Contact Sensing Along Soft Catheter Robots in Minimally Invasive Surgery Based on Magnetic Signals

Contact sensing along the soft catheter robots (SCRs) is crucial for enhancing surgical performance and intraoperative safety during minimally invasive procedures. Nevertheless, current approaches relying on medical imaging devices or optical fiber sensors are noticeably costly and pose challenges for clinical applications. This study develops a more implementable and cost-effective contact sensing method based on magnetic signals for SCR-based surgery. Specifically, several miniature Hall sensors are integrated into the SCR, and a heterogeneous magnetic field is applied. When the SCR comes into contact with the external environment, its deformation leads to changes in the magnetic signal detected by the Hall sensors. A contact sensing deep learning (ContactSenseDL) model is then developed to map the magnetic signal variation to contact position and force along the SCR. The proposed approach is validated on a catheter robot system prototype and achieves remarkable performance. In contact position prediction, the average error of axial arc length is as low as 1.97 mm (2.81% of the maximum insert length of the SCR), and the prediction of radial position exhibits high consistency with actual values. In contact force prediction, the average errors of friction and pressure are 2.43 (2.59% of the maximum friction) and 1.61 mN (2.45% of the maximum pressure), respectively. Additionally, contact sensing experiments are conducted on a knee model to demonstrate the potential application of this method. Overall, the proposed contact sensing strategy can effectively sense the contact position and force along SCRs in 3D space, holding promise for enhancing safety in SCR-based surgery.