Electromagnetic Navigation Systems (eMNS)

Updated 30 November 2025

Electromagnetic Navigation Systems (eMNS) are integrated platforms that use coil-generated magnetic fields and real-time sensing to localize, actuate, and control instruments in medical and industrial settings.
They employ advanced field modeling, machine learning, and nonlinear filtering to overcome challenges like core saturation and environmental distortions, ensuring high accuracy.
eMNS enable minimally invasive interventions and robotics through motion-centric control and dynamic feedback, significantly expanding operational workspaces and improving clinical outcomes.

Electromagnetic Navigation Systems (eMNS) are integrated platforms that employ coil-generated magnetic fields and real-time sensing to localize, actuate, and dynamically control magnetic or magnetically tracked instruments within a defined workspace. Predominantly used in image-guided interventions, minimally invasive surgery, robotic navigation, and medical instrument tracking, eMNS combine electromechanical hardware, field modeling, advanced filtering, algorithmic pose solvers, and high-bandwidth feedback control for precision spatial manipulation and localization in scenarios where optical or acoustic methods are infeasible.

1. System Architectures and Fundamental Principles

eMNS realize spatial control and localization by generating structured magnetic fields through arrays of electromagnets. There are two core operation modalities:

Electromagnetic tracking systems (EMT): Localize miniaturized EM sensors (coils or magnetic markers) immersed in the controlled field, inferring their pose from induced voltages via model-based solvers and real-time filtering (Jaeger et al., 2018, Mittmann et al., 2019).
Electromagnetic actuation systems: Actuate magnetic objects directly by exerting forces and torques with combined fields and gradients, enabling manipulation of magnetic tools, robots, or capsules (Schonewille et al., 2023, Zughaibi et al., 8 Feb 2024).

The basic physical interaction is governed by dipole field equations and the force/torque relationships

$\mathbf{F} = \nabla(\mathbf{m}\cdot\mathbf{B}),\quad \boldsymbol{\tau} = \mathbf{m}\times\mathbf{B}$

where $\mathbf{m}$ is the magnetic moment, and $\mathbf{B}$ is the resultant field at the object location (Schonewille et al., 2023). System fidelity critically depends on linearity (absence of core saturation), absence of external distortions, and the spatial proximity of the target to the field-generating coils.

2. Field Generation, Sensing, and Modeling

Electromagnetic Hardware Design

Coil arrays are engineered to maximize field magnitude, uniformity, and actuation workspace subject to thermal and geometric constraints (Schonewille et al., 2023, Zughaibi et al., 23 Nov 2025). Established designs range from compact, portable field generators for tracking (e.g., Planar 10-11 FG: 112 × 97 × 31 mm, 340 mm diameter coverage (Mittmann et al., 2019)) to large under-table eight-coil systems for micro-robotic actuation (up to 47.8 mT fields at 120 mm) (Schonewille et al., 2023).

Field Modeling: The superposition principle applies at low currents, allowing field computation via analytic dipole (Biot-Savart) models and linear calibration matrices (Schonewille et al., 2023, Jaeger et al., 2018).
Nonlinear Regimes: At high drive currents or in the presence of core saturation, superposition and linearity break down. Machine learning models—random forests (RF), artificial neural networks (ANN)—outperform traditional multipole approximations, reducing root mean square error (RMSE) by 40–80% overall, and >35 mT in the high-current regime (30–35 A) (Yu et al., 2019).

Model	RMSE_norm (mT, 30–35A)	Workspace limitation
Linear Multipole (LMEM)	>38	Severe at high field; saturates quickly
Random Forest (RF)	~18	Moderate improvement, less smooth extrapolation
ANN	~3	Best nonlinear performance, smooth mapping

Real-Time Sensing and Filtering

Acquisition: Synchronous demodulation (lock-in amplification) and high-frequency digital filtering extract field components from sensor coils in the presence of strong ambient and system noise (Jaeger et al., 2018, Gutnik et al., 2023).
Pose Solving: Nonlinear least-squares methods (Levenberg–Marquardt, trust region) or grid-based search minimize the discrepancy between measured and modeled field amplitudes to output the best-fit position/orientation (Gutnik et al., 2023, Jaeger et al., 2018).
Error Compensation: Artificial neural network-based online compensation corrects for environmental distortion, simultaneously providing uncertainty metrics and suggesting x-ray or external recalibration only when estimated error bounds are exceeded (Krumb et al., 2020).

