IROS Conference 2025 Conference Paper
A Physics-Informed Neural Network for the Calibration of Electromagnetic Navigation Systems
- Pascal Ernst
- Simone Gervasoni
- Derick Sivakumaran
- Enea Masina
- David F. Sargent
- Bradley J. Nelson
- Quentin Boehler
Electromagnetic Navigation Systems enable remote actuation of untethered micro and nanorobots, as well as the precise control of magnetic surgical tools for minimally invasive medical procedures. Accurate modeling of the magnetic fields generated by the electromagnets composing these systems is essential for achieving reliable and precise navigation. Existing modeling approaches either neglect nonlinear effects such as electromagnet saturation or fail to ensure that the field predictions are physically consistent. These limitations can lead to significant prediction errors, particularly in the estimation of field gradients, which directly impacts force calculations. As a result, inaccurate gradient predictions degrade force control performance, limiting the precision of magnetic actuation. In this work, we investigate physics-informed and data-driven modeling techniques to improve the accuracy of magnetic field and gradient predictions. Additionally, we introduce an approach for solving the inverse problem, developing models capable of predicting the required electromagnet currents to generate a desired magnetic field and gradient based on this approach. By incorporating physical constraints into the models, we enhance the predictive accuracy and physical consistency of the field estimates. In the experimental section, we demonstrate the benefits of these methods to enable improved force control in open-loop for untethered robots using a small-scale Electromagnetic Navigation System.