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, and 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.

本文提出了一种基于卷积神经网络的视觉伺服方法，并使用幻影和体内数据对其进行训练，以在空心环境中完成内腔镜导航任务的软体机器人解决方案。