3. Control, Actuation, and Workspace Expansion

Field vs. Motion-Centric Control

Traditional eMNS actuation targets a desired static field vector at the device, requiring high and spatially localized coil currents, which limits workspace due to $\sim 1/r^3$ decay.

Advanced "motion-centric" control directly specifies desired wrench (torque/force) vectors and solves for the optimal coil current allocation with quadratic programming (energy-optimal allocation), substantially reducing currents and enabling much larger workspaces (Zughaibi et al., 23 Nov 2025).

Workspace Expansion: On OctoMag, replacing field-centric with torque/force-centric control reduced necessary current by two orders of magnitude (0.1–0.2 A vs. 8–14 A per coil) while expanding the field workspace from 5–8 cm up to 20 cm from coil center; similarly, the Navion system achieved stable actuation at up to 50 cm (Zughaibi et al., 23 Nov 2025).

System	Traditional Field-Alignment Range	Motion-Centric Control Range
OctoMag	5–8 cm	∼20 cm
Navion	Not specified	Up to 50 cm

Multi-agent Actuation: Optimal allocation and redundancy in coil arrays enable independent actuation of multiple instruments within a shared workspace, first demonstrated by simultaneous 3D inverted pendulum stabilization (Zughaibi et al., 23 Nov 2025).

Dynamic Feedback and High-Bandwidth Operation

System Dynamics: Lagrangian modeling, system identification (multisine protocols), and state-space control enable high-fidelity dynamic control with mechanical resonance up to ∼5–6 Hz and actuation bandwidths (−3 dB) of 8–10 Hz (Zughaibi et al., 8 Feb 2024).
Feedback Control: State-feedback (LQR, LQRI) and iterative learning control (ILC) architectures match reference trajectories, reject disturbances, and compensate for alignment errors in real time (Zughaibi et al., 8 Feb 2024, Zughaibi et al., 23 Nov 2025).
Platform Bandwidth: Control-loop rates of 125–200 Hz with current-loop bandwidths >24 Hz are critical for translational performance in surgical or robotic environments subjected to dynamic disturbances (Zughaibi et al., 23 Nov 2025, Zughaibi et al., 8 Feb 2024).

4. Applications and Use Cases

Medical Interventions

Instrument Tracking: Sub-millimeter accuracy (<0.6 mm positional error, <0.2° orientation error) supports navigation in percutaneous thyroid puncture, endoscopic sinus surgery, and neurovascular catheterization (Mittmann et al., 2019). Compact, portable field generators can be integrated into surgical headrests for proximity and flexibility.
Microsurgery and Magnetic Actuation: Under-table eight-coil eMNS delivers up to 14.3 mN force on 1×1×3 mm micro-tools, with workspace encompassing an entire human head (sphere of 120 mm radius, 222° surgical cone) (Schonewille et al., 2023).
Wireless Capsule Endoscopy and Tubular Navigation: Dipole-based external actuation and localization enable autonomous endoscopic navigation in unknown and variable anatomies, with model-predictive control for trajectory following, achieving RMS tracking errors as low as 2–4 mm in phantom and ex-vivo tissue (Xu et al., 2021).
Hybrid Navigation: ANN-based compensation integrated with uncertainty-driven hybrid navigation (e.g., guidewire tracking with adaptive fluoroscopy) reduces required x-ray recalibrations by >50% for sub-millimeter accuracy (Krumb et al., 2020).
Bioelectric Registration: Fusion of EM tracking and bioelectric signatures from cardiac electrophysiology catheters provides fully automatic, markerless registration of preoperative CT to EM tracker space with mean RMS errors <1 mm after ICP refinement (Ramadani et al., 2022).

