Toward Soft Optical Networks for Deformation Sensing

Biological systems achieve great efficiency with tactile sensing by multiplexing receptors along shared afferent pathways, whereas soft robots, on the other hand, still rely on bulky, one-to-one sensor routing. To introduce this capability to soft-engineered systems, we present a soft optical network that utilizes input-agnostic junctions to multiplex time-of-flight signals. By connecting elastomeric waveguides into a network, we consolidate multiple sensory zones into a single shared afferent channel. We developed a curve-fitting optimization approach to deconvolve overlapping time-of-flight signals. The amplitude and arrival time of those signals convey information about the deformation state of each of the sensor paths in the robot. Testing with a prototype incorporating hard optical junctions demonstrates that the system disambiguates press and stretch deformations across four sensing zones. The prototype network produced 3 distinct signal peaks associated with the different zones. While each zone behaved differently, the system could generally detect changes at 20-gram increments of pressing, and signal peaks 1 and 3 showed sensitivity to stretching. Finally, we discuss an open-face molding approach for fabricating fully soft junctions, and describe its limitations, providing insights to guide the future development of fully soft, monolithic, proprioceptive, and exteroceptive networks.