Behnam Moradkhani

Harshith Jella, Pejman Kheradmand, Joseph Klein et al.

This paper introduces a novel robotic system designed to manage severe bleeding in emergency scenarios, including unique environments like space stations. The robot features a shape-adjustable "ring mechanism", transitioning from a circular to an elliptical configuration to adjust wound coverage across various anatomical regions. We developed various arms for this ring mechanism with varying flexibilities to improve adaptability when applied to non-extremities of the body (abdomen, back, neck, etc.). To apply equal and constant pressure across the wound, we developed an inflatable ring and airbag balloon that are compatible with this shape-changing ring mechanism. A series of experiments focused on evaluating various ring arm configurations to characterize their bending stiffness. Subsequent experiments measured the force exerted by the airbag balloon system using a digital scale. Despite its promising performance, certain limitations related to coverage area are identified. The shape-changing effect of the device is limited to scenarios involving partially inflated or deflated airbag balloons, and cannot fully conform to complex anatomical regions. Finally, the device was tested on casualty simulation kits, where it successfully demonstrated its ability to control simulated bleeding.

Pejman Kheradmand, Behnam Moradkhani, Mir Masoud Ale Ali et al.

Steerable needles have the potential to improve interstitial brachytherapy by enabling curved trajectories that avoid sensitive anatomical structures. However, existing modeling and control approaches are primarily developed for custom needle designs and are not directly applicable to stylets compatible with commercially available clinical needles. This paper presents a bilinear model predictive control (MPC) framework for a tendon-driven steerable stylet integrated with a standard brachytherapy needle. \textcolor{black}{A geometric bilinear model is formulated with three virtual inputs (an insertion speed and two bending rates) which are mapped to physically realizable inputs consisting of the insertion speed and the associated tendon tensions.} The approach is validated through simulations and physical insertion experiments in tissue-mimicking phantom material using image-based tip tracking. While open-loop model validation yielded estimation errors below $2$~mm, corresponding to $3\%$ of the inserted needle length, and closed-loop fixed-target tracking achieved an error as low as $1.45$~mm, corresponding to $1.7\%$ of the inserted length, experiments showed larger position errors in certain bending directions, reaching $8.3$~mm, or $7.8\%$ of the inserted length. Overall, the results demonstrate the feasibility of fixed-target positioning and moving-target trajectory tracking for clinically compatible steerable brachytherapy systems, while highlighting necessary areas for future improvements in calibration and sensing.

Yuan Wang, Max McCandless, Abdulhamit Donder et al.

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.