Experimental validation of robot-assisted cardiovascular catheterization: model-based versus model-free control.

PURPOSE: In cardiac electrophysiology, a long and flexible catheter is delivered to a cardiac chamber for the treatment of arrhythmias. Although several robot-assisted platforms have been commercialized, the disorientation in tele-operation is still not well solved. We propose a validation platform for robot-assisted cardiac EP catheterization, integrating a customized MR Safe robot, a standard clinically used EP catheter, and a human-robot interface. Both model-based and model-free control methods are implemented in the platform for quantitative evaluation and comparison. METHODS: The model-based and model-free control methods were validated by subject test (ten participants), in which the subjects have to perform a simulated radiofrequency ablation task using both methods. A virtual endoscopic view of the catheter is also provided to enhance hand-to-eye coordination. Assessment indices for targeting accuracy and efficiency were acquired for the evaluation. RESULTS: (1) Accuracy: The average distance measured from catheter tip to the closest lesion target during ablation of model-free method was 19.1% shorter than that of model-based control. (2) Efficiency: The model-free control reduced the total missed targets by 35.8% and the maximum continuously missed targets by 46.2%, both indices corresponded to a low p value ([Formula: see text]). CONCLUSION: The model-free method performed better in terms of both accuracy and efficiency, indicating the model-free control could adapt to soft interaction with environment, as compared with the model-based control that does not consider contacts.

Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation.

Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.