Title: FBG-Based Position Estimation of Highly Deformable Continuum Manipulators: Model-Dependent vs. Data-Driven Approaches,
Abstract: Conventional shape sensing techniques using Fiber Bragg Grating (FBG) involve
finding the curvature at discrete FBG active areas and integrating curvature
over the length of the continuum dexterous manipulator (CDM) for tip position
estimation (TPE). However, due to limited number of sensing locations and many
geometrical assumptions, these methods are prone to large error propagation
especially when the CDM undergoes large deflections. In this paper, we study
the complications of using the conventional TPE methods that are dependent on
sensor model and propose a new data-driven method that overcomes these
challenges. The proposed method consists of a regression model that takes FBG
wavelength raw data as input and directly estimates the CDM's tip position.
This model is pre-operatively (off-line) trained on position information from
optical trackers/cameras (as the ground truth) and it intra-operatively
(on-line) estimates CDM tip position using only the FBG wavelength data. The
method's performance is evaluated on a CDM developed for orthopedic
applications, and the results are compared to conventional model-dependent
methods during large deflection bendings. Mean absolute TPE error (and standard
deviation) of 1.52 (0.67) mm and 0.11 (0.1) mm with maximum absolute errors of
3.63 mm and 0.62 mm for the conventional and the proposed data-driven
techniques were obtained, respectively. These results demonstrate a significant
out-performance of the proposed data-driven approach versus the conventional
estimation technique.