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: 2239673
• 负责人: Suyi Li
• 金额: $ 27.59万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239673&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.

该项目支持旨在利用弹性多稳定性并改变合规性和连续机器人的设计和动态控制（又称“软机器人”）的基础研究。 生物模拟，材料科学，制造技术和控制理论的持续进展使我们能够建立真正的软机器人，可以在非结构化和动态的任务环境中与人类合作。 这些机器人在救灾工作，最少的侵入性手术和辅助医疗保健方面的传统僵化机器人比传统的刚性机器人要优越，更安全。 但是，软机器人的合规性和连续性，以及它们的严重不足的事实，对有效的动态建模和控制构成了重大挑战。 这项研究将首次系统地研究软机器人中多稳定性的使用来应对这些关键挑战。 多稳定性可以在软机器人的主体中创建“机械智能”，因为它可以协调（或序列）机器人运动并重新配置状态空间而不依赖任何数字控制器。 这样，人们就可以将低级控制任务直接“外包”到机器人体内，并以前所未有的效率和有效性制定一种混合机械数字方法，以进行动态建模和控制。 该项目还将支持不同的教育活动，例如使用折纸折叠机器人作为教学工具，为学生提供科学，技术，工程和数学领域的未来职业。这项研究将制定一个集成的框架，例如动态建模，设计和织物，以通过多稳定性释放上述机械智能的潜力。 研究团队将使用折纸作为物理平台，并完成研究任务的层次结构：1）具有规定和适应性双稳定性的机器人模块建模和制造； 2）具有嵌入式机械智能的机器人组件的设计和验证，以及3）完整机器人的结构，具有机械智能和数字智能之间的接口。 为了完成这些研究任务，研究团队将通过扩展绝对的节点坐标配方，基于多目标优化算法开发设计方法，从而为Bissable折纸模块提供新的动态模型，并使用响应式材料，例如Shape Memory聚合物，以确保机械智能的多功能性和可靠性。 在竞争之后，研究团队将提供演示机器人，以利用杂种机械数字智能进行运动或操纵。 在这些机器人中，机械域中的嵌入式智能执行了较低级别的控制任务（例如运动步态的产生），而高级任务（例如改变运动方向和速度，根据工作环境改变了运动方向和速度）是由传感器和控制者在数字域中通过数字授权而获得的，并反映了NSF的Infort and deem deem and dee eyme deem and dee embor的依据。更广泛的影响审查标准。

项目成果

Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

• DOI: 10.1002/aisy.202300086
• 发表时间: 2023-02
• 期刊: Advanced Intelligent Systems
• 影响因子: 7.4
• 作者: Jun Wang;Suyi Li