Exploiting Multi-Stability to Enable Mechanical Intelligence for Versatile and Efficient Control of Soft Robotic Locomotion and Manipulation

利用多稳定性实现机械智能，实现软机器人运动和操纵的多功能、高效控制

• 批准号: 1933124
• 负责人: Suyi Li
• 金额: $ 27.59万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933124&HistoricalAwards=false
• 关键词: Exploiting; Multi; Stability; Enable; Mechanical

This project supports the fundamental research that aims to harness elastic multi-stability and transform the design and dynamic control of compliant and continuous robots (aka. "soft robots"). The ongoing advances in bio-mimicry, material science, fabrication technology, and control theory are enabling us to build genuinely soft robots that can collaborate with humans in unstructured and dynamic task environments. These robots are significantly superior and safer than the traditional rigid robots in disaster relief efforts, minimal-invasive surgeries, and assistive healthcare. However, the compliant and continuous nature of soft robots, as well as the fact that they are severely underactuated, imposes significant challenges for effective dynamic modeling and control. This research will, for the first time, systematically examine the use of multi-stability in soft robots to address these critical challenges. Multi-stability can create a "mechanical intelligence" in the body of a soft robot because it can coordinate (or sequence) the robotic motion and re-configure the state-space without relying on any digital controllers. In this way, one can directly "outsource" the low-level control tasks to the robotic body and formulate a hybrid mechanical-digital approach for dynamic modeling and control with unprecedented efficiency and effectiveness. The project will also sup-port different educational activities, such as using origami folded robots as the teaching tool, to in-spire and prepare students for their future career in the Science, Technology, Engineering, and Mathematics fields. This research will formulate an integrated framework, e.g., dynamic modeling, design, and fabrica-tion,- to unleash the potential of the aforementioned mechanical intelligence by multi-stability. The research team will use origami as the physical platform and complete a hierarchy of research tasks: 1) modeling and fabrication of robotic modules with prescribed and adaptive bi-stability; 2) design and validation of robotic components with embedded mechanical intelligence, and 3) con-struction of full robots with an interface between mechanical and digital intelligence. To complete these research tasks, the research team will derive new dynamic models for the bistable origami modules by expanding the absolute nodal coordinate formulation, develop design methodologies based on multi-objective optimization algorithms, and use responsive materials such as shape memory polymers to ensure the versatility and robustness of mechanical intelligence. Upon com-pletion, the research team will deliver demonstration robots that exploit the hybrid mechanical-digital intelligence for locomotion or manipulation. In these robots, the lower-level control tasks (such as locomotion gait generation) are executed by the embedded intelligence in the mechanical domain, while the high-level tasks (such as changing locomotion direction and speed according to the working environment) are achieved by sensors and controllers in the digital domain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目支持基础研究，旨在利用弹性多稳定性，改变柔性和连续机器人的设计和动态控制。“软机器人”)。仿生学、材料科学、制造技术和控制理论的不断进步使我们能够制造出真正的软体机器人，这些机器人可以在非结构化和动态任务环境中与人类合作。这些机器人在救灾、微创手术、辅助医疗等方面明显优于传统的刚性机器人，安全性更高。然而，软机器人的柔顺性和连续性，以及它们严重欠驱动的事实，对有效的动态建模和控制提出了重大挑战。这项研究将首次系统地研究在软机器人中使用多稳定性来解决这些关键挑战。多稳定性可以在软体机器人体内创造一种“机械智能”，因为它可以在不依赖任何数字控制器的情况下协调（或排序）机器人运动并重新配置状态空间。通过这种方式，人们可以直接将低级控制任务“外包”给机器人本体，并制定一种机械-数字混合方法，以前所未有的效率和有效性进行动态建模和控制。该项目还将支持不同的教育活动，例如使用折纸机器人作为教学工具，以激励和帮助学生为未来在科学、技术、工程和数学领域的职业生涯做好准备。本研究将制定一个综合的框架，如动态建模、设计和制造，通过多稳定性释放上述机械智能的潜力。研究团队将以折纸为物理平台，完成以下研究任务：1)具有规定和自适应双稳定性的机器人模块建模和制造；2)设计和验证具有嵌入式机械智能的机器人组件，以及3)构建具有机械智能和数字智能之间接口的完整机器人。为了完成这些研究任务，研究小组将通过扩展绝对节点坐标公式来推导双稳态折纸模块的新动态模型，开发基于多目标优化算法的设计方法，并使用响应材料如形状记忆聚合物来确保机械智能的通用性和鲁棒性。完成后，研究团队将展示利用机械-数字混合智能进行运动或操作的示范机器人。在这些机器人中，较低级的控制任务（如运动步态生成）由机械域的嵌入式智能执行，而高级任务（如根据工作环境改变运动方向和速度）由数字域的传感器和控制器完成。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Switchable structures using asymmetric fiber composite laminates: two case studies

• DOI: 10.1117/12.2612988
• 发表时间: 2022-04
• 作者: Vishrut Deshpande;O. Myers;Suyi Li

TMP origami jumping mechanism with nonlinear stiffness

• DOI: 10.1088/1361-665x/abf5b2
• 发表时间: 2021-04
• 期刊: Smart Materials and Structures
• 影响因子: 4.1
• 作者: Sahand Sadeghi;Sam Allison;Blake Bestill;Suyi Li

Examine the Bending Stiffness of Generalized Kresling Modules for Robotic Manipulation

检查用于机器人操作的广义 Kresling 模块的弯曲刚度

• DOI: 10.1115/detc2020-22187
• 发表时间: 2020
• 期刊: ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Kaufmann, Joshua;Li, Suyi
• 通讯作者: Li, Suyi