Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/212721
Title: MULTIFUNCTIONAL ROBOTIC MATERIAL DESIGN BY 2D-MATERIALS AND ARTIFICIAL INTELLIGENCE FOR SOFT MACHINES
Authors: YANG HAITAO
Keywords: Robotic Materials, 2D Materials, Soft Robotics, Strain Sensors, Stretchable Electronics, Machine Intelligence
Issue Date: 5-Aug-2021
Citation: YANG HAITAO (2021-08-05). MULTIFUNCTIONAL ROBOTIC MATERIAL DESIGN BY 2D-MATERIALS AND ARTIFICIAL INTELLIGENCE FOR SOFT MACHINES. ScholarBank@NUS Repository.
Abstract: 2D materials (e.g., graphene, MXene) demonstrate superior electrical, mechanical, and chemical properties, which are highly desired for soft machines, enabling them to work in different environments and achieve optimal human-machine interfaces. Nevertheless, the incompatibility between “hard” 2D materials and “soft” matter has been a huge challenge towards full utilization of their intrinsic physicochemical properties in fabricated soft robots. This thesis works on the integration of functional 2D materials into soft matter for fabricating soft robotic materials with required multifunctionality and reconfigurability. Reliable strategies were developed through three aspects: (1) a post-stabilization approach is developed to produce reconfigurable and multifunctional robotic backbones for the fabrication of origami robots with built-in multifunctionality; (2) integrating textured 2D materials onto soft robotic bodies, where the upper layer 2D materials can undergo programmed folding/unfolding processes to mimic skin’s stretchability; (3) utilizing emerging ML tools to realize automatic robotic material design for various soft machines.
URI: https://scholarbank.nus.edu.sg/handle/10635/212721
Appears in Collections:Ph.D Theses (Open)

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