In cardiovascular interventions, when steering catheters and especially robotic catheters, great care should be paid to prevent applying too large forces on the vessel walls as this could dislodge calcifications, induce scars or even cause perforation. To address this challenge, this paper presents a novel compliant motion control algorithm that relies solely on position sensing of the catheter tip and knowledge of the catheter's behavior. The proposed algorithm features a data-driven tip position controller. The controller is trained based on a so-called control Long Short-Term Memory Network (control-LSTM). Trajectory following experiments are conducted to validate the quality of the proposed control-LSTM. Results demonstrated superior positioning capability with sub-degree precision of the new approach in the presence of severe rate-dependent hysteresis. Experiments both in a simplified setup as well as in an aortic phantom further show that the proposed approach allows reducing the interaction forces with the environment by around 70%. This work shows how deep learning can be exploited advantageously to avoid tedious modeling that would be needed to precisely steer continuum robots in constrained environments such as the patient's vasculature.@en

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. @en