Enhanced Accuracy in Magnetic Actuation: Closed-Loop Control of a Magnetic Agent With Low-Error Numerical Magnetic Model Estimation

Magnetic actuation holds promise for wirelessly controlling small, magnetic surgical tools and may enable the next generation of ultra minimally invasive surgical robotic systems. Precise torque and force exertion are required for safe surgical operations and accurate state control. Dipole field est...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-10, Vol.7 (4), p.9429-9436
Main Authors: Erin, Onder, Raval, Suraj, Schwehr, Trevor J., Pryor, Will, Barnoy, Yotam, Bell, Adrian, Liu, Xiaolong, Mair, Lamar O., Weinberg, Irving N., Krieger, Axel, Diaz-Mercado, Yancy
Format: Article
Language:English
Subjects:
Accuracy
Actuation
Closed-loop control
Coils
Dipoles
Errors
Estimation
Feedback control
Finite element method
Magnetic fields
magnetic modeling
Magnetic moments
Magnetic recording
Magnetic resonance imaging
magnetic robots
Mathematical models
medical robotics
Robots
Robustness (mathematics)
Surgical instruments
Torque
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Summary:Magnetic actuation holds promise for wirelessly controlling small, magnetic surgical tools and may enable the next generation of ultra minimally invasive surgical robotic systems. Precise torque and force exertion are required for safe surgical operations and accurate state control. Dipole field estimation models perform well far from electromagnets but yield large errors near coils. Thus, manipulations near coils suffer from severe (10x) field modeling errors. We experimentally quantify closed-loop magnetic agent control performance by using both a highly erroneous dipole model and a more accurate numerical magnetic model to estimate magnetic forces and torques for any given robot pose in 2D. We compare experimental measurements with estimation errors for the dipole model and our finite element analysis (FEA) based model of fields near coils. With five different paths designed for this study, we demonstrate that FEA-based magnetic field modeling reduces positioning root-mean-square (RMS) errors by 48% to 79% as compared with dipole models. Models demonstrate close agreement for magnetic field direction estimation, showing similar accuracy for orientation control. Such improved magnetic modelling is crucial for systems requiring robust estimates of magnetic forces for positioning agents, particularly in force-sensitive environments like surgical manipulation.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3191047