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We address the challenge of reliable and accurate proprioception in softrobots, specifically those with tight packaging constraints and relying only oninternally embedded sensors. While various sensing approaches with singlesensors have been tried, often with a constant curvature assumption, we lookinto sensing local deformations at multiple locations of the sensor. In ourapproach, we multi-tap an off-the-shelf resistive sensor by creating multipleelectrical connections onto the resistive layer of the sensor and we insert thesensor into a soft body. This modification allows us to measure changes inresistance at multiple segments throughout the length of the sensor, providingimproved resolution of local deformations in the soft body. These measurementsinform a model based on a finite element method (FEM) that estimates the shapeof the soft body and the magnitude of an external force acting at a knownarbitrary location. Our model-based approach estimates soft body deformationwith approximately 3% average relative error while taking into account internalfluidic actuation. Our estimate of external force disturbance has an 11%relative error within a range of 0 to 5 N. The combined sensing and modelingapproach can be integrated, for instance, into soft manipulation platforms toenable features such as identifying the shape and material properties of anobject being grasped. Such manipulators can benefit from the inherent softnessand compliance while being fully proprioceptive, relying only on embeddedsensing and not on external systems such as motion capture. Such proprioceptionis essential for the deployment of soft robots in real-world scenarios.

Multi-tap Resistive Sensing and FEM Modeling enables Shape and Force Estimation in Soft Robots