Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing

TL;DR

This paper introduces a soft robotic endoscope that autonomously navigates inside luminal organs using deep learning-based visual servoing, trained on phantom and in-vivo data, validated in anatomical phantoms with promising results.

Contribution

It presents a novel soft robot with CNN-based visual servoing for autonomous intraluminal navigation, including a model-less control approach and validation in realistic conditions.

Findings

Successful autonomous navigation in anatomical phantoms

Robust performance despite different training conditions

Effective control in constrained luminal environments

Abstract

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.