  We address the challenge of reliable and accurate proprioception in soft
robots, specifically those with tight packaging constraints and relying only on
internally embedded sensors. While various sensing approaches with single
sensors have been tried, often with a constant curvature assumption, we look
into sensing local deformations at multiple locations of the sensor. In our
approach, we multi-tap an off-the-shelf resistive sensor by creating multiple
electrical connections onto the resistive layer of the sensor and we insert the
sensor into a soft body. This modification allows us to measure changes in
resistance at multiple segments throughout the length of the sensor, providing
improved resolution of local deformations in the soft body. These measurements
inform a model based on a finite element method (FEM) that estimates the shape
of the soft body and the magnitude of an external force acting at a known
arbitrary location. Our model-based approach estimates soft body deformation
with approximately 3% average relative error while taking into account internal
fluidic actuation. Our estimate of external force disturbance has an 11%
relative error within a range of 0 to 5 N. The combined sensing and modeling
approach can be integrated, for instance, into soft manipulation platforms to
enable features such as identifying the shape and material properties of an
object being grasped. Such manipulators can benefit from the inherent softness
and compliance while being fully proprioceptive, relying only on embedded
sensing and not on external systems such as motion capture. Such proprioception
is essential for the deployment of soft robots in real-world scenarios.
