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2. Using Supervised Deep-Learning to Model Edge-FBG Shape Sensors

Author

Roodsari, Samaneh Manavi, Huck-Horvath, Antal, Freund, Sara, Zam, Azhar, Rauter, Georg, Schade, Wolfgang, and Cattin, Philippe C.

Subjects
Computer Science - Robotics
Abstract

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 motion control of such snake-like manipulators necessitates an accurate navigation system that requires no line-of-sight and is immune to electromagnetic noises. Fiber Bragg Grating (FBG) shape sensors, particularly edge-FBGs, are promising tools for this task. However, in edge-FBG sensors, the intensity ratio between Bragg wavelengths carries the strain information that can be affected by undesired bending-related 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 full edge-FBG spectrum and accurately predict the sensor's shape. In this paper, we conduct a more thorough investigation to find a suitable architectural design with lower prediction errors. We use the Hyperband algorithm to search for optimal hyperparameters in two steps. First, we limit the search space to layer settings, where the best-performing configuration gets selected. Then, we modify the search space for tuning the training and loss calculation hyperparameters. We also analyze various data transformations on the input and output variables, as data rescaling can directly influence the model's performance. Moreover, we performed discriminative training using Siamese network architecture that employs two CNNs 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 sensor's shape with a median tip error of 3.11 mm., Comment: 27 pages, 12 figures, preprint

Published
2022
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19. Fiber-Optical 3D Shape Sensing

Author

Waltermann, Christian, Koch, Jan, Angelmahr, Martin, Burgmeier, Jörg, Thiel, Markus, Schade, Wolfgang, Adibi, Ali, Series editor, Asakura, Toshimitsu, Series editor, Rhodes, William T., Editor-in-chief, Hänsch, Theodor W., Series editor, Krausz, Ferenc, Series editor, Monemar, Bo A.J., Series editor, Venghaus, Herbert, Series editor, Weber, Horst, Series editor, Weinfurter, Harald, Series editor, and Marowsky, Gerd, editor

Published
2015
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41. The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs

Author

Roodsari, Samaneh Manavi, Freund, Sara, Angelmahr, Martin, Rauter, Georg, Schade, Wolfgang, Cattin, Philippe C., Roodsari, Samaneh Manavi, Freund, Sara, Angelmahr, Martin, Rauter, Georg, Schade, Wolfgang, and Cattin, Philippe C.

Abstract

Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. However, using low-cost interrogation systems for such intensity-based quasi-distributed sensors introduces further complications to the sensor's signal. Therefore, eccentric FBGs have not been able to accurately estimate complex multi-bend shapes. Here, we present a novel technique to overcome these limitations and provide accurate and precise shape estimation in eccentric FBG sensors. We investigate the most important bending-induced effects in curved optical fibers that are usually eliminated in intensity-based fiber sensors. These effects contain shape deformation information with a higher spatial resolution that we are now able to extract using deep learning techniques. We design a deep learning model based on a convolutional neural network that is trained to predict shapes given the sensor's spectra. We also provide a visual explanation, highlighting wavelength elements whose intensities are more relevant in making shape predictions. These findings imply that deep learning techniques benefit from the bending-induced effects that impact the desired signal in a complex manner. This is the first step toward cheap yet accurate fiber shape sensing solutions., Comment: 18 pages, 5 figures, preprint

Published
2022
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44. Mobilitätsmonitor Nr. 14 – Mai 2022

Author

Scherf, Christian, primary, Bösl, Mareike, primary, Emmerich, Julian, primary, Knie, Andreas, primary, Oehme, Rafael, primary, Ruhrort, Lisa, primary, Schade, Wolfgang, primary, Schmidt, Tabea, primary, and Streif, Marcel, primary

Published
2022
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45. Der Wert des ÖPNV zwischen Gemeinwohl und Kommerz: Wandel der Wertschöpfung durch neue Mobilitätsdienstleistungen

Author

Kollosche, Ingo, Oehme, Rafael, Schade, Wolfgang, Scherf, Christian, Streif, Marcel, and Thomas, Dirk

Subjects
Geschäftsmodell, ddc:330, Canvas, Innovation, Kommune, Nahverkehr
Abstract

Was passiert, wenn der ÖPNV durch neue Mobilitätsdienste wie Fahrzeugsharing oder Ridepooling erweitert wird? Im Fokus dieses Working Papers stehen aktuelle Treiber, aber auch zukünftige Wirkungszusammenhänge, Veränderungspotenziale und Gestaltungsoptionen sowie Folgen für Arbeit und Beschäftigung. Anhand des "Business Model Canvas" wird erläutert, welche Herausforderungen sich für Anbieter, Nachfrager und Rahmenakteure des ÖPNV ergeben.

Published
2022

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