Multimodal Soft Sensor Design through Single-Material Information Structuring

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.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Hardman, David Samuel

Advisor dc:contributor.advisor

• Iida, Fumiya

• Multimodal
• Robotics
• Sensors

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/eb56039b-2bf1-49e4-80ee-889d24e72b92/download
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.113246

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/375708