Neural-driven, active, and reconfigurable mechanical metamaterials (NARMM)

神经驱动、主动和可重构机械超材料 (NARMM)

• 批准号: MR/X035506/1
• 负责人: Antonio Elia Forte
• 金额: $ 203.35万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX035506%2F1
• 关键词: Neural; driven; active; reconfigurable; mechanical

The aim of the fellowship is to deliver the first robotic matter that can shape shift on command based on the instructions it receives.Mechanical metamaterials are engineered materials with mechanical properties defined by their structure rather than their composition. These are usually composed of building blocks (or cells) tessellated in a periodic fashion, which enable countless possibilities in terms of achievable properties. One of these properties is the ability to change shape. Deployable systems, soft robotics and medical devices, all benefit from materials whose shape can be actively controlled. Despite the great advancements in the field, current designs lack the capability of (i) activating individual cells, (ii) reconfiguring their internal structure to mimic multiple shapes, and (iii) undergoing large deformations while being intrinsically safe (i.e., soft) for human interaction. Achieving all these characteristics in a single mechanical metamaterial is indeed a challenging task. NARMM will deliver (4 year) and go beyond this (additional 3 years).The fellowship lays out an ambitious programme designed to investigate and develop robotic matter, based on mechanical metamaterials, that is active and can reconfigure on-command. To this end, I will employ a multidisciplinary strategy that involves mechanical modelling techniques, manufacturing methods, machine learning and, at a later stage, neuroscience.The team will start by investigating manufacturing pathways to create arrays of interconnected soft cells (similar to hollow cubes) that can volumetrically expand when pressurized. Next, we will explore strategies to selectively constrain the expansion of single cells, while others will be free to inflate. These local features will create stiffer fibers and defects, which will govern the global deformation of the robotic matter.In parallel, we will design numerical models to predict the deformation of the matter for different locations of the constraints, and create a database of solutions. We will then train a machine learning model on such databases, to unravel the relationship between the constraints map and the global deformation of the robotic matter. Once this is done, we will be able to provide a 3D target shape through an interactive device (e.g. pc, tablet) and software (e.g. Blender) to the machine learning model, which will identify the optimal constraints map and transmit it to the physical metamaterial to initiate the shape changing.In the long term (+3 years) the team will look into interfacing the robotic matter to respond to the neural signals from human hosts. Using non-invasive electrodes, we will collect electrical neural activity (EEG/EMG) from human volunteers while they perform different tasks. These will be classified into several commands for the robotic matter, which will deform to a target shape and produce mechanical work.The fellowship will benefit from a strong interdisciplinary network of partners and mentors across KCL, MIT, Harvard, Imperial, among others--- the ambition is to deliver a design platform for reconfigurable, soft robotic matter that interfaces and responds to humans, and to explore manufacturing at scale and commercialisation. In the process, we will gain important knowledge about the complex mechanical behaviour of cellular systems and how to create effective constraints at the cell level to govern the global deformation of the matter.The societal impact of NARMM will be enormous. With ~1.1 million people every year affected by stroke (of which 1% with locked-in syndrome), 50K individuals at any time affected by amyotrophic lateral sclerosis in Europe alone, and 60K people with amputation or congenital limb deficiency in the UK, the world needs innovative robotic devices to improve people's lives and support them during the daily tasks. NARMM will establish the first step along many paths, from wearable robots to shape-shifting prosthesis.

该奖学金的目的是提供第一个机器人物质，可以根据它收到的指令根据命令改变形状。机械超材料是工程材料，其机械性能由其结构而不是其成分决定。这些通常由以周期性方式镶嵌的构建块（或单元）组成，这使得在可实现的属性方面有无数的可能性。这些特性之一是改变形状的能力。可部署系统、软机器人和医疗设备都受益于形状可主动控制的材料。尽管在该领域中取得了巨大的进步，但当前的设计缺乏以下能力：（i）激活单个电池，（ii）重新配置它们的内部结构以模仿多种形状，以及（iii）在本质安全的同时经历大的变形（即，软）用于人际互动。在单一机械超材料中实现所有这些特性确实是一项具有挑战性的任务。NARMM将提供（4年）并超越此（额外3年）。该研究金制定了一项雄心勃勃的计划，旨在研究和开发基于机械超材料的机器人物质，该物质是活跃的，可以根据命令进行重新配置。为此，我将采用多学科的策略，涉及机械建模技术，制造方法，机器学习，并在稍后阶段，神经科学。该团队将从研究制造途径开始，以创建相互连接的软细胞阵列（类似于中空立方体），这些软细胞在加压时可以体积膨胀。接下来，我们将探索选择性限制单细胞扩张的策略，而其他细胞则可以自由膨胀。这些局部特征将产生更硬的纤维和缺陷，这将控制机器人物质的全局变形。与此同时，我们将设计数值模型来预测不同约束位置的物质变形，并创建解决方案数据库。然后，我们将在这些数据库上训练机器学习模型，以揭示约束映射与机器人物质的全局变形之间的关系。一旦完成，我们将能够通过交互式设备提供3D目标形状（例如PC、平板电脑）和软件（例如Blender）到机器学习模型，其将识别最佳约束图并将其传输到物理超材料以启动形状改变。（+3年）团队将研究机器人物质的接口，以响应来自人类宿主的神经信号。使用非侵入性电极，我们将收集人类志愿者在执行不同任务时的电神经活动（EEG/EMG）。这些将被分类为机器人物质的几个命令，这些命令将变形为目标形状并产生机械功。该奖学金将受益于KCL，MIT，哈佛，帝国理工学院等强大的跨学科合作伙伴和导师网络-其目标是为可重构的软机器人物质提供一个设计平台，该平台与人类接口并做出响应，并探索大规模生产和商业化。在这个过程中，我们将获得关于细胞系统的复杂力学行为的重要知识，以及如何在细胞水平上创建有效的约束来控制物质的全局变形。NARMM的社会影响将是巨大的。每年约有110万人受到中风的影响（其中1%患有闭锁综合征），仅在欧洲就有5万人随时受到肌萎缩侧索硬化症的影响，英国有6万人截肢或先天性肢体缺陷，世界需要创新的机器人设备来改善人们的生活并在日常工作中支持他们。NARMM将在从可穿戴机器人到变形假肢的许多道路上迈出沿着的第一步。