Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912878219493376 |
|---|---|
| author | Roshanfar, Majid Zhang, Alex He, Changyan Hooshiar, Amir Podolsky, Dale J. Looi, Thomas Diller, Eric |
| author_facet | Roshanfar, Majid Zhang, Alex He, Changyan Hooshiar, Amir Podolsky, Dale J. Looi, Thomas Diller, Eric |
| contents | This paper introduces a learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, providing a compact representation of deformation. A data-driven model was trained on 5,097 experimental samples to learn the mapping from magnetic field parameters (magnitude: 0-14 mT, frequency: 0.2-1.0 Hz, vertical tip distances: 90-100 mm) to Bezier control points defining the robot's 3D shape. Both Neural Network (NN) and Random Forest (RF) architectures were compared. The RF model outperformed the NN, achieving a mean RMSE of 0.087 mm in control point prediction and 0.064 mm in shape reconstruction error. Feature importance analysis revealed that magnetic field components predominantly influence distal control points, while frequency and distance affect the base configuration. Unlike prior studies applying general machine learning to soft robotic data, this framework introduces a new paradigm linking magnetic actuation inputs directly to geometric Bezier control points, creating an interpretable, low-dimensional deformation representation. This integration of magnetic field characterization, embedded FBG sensing, and Bezier-based learning provides a unified strategy extensible to other magnetically actuated continuum robots. By enabling sub-millimeter shape prediction and real-time inference, this work advances intelligent control of magnetically actuated soft robotic tools in minimally invasive neurosurgery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions Roshanfar, Majid Zhang, Alex He, Changyan Hooshiar, Amir Podolsky, Dale J. Looi, Thomas Diller, Eric Robotics This paper introduces a learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, providing a compact representation of deformation. A data-driven model was trained on 5,097 experimental samples to learn the mapping from magnetic field parameters (magnitude: 0-14 mT, frequency: 0.2-1.0 Hz, vertical tip distances: 90-100 mm) to Bezier control points defining the robot's 3D shape. Both Neural Network (NN) and Random Forest (RF) architectures were compared. The RF model outperformed the NN, achieving a mean RMSE of 0.087 mm in control point prediction and 0.064 mm in shape reconstruction error. Feature importance analysis revealed that magnetic field components predominantly influence distal control points, while frequency and distance affect the base configuration. Unlike prior studies applying general machine learning to soft robotic data, this framework introduces a new paradigm linking magnetic actuation inputs directly to geometric Bezier control points, creating an interpretable, low-dimensional deformation representation. This integration of magnetic field characterization, embedded FBG sensing, and Bezier-based learning provides a unified strategy extensible to other magnetically actuated continuum robots. By enabling sub-millimeter shape prediction and real-time inference, this work advances intelligent control of magnetically actuated soft robotic tools in minimally invasive neurosurgery. |
| title | Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.15155 |