Toward Task Autonomy in Robotic Cardiac Ablation: Learning-Based Kinematic Control of Soft Tendon-Driven Catheters.

The goal of this study was to propose and validate a control framework with level-2 autonomy (task autonomy) for the control of flexible ablation catheters. To this end, a kinematic model for the flexible portion of typical ablation catheters was developed and a 40-mm-long spring-loaded flexible catheter was fabricated. The feasible space of the catheter was obtained experimentally. Furthermore, a robotic catheter intervention system was prototyped for controlling the length of the catheter tendons. The proposed control framework used a support vector machine classifier to determine the tendons to be driven, and a fully connected neural network regressor to determine the length of the tendons. The classifier and regressors were trained with the data from the feasible space. The control system was implemented in parallel at user-interface and firmware and exhibited a 0.4-s lag in following the input. The validation studies were four trajectory tracking and four target reaching experiments. The system was capable of tracking trajectories with an error of 0.49 ± 0.32 and 0.62 ± 0.36 mm in slow and fast trajectories, respectively. Also, it exhibited submillimeter accuracy in reaching three preplanned targets and ruling out one nonfeasible target autonomously. The results showed improved accuracy and repeatability of the position control compared with the recent literature. The proposed learning-based approach could be used in enabling task autonomy for catheter-based ablation therapies.