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This study introduces ExoNav, a continuum robot utilizing helical tendon actuation to achieve precise navigation for spinal cord stimulation lead placement. By employing a Cosserat rod formulation and follow-the-leader motion strategies, the system demonstrates sub-millimeter accuracy in reaching critical motor fibers within the epidural space. The research advances robotic-assisted neurosurgery by enabling teleoperated access to previously difficult-to-reach targets for pain management and functional recovery.
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Helical Tendon-Driven Continuum Robot with Programmable Follow-the-Leader Operation

arXiv3 min read

arXiv:2601.13177v2 Announce Type: replace Abstract: Spinal cord stimulation (SCS) is widely used for pain management and has recently shown promise in promoting functional recovery following spinal cord injury. Effective activation of motor neurons requires accurate placement of SCS leads in the ventral or lateral epidural space, where corticospinal and rubrospinal motor fibers are located, a task that remains challenging with conventional manual steering. This study presents a static modeling framework for ExoNav, a steerable robotic tool designed to enable precise navigation to ventral and lateral epidural targets. A Cosserat rod formulation is employed to relate tendon actuation forces to the robot shape, and a simulation environment is developed based on this model, incorporating gravity as an external load. Experimental validation across four prototypes yields tip position root mean square errors (RMSEs) of 1.76 mm, 2.33 mm, 2.18 mm, and 1.33 mm, corresponding to 2.3 , 3.1, 3.4, and 2.1 percent of the robot length, respectively. Owing to its helical deformation under actuation, ExoNav enables follow-the-leader (FTL) motion when combined with insertion and rotation degrees of freedom, which is demonstrated in both simulation and experiments. The proposed simulation computes optimal tendon tensions to track desired FTL paths under gravity-induced deformation. Three FTL experiments show repeatable end-effector alignment with a maximum RMSE of 3.75 mm (less than 5 percent of the robot length). Finally, phantom experiments demonstrate successful teleoperated navigation to ventral and lateral spinal cord targets, as well as the dorsal root ganglia, highlighting ExoNav potential for both motor function recovery and pain management.

Abstract:Spinal cord stimulation (SCS) is widely used for pain management and has recently shown promise in promoting functional recovery following spinal cord injury. Effective activation of motor neurons requires accurate placement of SCS leads in the ventral or lateral epidural space, where corticospinal and rubrospinal motor fibers are located, a task that remains challenging with conventional manual steering. This study presents a static modeling framework for ExoNav, a steerable robotic tool designed to enable precise navigation to ventral and lateral epidural targets. A Cosserat rod formulation is employed to relate tendon actuation forces to the robot shape, and a simulation environment is developed based on this model, incorporating gravity as an external load. Experimental validation across four prototypes yields tip position root mean square errors (RMSEs) of 1.76 mm, 2.33 mm, 2.18 mm, and 1.33 mm, corresponding to 2.3 , 3.1, 3.4, and 2.1 percent of the robot length, respectively. Owing to its helical deformation under actuation, ExoNav enables follow-the-leader (FTL) motion when combined with insertion and rotation degrees of freedom, which is demonstrated in both simulation and experiments. The proposed simulation computes optimal tendon tensions to track desired FTL paths under gravity-induced deformation. Three FTL experiments show repeatable end-effector alignment with a maximum RMSE of 3.75 mm (less than 5 percent of the robot length). Finally, phantom experiments demonstrate successful teleoperated navigation to ventral and lateral spinal cord targets, as well as the dorsal root ganglia, highlighting ExoNav potential for both motor function recovery and pain management.

Robotics (cs.RO)

arXiv:2601.13177 [cs.RO]

(or arXiv:2601.13177v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2601.13177 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 339 words

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