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

这个早期概念探索性研究奖助金(AGER)项目探索了软机器人与常见的颗粒介质(如沙子、土壤或砾石)之间的相互作用。由橡胶或布料等顺应性材料制成的软机器人，在与人和周围使用时，比刚性机器人安全得多。软机器人还因其利用固有的结构顺应性来被动适应未知和意想不到的障碍和地形的能力而引人注目。然而，即使对于在坚硬表面上移动的刚性机器人来说，分析间歇性接触地面的机器人的运动也是困难的，当机器人的结构和地形都可能以鲜为人知的方式变形时，更是如此。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地工作的潜力，构建一个系统的框架来模拟在颗粒介质中或在颗粒介质上运动的软结构的力和运动是至关重要的。这个急切的项目分两个部分创建了这样一个框架。首先是一系列测试，测量与一组标准物体在颗粒状介质中以预定义模式移动相关的力和变形。接下来，使用数学模型来捕捉相互作用的基本特征，然后可以将其扩展到更一般的运动和几何形状。软机器人作为一个新领域正在迅速崛起，它有可能改变医疗保健、搜救、科学探索、矫形和假肢等应用，就像刚性机器人给制造业带来了革命性的变化一样。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，通过创造能够在不确定的地形中可靠地移动的软机器人，用于搜救、勘探、环境监测或建筑。该项目还支持通过加州大学圣地亚哥分校研究成功暑期培训学院(STARS)计划为本科生提供研究体验。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，以及2)开发颗粒材料与软机器人附件之间相互作用的离散元方法(DEM)和阻力理论(RFT)模型。移动机器人的移动面临着复杂的自然底物的挑战，如沙子、落叶、灌木丛和斜坡。移动机器人在真实环境中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，经过经验验证的颗粒模型可以用于设计和控制腿部机器人，以便在非结构化地形上进行有效的运动。然而，这种方法仅针对僵硬的入侵者进行了演示。具有软体和附肢的机器人为机器人的功能提供了新的机会，包括弹性、被动适应和安全交互。移动软机器人可以控制复杂衬底和软附属物之间的局部相互作用，并能够在复杂衬底上移动时对脚部的刚度和形状进行传感和反馈控制。然而，如果没有软体机器人附属物与复杂的自然底物之间相互作用的准确模型，这种潜力就不会实现。这个为期一年的项目的总体目标是能够预测性地了解软入侵者如何与颗粒状材料相互作用，以便在未来的应用中为软机器人的设计和控制提供信息。这些努力将使未来软机器人的设计和控制成为可能。此外，这项工作将引起对颗粒材料流动和失效感兴趣的科学家和工程师的兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.