Bayesian Optimization for Design of Multi-Actuator Soft Catheter Robots.

Catheter-based diagnosis and therapy have grown increasingly in recent years due to their improved clinical outcomes including decreased morbidity, shorter recovery time and minimally invasiveness compared to open surgeries. Although the scalability, customizability, and diversity of soft catheter robots are widely recognized, designers and roboticists still lack comprehensive techniques for modeling and designing them. This difficulty arises due to their continuum nature, which makes characterizing the properties and predicting a soft catheter's behavior challenging, complicating robot design tasks. In this paper, we propose modeling multi-actuator soft catheters to enable alignment with desired vessel shapes near the target area. We develop mathematical models to simulate the catheter's positioning due to the moments exerted by multiple pneumatic actuators along the catheter and use those models to compare optimization approaches that can achieve catheter alignment along a desired vessel shape. Specifically, our approach proposes finding the optimal geometric and material properties for a multi-actuator soft catheter robot using a bi-level optimization framework. The upper-level optimization process uses a modified Bayesian technique to seek the optimal geometric and material properties of the soft catheter, which minimize the deviance of the actuated catheter from a desired vessel shape, while the lower-level optimization process uses a gradient-based technique to obtain the actuator moments required to achieve that vessel shape. The results demonstrate the capability of our proposed multi-actuator soft catheter to align with the desired vessel shapes, and show that the proposed framework which is in the context of Bayesian optimization has the potential to expedite the design process.