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Enhanced Accuracy in Magnetic Actuation: Closed-loop Control of a Magnetic Agent with Low-Error Numerical Magnetic
Onder Erin1, Suraj Raval2, Trevor J Schwehr1
1Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Robotics and Automation Letters
|December 22, 2022
Summary
Accurate magnetic field modeling is crucial for robotic surgery. Using finite element analysis (FEA) models near coils significantly reduces magnetic control errors compared to simpler dipole models.
Area of Science:
- Robotics
- Medical Devices
- Electromagnetism
Background:
- Magnetic actuation offers wireless control for surgical tools, enabling minimally invasive robotic surgery.
- Precise force and torque control are essential for safe and accurate surgical manipulations.
- Traditional dipole field models exhibit significant errors near electromagnetic coils, hindering precise control.
Purpose of the Study:
- To experimentally evaluate the performance of magnetic agent control using both dipole and finite element analysis (FEA) models.
- To quantify the reduction in magnetic force and torque estimation errors near coils.
- To demonstrate the impact of improved magnetic field modeling on positioning accuracy in robotic surgery.
Main Methods:
- Experimental comparison of closed-loop magnetic agent control performance.
- Utilizing a dipole model and a finite element analysis (FEA) based numerical magnetic model.
- Estimating magnetic forces and torques for various robot poses in a 2D environment.
Main Results:
- FEA-based magnetic field modeling reduced positioning root-mean-square (RMS) errors by 48% to 79% compared to dipole models.
- Both models showed similar accuracy in magnetic field direction estimation for orientation control.
- Significant field modeling errors (up to 10x) were observed with dipole models near coils.
Conclusions:
- Finite element analysis (FEA) based magnetic field modeling significantly improves positioning accuracy for magnetic actuation systems.
- Accurate magnetic field modeling is critical for robust force estimation, especially in force-sensitive applications like surgical manipulation.
- Improved modeling is essential for advancing ultra-minimally invasive surgical robotic systems.
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