NRI: Collaborative Research: Exploiting Granular Mechanics to Enable Robotic Locomotion

NRI：合作研究：利用颗粒力学实现机器人运动

• 批准号: 1426443
• 负责人: Daniel Goldman
• 金额: $ 36万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426443&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Exploiting; Granular

We need robots to extend our reach into dirty and dangerous environments. To do so, mobile robots must be able to locomote in messy unstructured terrains. Conventional mobile robots have not begun to display the multi-functionality of organisms that inhabit natural terrains. This is because mobile robots have been created in, and their models mainly validated on, clean hard laboratory floors, whereas biological organisms have evolved to contend with heterogeneous, dirty and unpredictable environments. One important example of such real world complex terrain, often overlooked by our community despite its ubiquity, involves loose granular materials commonly found in deserts, disaster sites, containers, and caves. Therefore creation of the next level of mobility to traverse dirty environments requires simultaneous advances in both robotics and physics, particularly regarding the interactions associated with desired behaviors. The proposed work is built on a foundation of geometric mechanics, granular physics of intrusion and biological inspiration from desert-dwelling snakes. We use geometric mechanics, a field that applies principles from differential geometry to problems in classical mechanics, to design gaits for biologically inspired robots. We bring the benefits of the geometric tools to bear on granular environments: in even these mathematically "messy" systems, we can begin to efficiently analyze gaits. The key concept in this effort is that systems with complicated, nonlinear low-level physics often exhibit much "cleaner" high-level motion, often approximated by a kinematic relationship. Development of such high-level motion controllers will be aided by our ability to discover basic biological principles of locomotion in granular media. We will therefore develop computationally efficient analysis tools for granular materials and will develop techniques to study the locomotion of systems on the surface of granular media.

我们需要机器人将覆盖范围扩展到肮脏和危险的环境中。为此，移动机器人必须能够在凌乱的非结构化地形中机动。传统的移动机器人尚未开始显示居住在自然地形的生物的多功能性。这是因为已经创建了移动机器人，并且它们的模型主要在干净的硬实验室地板上得到验证，而生物生物体已经演变为与异构，肮脏和不可预测的环境抗衡。 这种现实世界中复杂地形的一个重要例子，尽管它无处不在，但经常被我们社区忽视，涉及在沙漠，灾难遗址，容器和洞穴中常见的颗粒状材料。因此，创建下一级别的移动性到遍历肮脏的环境需要同时在机器人技术和物理学上取得进步，尤其是与所需行为相关的相互作用。拟议的工作建立在几何力学，入侵的颗粒状物理学以及来自居住在沙漠居民蛇的生物学灵感的基础上。我们使用从差异几何形状到经典力学问题的原理的几何力学，该领域为生物学启发的机器人设计步态。我们带来了几何工具在颗粒环境上的好处：即使在这些数学上的“凌乱”系统中，我们也可以开始有效地分析步态。这项工作的关键概念是，具有复杂，非线性低水平物理学的系统通常表现出很多“清洁”的高级运动，通常是通过运动学关系近似的。我们有能力在颗粒介质中发现运动的基本生物学原理的能力将有助于这种高级运动控制器的发展。因此，我们将开发用于颗粒材料的计算高效分析工具，并将开发技术以研究颗粒培养基表面的系统运动。