Theoretical and Methodological Foundations for the Design and Implementation of Effective Soft Robots

有效软机器人设计和实现的理论和方法基础

• 批准号: RGPIN-2021-02750
• 负责人: Sedal, Audrey
• 金额: $ 1.97万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760277
• 关键词: Theoretical; Methodological; Foundations; Design; Implementation

The emergence of "soft" robots, whose bodies are made from stretchable and compliant materials, has fundamentally changed the way robotic systems are designed and constructed. Despite societal need for safe, collaborative robotic devices and a proliferation of promising design concepts, soft robots are not yet present in the human-collaborative and human-interactive applications where they might be most useful. The research objective of this proposal is to discover methodologies and principles that solve fundamental problems responsible for the gap between promise and implementation in soft robotics. The proposed research will use a hybrid approach combining continuum mechanics, machine learning, and novel shape measurement techniques to answer open research questions about design and control of soft robots with a variety of architectures deployed in a variety of environments. Soft robots are particularly difficult to design and implement because fundamental understanding of their behaviour does not yet exist. For instance, soft robots have continuously deformable bodies that cannot be modelled with a traditional approach based on rigid-body kinematics. In the short term, this research program will investigate the behaviour of the soft, stretchable and compliant materials and structures that make up the bodies of soft robots. It will follow four objectives: to measure the surface morphology of soft robots, to model soft robot stress relaxation, creep and hysteresis, develop hybridized modelling approaches that benefit from both physical principles and robot data, and investigate computational frameworks for jointly optimizing the design, sensing and control of soft robots. In the long term, the new principles and algorithms discovered in this program will form a methodological basis for resilient soft robots that perform safely and reliably in the real world. The tangible output of this research will be compact, resilient devices capable of sophisticated motions for manufacturing automation, assistive technology, haptics, minimally invasive surgery, and remote navigation of unstructured environments. The proposed research will develop Highly Qualified Personnel (HQP) with "T-shaped" skill sets combining interdisciplinary breadth with particular areas of expertise. Members of this research program will become familiar with mechanical design, theoretical and computational mechanics, machine learning and artificial intelligence, physical experimentation, and other skills that are part of the context of robotics. Principles of Equity, Diversity and Inclusion (EDI) will be central to the search for, selection of, and training of HQP. The findings of this program will form the basis of assistive and collaborative robotic devices that benefit Canadians while bolstering Canada's robotics industry. HQP emerging from this research program will have the expertise and leadership skills to innovate in academic and industrial settings across Canada.

“软”机器人的出现，其身体由可拉伸和顺应性材料制成，从根本上改变了机器人系统的设计和构造方式。尽管社会需要安全，协作的机器人设备和有前途的设计概念的扩散，软机器人还没有出现在人类协作和人类互动的应用程序，他们可能是最有用的。这项建议的研究目标是发现方法和原则，解决负责软机器人的承诺和实现之间的差距的根本问题。 拟议的研究将使用一种混合方法，结合连续介质力学，机器学习和新颖的形状测量技术，以回答有关软机器人设计和控制的开放性研究问题，这些软机器人具有部署在各种环境中的各种架构。软机器人特别难以设计和实现，因为对它们的行为还没有基本的了解。例如，软机器人具有连续可变形的身体，不能用基于刚体运动学的传统方法建模。 在短期内，该研究计划将研究构成软机器人身体的柔软，可拉伸和顺应性材料和结构的行为。它将遵循四个目标：测量软机器人的表面形态，模拟软机器人应力松弛，蠕变和滞后，开发混合建模方法，受益于物理原理和机器人数据，并调查计算框架，共同优化软机器人的设计，传感和控制。从长远来看，该计划中发现的新原理和算法将为在真实的世界中安全可靠地执行弹性软机器人提供方法论基础。这项研究的有形成果将是紧凑、有弹性的设备，能够进行复杂的运动，用于制造自动化、辅助技术、触觉、微创手术和非结构化环境的远程导航。 拟议的研究将开发高素质人才（HQP）与“T形”的技能组合结合跨学科的广度与特定领域的专业知识。该研究计划的成员将熟悉机械设计，理论和计算力学，机器学习和人工智能，物理实验以及其他属于机器人背景的技能。公平、多样性和包容性原则（EDI）将是HQP的搜索、选择和培训的核心。该计划的研究结果将成为辅助和协作机器人设备的基础，使加拿大人受益，同时支持加拿大的机器人产业。HQP从这个研究计划中脱颖而出，将拥有在加拿大学术和工业环境中创新的专业知识和领导技能。