  The safety and accuracy of robotic navigation hold paramount importance,
especially in the realm of soft continuum robotics, where the limitations of
traditional rigid sensors become evident. Encoders, piezoresistive, and
potentiometer sensors often fail to integrate well with the flexible nature of
these robots, adding unwanted bulk and rigidity. To overcome these hurdles, our
study presents a new approach to shape sensing in soft continuum robots through
the use of soft e-textile resistive sensors. This sensor, designed to
flawlessly integrate with the robot's structure, utilizes a resistive material
that adjusts its resistance in response to the robot's movements and
deformations. This adjustment facilitates the capture of multidimensional force
measurements across the soft sensor layers. A deep Convolutional Neural Network
(CNN) is employed to decode the sensor signals, enabling precise estimation of
the robot's shape configuration based on the detailed data from the e-textile
sensor. Our research investigates the efficacy of this e-textile sensor in
determining the curvature parameters of soft continuum robots. The findings are
encouraging, showing that the soft e-textile sensor not only matches but
potentially exceeds the capabilities of traditional rigid sensors in terms of
shape sensing and estimation. This advancement significantly boosts the safety
and efficiency of robotic navigation systems.
