TY - JOUR
T1 - Shape sensing of optical fiber Bragg gratings based on deep learning
AU - Manavi Roodsari, Samaneh
AU - Huck-Horvath, Antal
AU - Freund, Sara
AU - Zam, Azhar
AU - Rauter, Georg
AU - Schade, Wolfgang
AU - Cattin, Philippe C.
N1 - Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving precise and reliable shape estimation of such snake-like manipulators necessitates an accurate navigation system, that requires no line-of-sight and is immune to electromagnetic noise. Fiber Bragg grating (FBG) shape sensing, particularly eccentric FBG (eFBG), is a promising and cost-effective solution for this task. However, in eFBG sensors, the spectral intensity of the Bragg wavelengths that carries the strain information can be affected by undesired bending-induced phenomena, making standard characterization techniques less suitable for these sensors. We showed in our previous work that a deep learning model has the potential to extract the strain information from the eFBG sensor’s spectrum and accurately predict its shape. In this paper, we conducted a more thorough investigation to find a suitable architectural design of the deep learning model to further increase shape prediction accuracy. We used the Hyperband algorithm to search for optimal hyperparameters in two steps. First, we limited the search space to layer settings of the network, from which, the best-performing configuration was selected. Then, we modified the search space for tuning the training and loss calculation hyperparameters. We also analyzed various data transformations on the network’s input and output variables, as data rescaling can directly influence the model’s performance. Additionally, we performed discriminative training using the Siamese network architecture that employs two convolutional neural networks (CNN) with identical parameters to learn similarity metrics between the spectra of similar target values. The best-performing network architecture among all evaluated configurations can predict the shape of a 30 cm long sensor with a median tip error of 3.11 mm in a curvature range of 1.4 m−1 to 35.3
AB - Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving precise and reliable shape estimation of such snake-like manipulators necessitates an accurate navigation system, that requires no line-of-sight and is immune to electromagnetic noise. Fiber Bragg grating (FBG) shape sensing, particularly eccentric FBG (eFBG), is a promising and cost-effective solution for this task. However, in eFBG sensors, the spectral intensity of the Bragg wavelengths that carries the strain information can be affected by undesired bending-induced phenomena, making standard characterization techniques less suitable for these sensors. We showed in our previous work that a deep learning model has the potential to extract the strain information from the eFBG sensor’s spectrum and accurately predict its shape. In this paper, we conducted a more thorough investigation to find a suitable architectural design of the deep learning model to further increase shape prediction accuracy. We used the Hyperband algorithm to search for optimal hyperparameters in two steps. First, we limited the search space to layer settings of the network, from which, the best-performing configuration was selected. Then, we modified the search space for tuning the training and loss calculation hyperparameters. We also analyzed various data transformations on the network’s input and output variables, as data rescaling can directly influence the model’s performance. Additionally, we performed discriminative training using the Siamese network architecture that employs two convolutional neural networks (CNN) with identical parameters to learn similarity metrics between the spectra of similar target values. The best-performing network architecture among all evaluated configurations can predict the shape of a 30 cm long sensor with a median tip error of 3.11 mm in a curvature range of 1.4 m−1 to 35.3
KW - bending birefringence
KW - bending loss
KW - curvature sensing
KW - eccentric FBG
KW - fiber sensor
KW - shape sensing
KW - supervised deep learning
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UR - http://www.scopus.com/inward/citedby.url?scp=85165137193&partnerID=8YFLogxK
U2 - 10.1088/2632-2153/acda10
DO - 10.1088/2632-2153/acda10
M3 - Article
AN - SCOPUS:85165137193
SN - 2632-2153
VL - 4
JO - Machine Learning: Science and Technology
JF - Machine Learning: Science and Technology
IS - 2
M1 - 025037
ER -