Proprioception, the ability to perceive one’s own configuration and
movement in space, enables organisms to safely and accurately interact
with their environment and each other. The underlying sensory nerves
that make this possible are highly dense and use sophisticated
communication pathways to propagate signals from nerves in muscle, skin
and joints to the central nervous system wherein the organism can
process and react to stimuli. In a step forward to realize robots with
such perceptive capability, we propose a flexible sensor framework that
incorporates a novel hybrid modeling strategy, taking advantage of
computational mechanics and machine learning. We implement the sensor
framework on a large, thin and flexible sensor that transforms sparsely
distributed strains into continuous surface shape. Finite element (FE)
analysis is utilized to determine sensor design parameters, while an FE
model is built to enrich the morphological data used in the supervised
training to achieve continuous surface reconstruction. A mapping between
the local strain data and the enriched surface data is subsequently
trained using ensemble learning. This hybrid approach enables real-time,
robust and high-order surface shape reconstruction. The sensing
performance is evaluated in terms of accuracy, repeatability, and
feasibility with numerous scenarios, which has not been demonstrated and
reported on such a large-scale (A4-paper-size) sensor before.
Large‐Scale Surface Shape Sensing with Learning‐Based Computational Mechanics