  We introduce a novel shape-sensing method using Resistive Flex Sensors (RFS)
embedded in cable-driven Continuum Dexterous Manipulators (CDMs). The RFS is
predominantly sensitive to deformation rather than direct forces, making it a
distinctive tool for shape sensing. The RFS unit we designed is a considerably
less expensive and robust alternative, offering comparable accuracy and
real-time performance to existing shape sensing methods used for the CDMs
proposed for minimally-invasive surgery. Our design allows the RFS to move
along and inside the CDM conforming to its curvature, offering the ability to
capture resistance metrics from various bending positions without the need for
elaborate sensor setups. The RFS unit is calibrated using an overhead camera
and a ResNet machine learning framework. Experiments using a 3D printed
prototype of the CDM achieved an average shape estimation error of 0.968 mm
with a standard error of 0.275 mm. The response time of the model was
approximately 1.16 ms, making real-time shape sensing feasible. While this
preliminary study successfully showed the feasibility of our approach for
C-shape CDM deformations with non-constant curvatures, we are currently
extending the results to show the feasibility for adapting to more complex CDM
configurations such as S-shape created in obstructed environments or in
presence of the external forces.
