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.

翻译：脊髓刺激（SCS）主要用于疼痛管理，近期研究显示其在促进脊髓损伤患者功能恢复方面亦具有显著疗效。为实现对运动神经元的有效刺激，理想情况下需将SCS导线植入腹侧或外侧硬膜外间隙，即皮质脊髓束和红核脊髓束运动纤维所在区域。这对当前依赖手动操控的标准操作提出了严峻挑战。本研究提出一种用于ExoNav可操纵机器人工具的静态建模方法，该工具专为精准导航至腹侧与外侧硬膜外间隙而设计。我们采用Cosserat杆理论框架建立了肌腱驱动力与机器人整体构型之间的力学关系，并以重力为例探讨了外部载荷的影响，将其完整纳入模型与仿真系统。实验结果显示，在四个测试样机中获得的均方根误差值分别为1.76毫米、2.33毫米、2.18毫米和1.33毫米。基于ExoNav在驱动时呈现的螺旋形态，通过为该系统增加插入与旋转自由度，机器人可实现跟随引导（FTL）运动，该特性已在仿真与实验中得到验证。所提出的仿真模型能够在存在重力致形变的情况下，计算实现预定FTL轨迹所需的最优肌腱张力。通过三次FTL实验验证，末端执行器位置与目标路径呈现可重复的对齐特性，最大均方根误差为3.75毫米。最后，在体模实验中演示了遥操作机器人成功导航至脊髓外侧与腹侧目标区域的过程。操作者还能引导器械抵达背根神经节，这展现了ExoNav在运动功能恢复与疼痛管理双重领域的应用潜力。