robots Under Hybrid Actuation

EasyChair Preprint 10289

Abstract

This paper presents a computational model for predicting the shape of fully soft continuum magnetic robots (FSCMRs) under different actuation scenarios, including permanent magnets (PM) and electromagnetic fields (EM). The accuracy of the model is validated using experimental data, and the results show that the model has acceptable accuracy in predicting the shapes and tip positions of FSCMRs under hybrid actuation systems. The study demonstrates the potential of the model as a predictive tool for guiding soft magnetic robots in more versatile clinical applications. The experimental setup includes an external magnetic field generator, two robotic arms, a camera, and a computer for monitoring. The FSCMRs have a diameter of 600 µm and a length of 25 mm, and the test bed and FSCMR holder are 3D-printed. The simulation is performed using COMSOL Multiphysics software, and the mechanical properties are introduced from previous work. The simulated magnetic fields match three actuation scenarios: one PM, two PMs, and a hybrid system consisting of one PM and the EM system. The suitability and accuracy of the proposed numerical platform are assessed based on the shape classification of the FSCMR and the tip position. The results show that the platform has acceptable accuracy in predicting FSCMR shapes and tip positions under hybrid actuation scenarios, making it a useful predictive tool for guiding soft magnetic robots in clinical applications.

Keyphrases: fully soft continuum magnetic microrobots (FSCMs), inhomogeneous magnetic field, magnetic dipoles, magnetic gradients, shape deformation

@booklet{EasyChair:10289,
  author    = {Kiana Abolfathi and James Chandler and Keyoumars Ashkan and Luca Citi and Pietro Valdastri and Ali Kafash Hoshiar},
  title     = {Computational Predictive Model for Full Body Controlled Soft Continuum Magnetic Robots Under Hybrid Actuation},
  howpublished = {EasyChair Preprint 10289},
  year      = {EasyChair, 2023}}