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.

从碳纳米管到人类大小的软机器人，柔性和可变形的结构贯穿下一代有前途的工程学科。然而，这些机械系统的仿真通常很慢，并且仿真软件的使用具有挑战性。此外，在常见的机器人研究和教育软件中，几乎没有支持模拟柔性结构，仅限于专家使用智能软机器人。另一方面，计算机图形学社区已经开发出先进的软件来模拟头发和毛皮等柔性结构。最近的研究表明，这些计算机图形学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，用于这些快速的基于物理的柔性结构模拟，并将该软件纳入国家网络基础设施生态系统。这个软件，DiSMech，可以被所有年龄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machines.The目标的突破性设计这项工作的目的是开发一个离散微分几何（DDG）仿真环境到一个广泛可用的软件包，能够建模软和柔性结构。DDG方法能够对组合成任意形状的细长杆和柔性壳进行低维建模，与计算昂贵的有限元分析（FEA）技术相比，建立了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，该软件包适应了以前的工作，以满足国家网络基础设施的标准：可维护性，可扩展性，以及强大的用户界面。接下来，通过将DiSMech集成到现有的机器人软件套件中，为各种柔软灵活的机器人构建虚拟测试平台。项目团队将使用结合了机器学习方法的软件框架来开发运动策略，例如软机器人。最后，DiSMech的附加组件将把机器学习与基于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，以捕获广泛的柔软和灵活的结构，具有足够的计算速度学习机器人控制，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空白。该项目得到了计算机信息科学工程局高级网络基础设施办公室&&和民用部门的支持，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。