Adapting Neural-Based Models for Position Error Compensation in Robotic Catheter Systems

Abstract

Robotic catheter systems with master–slave designs are employed for teleoperated navigation of flexible endovascular tools for treating calcified lesions. Despite improved tool manipulation techniques, patient safety and lowering operative risks remain top priorities. Therefore, minimizing undesirable drifts and imprecise navigation of flexible tools during intravascular catheterization is essential. In the current master–slave designs, finite displacement lag between position command and actual navigation action at the slave device affects smooth catheterization. In this study, we designed and developed a compact 2-DOF robotic catheter system and characterized the influence of displacement step values, velocity, and motion gap on the position error at the slave device. For uniform and varying motion commands from the master platform, the results indicate that the overall position error increases with the distance traveled and the displacement step values, respectively. Hence, we proposed using recurrent neural networks—long short-term memory and gated recurrent unit controllers to predict the slave robot’s position and appropriate compensation value per translation step. An analysis of in-silico studies with CoppeliaSim showed that the neural-based controllers can ensure uniform motion mapping between the master–slave devices. Furthermore, we implemented the models within the RCS for a catheterization length of 120 mm. The result demonstrates that the controllers suitably aid the slave robot’s stepwise displacement. Thus, the neural-based controllers help match the translational motion and precise tool navigation by the slave robotic device. Therefore, the neural-based controllers could contribute to alleviating patients’ safety concerns during robotic interventions.

Keywords

• robot-assisted catheterization
• position error control
• teleoperation
• learning-based systems
• deep learning