Collaborative Research: Elements: Discrete Simulation of Flexible Structures and Soft Robots

合作研究：元素：柔性结构和软体机器人的离散仿真

• 批准号: 2209783
• 负责人: Andrew Sabelhaus
• 金额: $ 17万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209783&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; Discrete; Simulation

From carbon nanotubes to human-size soft robots, flexible and deformable structures are present throughout the next generation of promising engineering disciplines. However, simulation of these mechanical systems is often slow, and simulation software is challenging to use. In addition, there is little support for simulating flexible structures in common robotics research and education software, limiting the use of intelligent soft robots to experts only. On the other hand, the computer graphics community has developed advanced software for simulating flexible structures like hair and fur. Recent research has shown these computer graphics approaches can accurately simulate soft robots and flexible structures faster than real-time. This project develops an easy-to-use open-source software platform for these fast physics-based simulations of flexible structures, and incorporates the software into the national cyberinfrastructure ecosystem. This software, DiSMech, can be used by researchers of all ages to investigate the mechanics of slender structures, autonomy for soft robots, and breakthrough designs for deformable machines.The objective of this work is to develop a discrete differential geometry (DDG) simulation environment into a widely-available software package capable of modeling soft and flexible structures. The DDG approach enables low-dimensional modeling of slender rods and flexible shells combined into arbitrary shapes, establishing a practical but still physically accurate contrast to computationally expensive finite element analysis (FEA) techniques. This work first develops a core software package for DiSMech that adapts prior work to meet the standard for national cyberinfrastructure: maintainable, extensible, and with a robust user interface. Next, a virtual testbed for a wide class of soft and flexible robots is built by incorporating DiSMech into an existing robotics software suite. The project team will use the combined software framework with a machine learning approach to develop a locomotion strategy for example soft robots. Finally, add-ons to DiSMech will incorporate machine learning alongside the DDG-based physics models for even faster simulations, demonstrating the research potential for this software in uncovering underlying physical phenomena. By advancing DDG-based physics simulations to capture a wide range of soft and flexible structures, with a computational speed sufficient for learned robot control, all in an easy-to-use interface, DiSMech addresses an important gap in the national cyberinfrastructure.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering and the Divisions of Civil, Mechanical and Manufacturing Innovation and Electrical, Communications and Cyber Systems in the Directorate of Engineering.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.

从碳纳米管到人体大小的软机器人，柔性和可变形的结构存在于下一代有前途的工程学科中。然而，这些机械系统的仿真通常很慢，并且仿真软件的使用也具有挑战性。此外，常见的机器人研究和教育软件很少支持模拟柔性结构，限制了智能软机器人的使用仅限于专家。另一方面，计算机图形学界开发了先进的软件来模拟头发和毛皮等灵活的结构。最近的研究表明，这些计算机图形方法可以比实时更快地准确模拟软机器人和柔性结构。该项目开发了一个易于使用的开源软件平台，用于对柔性结构进行基于物理的快速模拟，并将该软件纳入国家网络基础设施生态系统。该软件 DiSMech 可供各个年龄段的研究人员用来研究细长结构的力学、软机器人的自主性以及可变形机器的突破性设计。这项工作的目标是开发离散微分几何 (DDG) 仿真环境成为一个广泛使用的软件包，能够对柔软且灵活的结构进行建模。 DDG 方法能够对组合成任意形状的细长杆和柔性壳体进行低维建模，与计算成本昂贵的有限元分析 (FEA) 技术建立实用但物理上仍然准确的对比。这项工作首先为 DiSMech 开发了一个核心软件包，该软件包改编了之前的工作以满足国家网络基础设施的标准：可维护、可扩展且具有强大的用户界面。接下来，通过将 DiSMech 合并到现有的机器人软件套件中，构建了适用于各种软体和灵活机器人的虚拟测试台。该项目团队将使用组合软件框架和机器学习方法来开发运动策略，例如软机器人。最后，DiSMech 的附加组件将机器学习与基于 DDG 的物理模型结合起来，以实现更快的模拟，展示了该软件在揭示潜在物理现象方面的研究潜力。通过推进基于 DDG 的物理模拟来捕获各种柔软且灵活的结构，并具有足以进行学习机器人控制的计算速度，所有这些都在一个易于使用的界面中，DiSMech 解决了国家网络基础设施中的一个重要差距。该项目得到计算机与信息科学与工程局高级网络基础设施办公室以及工程局土木、机械和制造创新以及电气、通信和网络系统部门的支持。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。