变形
计算机科学
软机器人
执行机构
智能材料
领域(数学)
分布式计算
控制工程
纳米技术
人工智能
工程类
材料科学
数学
纯数学
作者
Xiaoyue Ni,Yun Bai,Heling Wang,Yeguang Xue,Yuxin Pan,Jin‐Tae Kim,Xinchen Ni,Tzu‐Li Liu,Yiyuan Yang,Mengdi Han,Yonggang Huang,John A. Rogers
出处
期刊:Research Square - Research Square
日期:2021-12-10
标识
DOI:10.21203/rs.3.rs-1120720/v1
摘要
Abstract Dynamic shape-morphing soft materials systems are ubiquitous in living organisms; they are also of rapidly increasing relevance to emerging technologies in soft machines 1–4 , flexible electronics 5–7 , and smart medicines 8,9 . Soft matter equipped with responsive components can switch between designed shapes or structures, but cannot support the types of dynamic morphing capabilities needed to reproduce natural, continuous processes of interest for many applications 10–27 . Challenges lie in the development of schemes to reprogram target shapes post fabrication, especially when complexities associated with the operating physics and disturbances from the environment can prohibit the use of deterministic theoretical models to guide inverse design and control strategies 3,28–32 . Here, we present a mechanical metasurface constructed from a matrix of filamentary metal traces, driven by reprogrammable, distributed Lorentz forces that follow from passage of electrical currents in the presence of a static magnetic field. The resulting system demonstrates complex, dynamic morphing capabilities with response times within 0.1 s. Implementing an in-situ stereo-imaging feedback strategy with a digitally controlled actuation scheme guided by an optimization algorithm, yields surfaces that can self-evolve into a wide range of 3-dimensional (3D) target shapes with high precision, including an ability to morph against extrinsic or intrinsic perturbations. These concepts support a data-driven approach to the design of dynamic, soft matter, with many unique characteristics.
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