Non-Medical and Industrial Applications

AUV Docking: Electromagnetic beacons and real-time nonlinear localization algorithms achieve sub–3 cm accuracy in omnidirectional terminal-phase underwater docking, using magnetometer-only real-time pose estimation (Gutnik et al., 2023).
Indoor Navigation: Distributed magnetometer arrays aid inertial navigation by tightly coupling polynomial field models with Kalman filtering, achieving 1–2 orders of magnitude reduction in position drift over INS alone (<3 m error over 2 min traversals) (Huang et al., 2023).

5. Error Sources, Compensation, and Robustness

Metallic Disturbances: Field distortion near metallic objects (ferromagnetic steel, SST 303) can introduce position errors up to 4.8 mm at close proximity; selecting magnetically passive surgical instruments (SST 416, aluminum) and maintaining separation >5 cm minimizes systematic errors (<0.5 mm) (Mittmann et al., 2019).
Field Modeling Nonlinearities: Core saturation, thermal drift, and cross-coil magnetic coupling undermine the predictive accuracy of linear field models; machine learning regressors (ANN, RF) close this gap by directly learning nonlinear field mappings from multidimensional sensor/actuator data (Yu et al., 2019).
Algorithmic Compensation: Real-time uncertainty-aware error compensation via neural networks and adaptive filtering maintains accuracy under time-varying distortion, triggers recalibration only when necessary, and provides quantitative error bounds for clinical safety (Krumb et al., 2020).
Systemic Limitations: Current hardware bottlenecks include coil cooling, core saturation (>4–6 A/mm²), and finite actuation bandwidth. Heat management (active water cooling, advanced core materials) and hardware scalability are active research areas (Schonewille et al., 2023).

6. Software Frameworks and Standardization

A modular eMNS software framework typically comprises data acquisition, filtering, field modeling, and pose-solving modules, each with minimal, standardized data interfaces (Jaeger et al., 2018).

Acquisition modules digitize high-frequency analog sensor data.
Filter modules extract field/coupling amplitude via digital bandpass or Kalman filtering.
Model modules generate predicted field/voltage for arbitrary poses by analytic or data-driven models.
Solver modules compute real-time position/orientation through nonlinear optimization against observed sensor data.

The reference open-source framework, demonstrated in both Python and Matlab, achieves indicative system latencies of 5 ms and <1 mm RMS tracking errors at update rates >80 Hz (Jaeger et al., 2018).

7. Future Directions and Scalability

Workspace Expansion: Adoption of motion-centric, energy-optimal allocation with dynamic feedback is expected to generalize to higher degrees of freedom, complex multi-agent environments, and clinical-scale interventions (Zughaibi et al., 23 Nov 2025).
Distributed Sensing and SLAM: Arrays of magnetometers with polynomial field modeling support robust mapping and extended “blind” navigation phases, relaxing loop-closure requirements in SLAM (Huang et al., 2023).
ML-Driven Field Modeling: Deep or physics-informed networks, uncertainty quantification, and online adaptation are key research frontiers for safety-critical systems (Yu et al., 2019).
Integrated Sensing Modalities: Bioelectric, inertial, optical, and magnetic sensing fusion yields robust, markerless, and workflow-integrated navigation and registration protocols for surgery (Ramadani et al., 2022, Xu et al., 2021).
Thermal and Component Miniaturization: Innovations in coil materials, active cooling, and hybrid electromagnetic/permanent magnet arrays are anticipated to expand operational regimes and minimize system bulk (Schonewille et al., 2023).
Clinical and Dynamic Applications: As actuation bandwidth and dynamic control improve, eMNS are poised for broader adoption in cardiovascular, neurovascular, and fast-response robotic tasks (Zughaibi et al., 8 Feb 2024).