Helical Tendon-Driven Continuum Robot with Programmable Follow-the-Leader Operation

TL;DR

The paper tackles the challenge of precisely placing spinal cord stimulation leads in ventral and lateral epidural spaces, where manual steering is limited. It introduces ExoNav, a helically notched nitinol-tube continuum robot actuated by a tendon, and develops a customized Cosserat-rod model to capture tendon-induced deformations under gravity. Key contributions include adapting the Cosserat framework for convoluted notch patterns, proposing a tendon-tension optimization approach for close-to-FTL operation under external loads, and validating the concept through four prototypes, a gravity-aware simulation, and phantom-spinal-cord demonstrations. The results indicate accurate planning and tracking of FTL paths, improved predictions when gravity is modeled, and practical feasibility for steering SCS leads to clinically relevant targets, with future work focusing on electrode integration and real-time control enhancements.

Abstract

Spinal cord stimulation (SCS) is primarily utilized for pain management and has recently demonstrated efficacy in promoting functional recovery in patients with spinal cord injury. Effective stimulation of motor neurons ideally requires the placement of SCS leads in the ventral or lateral epidural space where the corticospinal and rubrospinal motor fibers are located. This poses significant challenges with the current standard of manual steering. In this study, we present a static modeling approach for the ExoNav, a steerable robotic tool designed to facilitate precise navigation to the ventral and lateral epidural space. Cosserat rod framework is employed to establish the relationship between tendon actuation forces and the robot's overall shape. The effects of gravity, as an example of an external load, are investigated and implemented in the model and simulation. The experimental results indicate RMSE values of 1.76mm, 2.33mm, 2.18mm, and 1.33mm across four tested prototypes. Based on the helical shape of the ExoNav upon actuation, it is capable of performing follow-the-leader (FTL) motion by adding insertion and rotation DoFs to this robotic system, which is shown in simulation and experimentally. The proposed simulation has the capability to calculate optimum tendon tensions to follow the desired FTL paths while gravity-induced robot deformations are present. Three FTL experimental trials are conducted and the end-effector position showed repeatable alignments with the desired path with maximum RMSE value of 3.75mm. Ultimately, a phantom model demonstration is conducted where the teleoperated robot successfully navigated to the lateral and ventral spinal cord targets. Additionally, the user was able to navigate to the dorsal root ganglia, illustrating ExoNav's potential in both motor function recovery and pain management.

Figures (9)

• Figure 1: (a) cross-sectional view of spinal vertebral segments and the spinal cord with ExoNav deformed in a helical shape around the spinal cord. (b) Close view of ExoNav, navigated from the dorsal spinal cord (entry point) to the ventral spinal cord (target region).
• Figure 2: (a) Structure of ExoNav: a nitinol tube with helically machined rectangular notches along its length. Straight central axis and helical neutral axis are depicted over the structure. (b) Zoomed-in views showing the (a) central and neutral axis locations, and (b) notch patterns and their dimensions.
• Figure 3: (a) The ExoNav bending into a helical curve, with the global coordinate frame defined at the robot base. The position vector $\textbf{p}(s)$ denotes the neutral axis at an arbitrary arc-length $s$. (b) Cross-sectional view at arc-length $s$, showing the locations of the actuation tendon, the neutral axis, and the effective cross-sectional area in the local coordinate frame.
• Figure 4: (a) ExoNav simulation results without gravity modeling: (a-1) optimized $\tau$ values along different FTL trajectories and (a-2) simulated ExoNav end-effector and body trajectories evolution compared to the reference FTL trajectory. (b) ExoNav simulation results with gravity effects included: (b-1) optimized $\tau$ values along different FTL trajectories and (b-2) simulated ExoNav end-effector and body trajectories evolution compared to the reference FTL trajectory.
• Figure 5: Components of the actuation setup, with the tendon pulling mechanism on the left, the dual-DoF roller-gear mechanism in the center (inset shows circumferential and longitudinal gear heads), and the robot protruding from the outer tube on the right. An EM tracker is attached to the robot via a 3D-printed holder (inset on the right), with the EM field generator positioned beneath the exposed robot.