ExoNav II: Design of a Robotic Tool with Follow-the-Leader Motion Capability for Lateral and Ventral Spinal Cord Stimulation (SCS)

Spinal cord stimulation (SCS) electrodes are traditionally placed in the dorsal epidural space to stimulate the dorsal column fibers for pain therapy. Recently, SCS has gained attention in restoring gait. However, the motor fibers triggering locomotion are located in the ventral and lateral spinal cord. Currently, SCS electrodes are steered manually, making it difficult to navigate them to the lateral and ventral motor fibers in the spinal cord. In this work, we propose a helically micro-machined continuum robot that can bend in a helical shape when subjected to actuation tendon forces. Using a stiff outer tube and adding translational and rotational degrees of freedom, this helical continuum robot can perform follow-the-leader (FTL) motion. We propose a kinematic model to relate tendon stroke and geometric parameters of the robot's helical shape to its acquired trajectory and end-effector position. We evaluate the proposed kinematic model and the robot's FTL motion capability experimentally. The stroke-based method, which links tendon stroke values to the robot's shape, showed inaccuracies with a 19.84 mm deviation and an RMSE of 14.42 mm for 63.6 mm of robot's length bending. The position-based method, using kinematic equations to map joint space to task space, performed better with a 10.54 mm deviation and an RMSE of 8.04 mm. Follow-the-leader experiments showed deviations of 11.24 mm and 7.32 mm, with RMSE values of 8.67 mm and 5.18 mm for the stroke-based and position-based methods, respectively. Furthermore, end-effector trajectories in two FTL motion trials are compared to confirm the robot's repeatable behavior. Finally, we demonstrate the robot's operation on a 3D-printed spinal cord phantom model.

Towards the Development of a Tendon-Actuated Galvanometer for Endoscopic Surgical Laser Scanning

There is a need for precision pathological sensing, imaging, and tissue manipulation in neurosurgical procedures, such as brain tumor resection. Precise tumor margin identification and resection can prevent further growth and protect critical structures. Surgical lasers with small laser diameters and steering capabilities can allow for new minimally invasive procedures by traversing through complex anatomy, then providing energy to sense, visualize, and affect tissue. In this paper, we present the design of a small-scale tendon-actuated galvanometer (TAG) that can serve as an end-effector tool for a steerable surgical laser. The galvanometer sensor design, fabrication, and kinematic modeling are presented and derived. It can accurately rotate up to 30.14 degrees (or a laser reflection angle of 60.28 degrees). A kinematic mapping of input tendon stroke to output galvanometer angle change and a forward-kinematics model relating the end of the continuum joint to the laser end-point are derived and validated.

An FBG-based Stiffness Estimation Sensor for In-vivo Diagnostics

In-vivo tissue stiffness identification can be useful in pulmonary fibrosis diagnostics and minimally invasive tumor identification, among many other applications. In this work, we propose a palpation-based method for tissue stiffness estimation that uses a sensorized beam buckled onto the surface of a tissue. Fiber Bragg Gratings (FBGs) are used in our sensor as a shape-estimation modality to get real-time beam shape, even while the device is not visually monitored. A mechanical model is developed to predict the behavior of a buckling beam and is validated using finite element analysis and bench-top testing with phantom tissue samples (made of PDMS and PA-Gel). Bench-top estimations were conducted and the results were compared with the actual stiffness values. Mean RMSE and standard deviation (from the actual stiffnesses) values of 413.86 KPa and 313.82 KPa were obtained. Estimations for softer samples were relatively closer to the actual values. Ultimately, we used the stiffness sensor within a mock concentric tube robot as a demonstration of \textit{in-vivo} sensor feasibility. Bench-top trials with and without the robot demonstrate the effectiveness of this unique sensing modality in \textit{in-vivo} applications.

Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots

The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest. In this paper, we investigate the hysteretic response of two types of tendon-actuated continuum robots and, ultimately, compare three types of neural network modeling approaches with both forward and inverse kinematic mappings: feedforward neural network (FNN), FNN with a history input buffer, and long short-term memory (LSTM) network. We seek to determine which model best captures temporal dependent behavior. We find that, depending on the robot's design, choosing different kinematic inputs can alter whether hysteresis is exhibited by the system. Furthermore, we present the results of the model fittings, revealing that, in contrast to the standard FNN, both FNN with a history input buffer and the LSTM model exhibit the capacity to model historical dependence with comparable performance in capturing rate-dependent hysteresis.