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

• URL: http://arxiv.org/abs/2207.00401v1
• Date: Fri, 1 Jul 2022 13:17:45 GMT
• Title: Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
• Authors: Jorge F. Lazo and Chun-Feng Lai and Sara Moccia and Benoit Rosa and Michele Catellani and Michel de Mathelin and Giancarlo Ferrigno and Paul Breedveld and Jenny Dankelman and Elena De Momi
• Abstract summary: We present a synergic solution for intraluminal navigation consisting of a 3D printed endoscopic soft robot. Visual servoing, based on Convolutional Neural Networks (CNNs), is used to achieve the autonomous navigation task. The proposed robot is validated in anatomical phantoms in different path configurations.
• Score: 13.268863900187025
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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