Multi-modal deformation and temperature sensing for context-sensitive machines

Robert Baines, Fabio Zuliani, Neil Chennoufi, Sagar Joshi, Rebecca Kramer-Bottiglio and Jamie Paik ()
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Robert Baines: Yale University
Fabio Zuliani: Ecole Polytechnique Fédérale de Lausanne
Neil Chennoufi: Ecole Polytechnique Fédérale de Lausanne
Sagar Joshi: Ecole Polytechnique Fédérale de Lausanne
Rebecca Kramer-Bottiglio: Yale University
Jamie Paik: Ecole Polytechnique Fédérale de Lausanne

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Owing to the remarkable properties of the somatosensory system, human skin compactly perceives myriad forms of physical stimuli with high precision. Machines, conversely, are often equipped with sensory suites constituted of dozens of unique sensors, each made for detecting limited stimuli. Emerging high degree-of-freedom human-robot interfaces and soft robot applications are delimited by the lack of simple, cohesive, and information-dense sensing technologies. Stepping toward biological levels of proprioception, we present a sensing technology capable of decoding omnidirectional bending, compression, stretch, binary changes in temperature, and combinations thereof. This multi-modal deformation and temperature sensor harnesses chromaticity and intensity of light as it travels through patterned elastomer doped with functional dyes. Deformations and temperature shifts augment the light chromaticity and intensity, resulting in a one-to-one mapping between stimulus modes that are sequentially combined and the sensor output. We study the working principle of the sensor via a comprehensive opto-thermo-mechanical assay, and find that the information density provided by a single sensing element permits deciphering rich and diverse human-robot and robot-environmental interactions.

Date: 2023
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DOI: 10.1038/s41467-023-42655-y

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