Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The development of tools to automate certain tasks could alleviate the physical and mental load of doctors during interventions allowing them to focus on diagnosis and decision-making tasks. In this paper we present a synergic solution for intraluminal navigation consisting of a 3D printed endoscopic soft robot that can move safely inside luminal structures. Visual servoing based on Convolutional Neural Networks (CNNs) is used to achieve the autonomous navigation task. The CNN is trained with phantoms and in-vivo data to segment the lumen and a model-less approach is presented to control the movement in constrained environments. The proposed robot is validated in anatomical phantoms in different path configurations. We analyze the movement of the robot using different metrics such as task completion time smoothness error in the steady-state mean and maximum error. We show that our method is suitable to navigate safely in hollow environments and conditions which are different than the ones the network was originally trained on.

Recent technological advancements in cardiovascular surgery such as transapical transcatheter aortic valve implantation (TA-TAVI) enabled treatment to elderly that were initially declined surgery. However, valve malpositioning during TA-TAVI have been reported in several cases. In this preliminary study, we present a novel approach in which a Robotically-Actuated Delivery Sheath (RADS) is used to potentially facilitate valve positioning. A model is developed that describes the shape and articulating tip position of the RADS. We developed a two-dimensional ultrasound tracking method that evaluates the tip position of the RADS in ultrasound images. Both modeling and ultrasound tracking are combined into an integrated system that facilitates closed-loop control of the articulating tip of the RADS. Experiments are performed in order to evaluate the tracking accuracy of the RADS. Experiments show mean positioning errors of approximately 2 mm along the x- and y-axes. Our study demonstrates that the RADS can potentially provide compensation for beating heart and respiratory motions during valve positioning and deployment in TA-TAVI.