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

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

Lazo, J. F. (Politecnico di Milano; University of Strasbourg)
Lai, C. (TU Delft Medical Instruments & Bio-Inspired Technology; Politecnico di Milano)
Moccia, S. (The BioRobotics Institute; Scuola Superiore Sant’Anna)
Rosa, B. (University of Strasbourg)
Catellani, M. (University of Strasbourg)
Mathelin, M. de (European Institute of Oncology IRCCS)
Ferrigno, G. (Politecnico di Milano)
Breedveld, P. (TU Delft Medical Instruments & Bio-Inspired Technology)
Dankelman, J. (TU Delft Medical Instruments & Bio-Inspired Technology)
Momi, E. De (Politecnico di Milano)

2022

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.

http://resolver.tudelft.nl/uuid:fe4c4e6f-5765-49bb-8e4e-6be2c1b79381

https://doi.org/10.1109/IROS47612.2022.9982141

IEEE

2023-06-26

978-1-6654-7927-1

Proceedings 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), 2022-10-23 → 2022-10-27, Kyoto, Japan

IEEE International Conference on Intelligent Robots and Systems, 2153-0858, 2022-October

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 J. F. Lazo, C. Lai, S. Moccia, B. Rosa, M. Catellani, M. de Mathelin, G. Ferrigno, P. Breedveld, J. Dankelman, E. De Momi