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Multimodal Soft Sensor Design through Single-Material Information Structuring https://doi.org/10.17863/CAM.113246 Hardman, David Samuel Iida, Fumiya Human skin provides flexibility, compliance, pressure sensing, temperature sensing, and stimulus localisation while also being able to detect and heal damages. As technologies move towards the design of increasingly general-purpose robots, we require soft sensors which replicate these essential properties without sacrificing robustness, longevity, or straightforward fabrication. This thesis proposes a design framework in which multimodal soft sensors are considered as information-generating systems, the outputs of which can be structured through material, morphology, and electrical excitation co-design. The effects of each are experimentally explored, with the ultimate goal of single-material soft multimodal sensor fabrication. Their outputs are analysed through the learning-based processing of large physically-collected datasets, using architectures capable of adapting to the nonlinear responses of functional materials such as self-healing polymers. In doing so, numerous novel contributions are demonstrated: designing at the material level yields a 3D-printable sensorised hydrogel which responds to various multimodal stimuli; morphology explorations lead to a temperature & strain-sensitive membrane inspired by the distribution of receptors in the human skin; and multiplexed electrical excitations enable the sensorisation of a full-size 3D hand using electrodes positioned around its wrist. The thesis's experimental work culminates in the demonstration of a single-material multimodal soft skin, which holds great potential for the implementation of soft robotic sensors: the final chapter explores the vast future directions expected to stem from this work. University of Cambridge 2024-06-04 Thesis Doctoral Doctor of Philosophy (PhD) eng https://www.repository.cam.ac.uk/handle/1810/375708 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7dbe8302-43bf-4e40-a904-10658ab2d5dc/download 0ad9db03ccc8a90a50d368c88f8a90bd https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/eb56039b-2bf1-49e4-80ee-889d24e72b92/download 87eda9de84448d1f82354d60eee3eb5f https://creativecommons.org/licenses/by/4.0/ Multimodal Robotics Sensors