Shape sensing of optical fiber Bragg gratings based on deep learning

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 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 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 shape of a 30cm long sensor with a median tip error of 3.11mm in a curvature range of 1.4m−1 to 35.3m−1.

Deep learning-based approach for high spatial resolution fibre shape sensing

“…Each scan was recorded from 800 to 890 nm, comprising 190 wavelength components. The target data are the relative coordinates of 20 discrete points (reflective markers of the tracking system) measured over the length of the shape sensor (more detail on data preprocessing is available in 41 ). This dataset consists of approximately 58,000 samples collected during 30 min of random movement of the fiber sensor.…”