EAGER: Modeling the Interaction Physics between Soft-structures and Granular Materials

EAGER：模拟软结构和颗粒材料之间的相互作用物理

基本信息

批准号：
1837662
负责人：
Nicholas Gravish
金额：
$ 12.46万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2019-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837662&HistoricalAwards=false
关键词：
EAGER Modeling Interaction Physics between

项目摘要

This EArly-concept Grant for Exploratory Research (EAGER) project explores the interaction between soft robots and commonly occurring granular media, like sand, soil, or gravel. Soft robots constructed from compliant materials like rubber or cloth are much safer than rigid robots for use with and around people. Soft robots are also remarkable for their ability to use intrinsic structural compliance to passively adapt to unknown and unexpected obstacles and terrain. Yet analyzing the movements of a robot that makes intermittent contact with the ground is difficult even for rigid robots moving on hard surfaces, and much more so when both the robot structure and the terrain may deform in poorly understood ways. Thus, in order to fully realize the potential of soft robots operating reliably and predictably in unknown natural terrain, it is critical to construct a systematic framework for modeling the forces and movements of soft structures moving in or on granular media. This EAGER project creates such a framework in two parts. First is a sequence of tests that measure the forces and deformations associated with a set of standard objects moving in pre-defined patterns through a granular medium. Next, mathematical models are used to capture the essential features of the interaction, which may then be extended to more general motions and geometries. Soft robotics is rapidly emerging as a new field, with the potential to transform applications such as health care, search and rescue, scientific exploration, and orthotics and prosthetics, much as rigid robots revolutionized manufacturing. The results of this project will help advance the national prosperity and welfare, and secure the national defense, for example, by enabling the creation of soft robots that can move reliably through uncertain terrain for search-and-rescue, exploration, environmental monitoring, or construction. The project also supports providing a research experience to undergraduate students through the UC San Diego Summer Training Academy for research Success (STARS) program.The primary goals of this project are to, 1) develop an experimental system to study the forces and deformation of soft intruders in laboratory granular materials, and 2) develop discrete element method (DEM) and resistive force theory (RFT) models of the interaction between granular material and soft robot appendages. Locomotion of mobile robots is challenged by complex, natural substrates such as sand, leaf-litter, brush, and slopes. Effective movement and control of mobile robots over real-world environments requires study of the failure modes of a model natural substrate granular material. A recent study demonstrated that empirically verified granular models can be used to design and control legged robots for effective locomotion on unstructured terrain. However, this approach has only been demonstrated for rigid intruders. Robots with soft bodies and appendages present new opportunities for robot functionality, including resilience, passive adaptation, and safe interaction. Mobile soft robots have the potential to control the local interactions between complex substrates and soft appendages, and to enable sensing and feedback control of foot stiffness and shape when moving across complex substrates. However, this potential will not be realized without accurate models of the interactions between soft robot appendages and complex, natural substrates. The overarching goal of this one-year project is to enable predictive understanding of how soft intruders interact with granular material to inform soft robot design and control in future applications. These efforts will enable the design and control of future soft robots. Additionally, this work will be of interest to scientists and engineers interested in the flow and failure of granular materials.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.

这项对探索性研究（急切）项目的早期概念赠款探讨了软机器人与通常发生的颗粒介质（如沙子，土壤或砾石）之间的相互作用。由橡胶或布（例如橡胶或布）制成的软机器人比与人一起使用的刚性机器人更安全。软机器人也因其使用固有的结构合规性来被动地适应未知和意外的障碍和地形而引人注目。然而，即使在硬表面上移动的刚性机器人，也很难分析机器人与地面间歇接触的机器人的动作，而且当机器人结构和地形都可能以较知的理解方式变形时。因此，为了充分实现软机器人在未知的自然地形中可靠和可预测的操作的潜力，至关重要的是，构建一个系统的框架，以建模在颗粒介质中或在颗粒介质上移动的软结构的力和运动。这个渴望的项目将分为两个部分创建这样的框架。首先是一系列测试序列，测量与一组与颗粒介质中预定义图案移动的标准对象相关的力和变形。接下来，使用数学模型来捕获相互作用的基本特征，然后可以将其扩展到更通用的运动和几何形状。软机器人技术正在迅速成为一个新领域，具有改变医疗保健，搜救，科学探索以及矫形器和假肢等应用的潜力，就像刚性机器人革命性的制造业一样。该项目的结果将有助于促进国家繁荣和福利，并通过实现可以在不确定的地形上进行搜索，探索，环境监测或建设的不确定地形来实现软机器人来确保国防部。该项目还支持通过UC圣地亚哥夏季培训学院（Stars）计划提供研究经验。移动机器人的运动受到复杂的自然基材（例如沙子，叶子，刷子和斜坡）的挑战。在实际环境中，有效的移动机器人运动和控制需要研究模型自然基材颗粒材料的故障模式。最近的一项研究表明，经验验证的颗粒模型可用于设计和控制腿部机器人，以有效地在非结构化地形上进行运动。但是，仅针对刚性入侵者证明了这种方法。具有软体和附属物的机器人为机器人功能提供了新的机会，包括弹性，被动适应和安全的互动。移动软机器人有可能控制复杂的基板和软附件之间的局部相互作用，并在跨复杂底物移动时对脚部刚度和形状进行感应和反馈控制。但是，如果没有软机器人附属物与复杂的自然基板之间相互作用的准确模型，将无法实现这一潜力。这个为期一年的项目的总体目标是对软入侵者如何与颗粒材料相互作用，以告知软机器人的设计和将来的应用中的颗粒材料。这些努力将使未来软机器人的设计和控制。此外，这项工作将引起对粒状材料流量和失败的科学家和工程师的关注。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。