Deep-learning-based Position Control of a Robotic Catheter under Environmental Contact

Abstract

Precise control of robotic catheters remains challenging in interventions. Inherent non-linearities such as hysteresis and external disturbances such as blood flow or contact with the vessel walls have a large impact on the reachable positioning precision. As inaccurate positioning of the catheter tip could lead to tissue damage, controllers that would perform adequately in the presence of hysteresis and environmental contacts would be highly desirable. This paper proposes a method based on multiple Long Short-Term Memory Networks (LSTMs). To this end, a so-called free-space-LSTM (f-LSTM) is trained in order to steer the catheter when it moves in free. Constrained-space-LSTMs (c-LSTMs) are trained to drive the catheter when it is in contact with an obstacle. Based on contact estimation methods, LSTMs are switched. The f-LSTM and c-LSTMs are first tested in free space motion and under constraint situations. The results reveal that LSTMs perform well (RMSE < 0.5 mm) for a steerable robot section with a total length of 77 mm when tested in the same situation where trained. However, when f-LSTM and c-LSTM were tested in an environment different from the one in which they were trained, errors tended to increase. The results highlight the need to exhaustively estimate the contact location and switch between different LSTMs accordingly. The effective working range of a c-LSTM was investigated as well. Experiments showed that a well-Trained single c-LSTM could be used effectively in a range of 8.8 mm among the entire length of a steerable catheter section, while maintaining the average tip positioning error below 2 mm in this range.