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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.

这个早期概念探索性研究资助（EAGER）项目探索了软机器人与常见颗粒介质（如沙子，土壤或砾石）之间的相互作用。由橡胶或布等柔性材料制成的软机器人比刚性机器人更安全，可以与人一起使用。软机器人还具有利用固有结构顺应性被动适应未知和意外障碍物和地形的能力。然而，即使对于在坚硬表面上移动的刚性机器人，也很难分析与地面间歇接触的机器人的运动，当机器人结构和地形都可能以不太清楚的方式变形时，情况就更是如此了。因此，为了充分发挥软机器人在未知自然地形中可靠和可预测地运行的潜力，构建一个系统的框架来模拟软结构在颗粒介质中或颗粒介质上移动的力和运动是至关重要的。这个EAGER项目创建了这样一个框架，分为两个部分。首先是一系列的测试，测量与一组标准物体在预定义的模式下移动通过颗粒介质相关的力和变形。接下来，数学模型被用来捕捉的相互作用，然后可以扩展到更一般的运动和几何形状的基本特征。软机器人正在迅速成为一个新的领域，有可能改变医疗保健，搜索和救援，科学探索，矫形和假肢等应用，就像刚性机器人彻底改变了制造业一样。该项目的成果将有助于促进国家的繁荣和福利，并确保国防安全，例如，通过创造能够可靠地通过不确定地形进行搜索和救援，勘探，环境监测或建设的软机器人。该项目还支持通过加州大学圣地亚哥分校夏季培训学院的研究成功（STARS）计划为本科生提供研究经验。该项目的主要目标是，1）开发一个实验系统来研究实验室颗粒材料中软入侵者的力和变形，2）建立了颗粒材料与机器人柔性附件相互作用的离散元法（DEM）和阻力理论（RFT）模型。移动的机器人的运动受到复杂的自然基质的挑战，如沙子、落叶、灌木和斜坡。真实世界环境中的移动的机器人的有效移动和控制需要研究模型天然基质颗粒材料的失效模式。最近的一项研究表明，经验验证的颗粒模型可以用来设计和控制腿式机器人有效的运动在非结构化的地形。然而，这种方法只被证明是刚性入侵者。具有柔软身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，被动适应和安全交互。移动的软机器人具有控制复杂基底和软附属物之间的局部相互作用的潜力，并且当在复杂基底上移动时，能够感测和反馈控制足部刚度和形状。然而，这种潜力将无法实现没有精确的模型之间的相互作用的软机器人附件和复杂的，自然基板。这个为期一年的项目的总体目标是预测性地了解软入侵者如何与颗粒材料相互作用，以告知未来应用中的软机器人设计和控制。这些努力将使未来软机器人的设计和控制成为可能。此外，这项工作将是感兴趣的科学家和工程师在流动和颗粒材料的故障。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。