TY - JOUR
T1 - Data-Driven Shape Sensing of a Surgical Continuum Manipulator Using an Uncalibrated Fiber Bragg Grating Sensor
AU - Sefati, Shahriar
AU - Gao, Cong
AU - Iordachita, Iulian
AU - Taylor, Russell H.
AU - Armand, Mehran
N1 - Funding Information:
Manuscript received September 24, 2020; accepted September 27, 2020. Date of publication October 1, 2020; date of current version January 6, 2021. This work was supported in part by the National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering (NIH/NIBIB) under Grant R01EB016703 and in part by the Internal Funds of Johns Hopkins University. The associate editor coordinating the review of this article and approving it for publication was Dr. Anuj K. Sharma. (Corresponding author: Shahriar Sefati.) Shahriar Sefati, Cong Gao, Iulian Iordachita, and Russell H. Taylor are with the Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail: sefati@jhu.edu; cgao11@jhu.edu; iordachita@jhu.edu; rht@jhu.edu).
Publisher Copyright:
© 2020 IEEE.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - This article proposes a data-driven learning-based approach for shape sensing and Distal-end Position Estimation (DPE) of a surgical Continuum Manipulator (CM) in constrained environments using Fiber Bragg Grating (FBG) sensors. The proposed approach uses only the sensory data from an unmodeled uncalibrated sensor embedded in the CM to estimate the shape and DPE. It serves as an alternate to the conventional mechanics-based sensor-model-dependent approach which relies on several sensor and CM geometrical assumptions. Unlike the conventional approach where the shape is reconstructed from proximal to distal end of the device, we propose a reversed approach where the distal-end position is estimated first and given this information, shape is then reconstructed from distal to proximal end. The proposed methodology yields more accurate DPE by avoiding accumulation of integration errors in conventional approaches. We study three data-driven models, namely a linear regression model, a Deep Neural Network (DNN), and a Temporal Neural Network (TNN) and compare DPE and shape reconstruction results. Additionally, we test both approaches (data-driven and model-dependent) against internal and external disturbances to the CM and its environment such as incorporation of flexible medical instruments into the CM and contacts with obstacles in taskspace. Using the data-driven (DNN) and model-dependent approaches, the following max absolute errors are observed for DPE: 0.78 mm and 2.45 mm in free bending motion, 0.11 mm and 3.20 mm with flexible instruments, and 1.22 mm and 3.19 mm with taskspace obstacles, indicating superior performance of the proposed data-driven approach compared to the conventional approaches.
AB - This article proposes a data-driven learning-based approach for shape sensing and Distal-end Position Estimation (DPE) of a surgical Continuum Manipulator (CM) in constrained environments using Fiber Bragg Grating (FBG) sensors. The proposed approach uses only the sensory data from an unmodeled uncalibrated sensor embedded in the CM to estimate the shape and DPE. It serves as an alternate to the conventional mechanics-based sensor-model-dependent approach which relies on several sensor and CM geometrical assumptions. Unlike the conventional approach where the shape is reconstructed from proximal to distal end of the device, we propose a reversed approach where the distal-end position is estimated first and given this information, shape is then reconstructed from distal to proximal end. The proposed methodology yields more accurate DPE by avoiding accumulation of integration errors in conventional approaches. We study three data-driven models, namely a linear regression model, a Deep Neural Network (DNN), and a Temporal Neural Network (TNN) and compare DPE and shape reconstruction results. Additionally, we test both approaches (data-driven and model-dependent) against internal and external disturbances to the CM and its environment such as incorporation of flexible medical instruments into the CM and contacts with obstacles in taskspace. Using the data-driven (DNN) and model-dependent approaches, the following max absolute errors are observed for DPE: 0.78 mm and 2.45 mm in free bending motion, 0.11 mm and 3.20 mm with flexible instruments, and 1.22 mm and 3.19 mm with taskspace obstacles, indicating superior performance of the proposed data-driven approach compared to the conventional approaches.
KW - Deep Neural Networks
KW - Fiber Bragg Grating
KW - Temporal Neural Networks
KW - continuum manipulator
KW - data-driven sensing
KW - shape sensing
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U2 - 10.1109/JSEN.2020.3028208
DO - 10.1109/JSEN.2020.3028208
M3 - Article
AN - SCOPUS:85099518896
SN - 1530-437X
VL - 21
SP - 3066
EP - 3076
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 3
M1 - 9210547
ER -