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: 1426655
• 负责人: Howard Choset
• 金额: $ 47.54万
• 依托单位: Carnegie-Mellon University
• 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=1426655&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.

我们需要机器人将我们的触角延伸到肮脏和危险的环境中。要做到这一点，移动的机器人必须能够在杂乱的非结构化地形中移动。传统的移动的机器人还没有开始展示栖息在自然地形中的生物体的多功能性。这是因为移动的机器人是在干净的实验室硬地板上制造的，它们的模型主要在实验室硬地板上验证，而生物有机体已经进化到与异质的、肮脏的和不可预测的环境作斗争。 这种真实的世界复杂地形的一个重要例子，尽管它无处不在，但经常被我们的社区忽视，涉及在沙漠，灾难现场，容器和洞穴中常见的松散颗粒材料。因此，创造下一个级别的移动性来穿越肮脏的环境需要机器人和物理学的同时进步，特别是关于与期望行为相关的交互。拟议的工作是建立在几何力学的基础上，颗粒物理学的入侵和生物灵感来自沙漠居住的蛇。我们使用几何力学，这是一个将微分几何原理应用于经典力学问题的领域，为生物启发的机器人设计步态。我们将几何工具的优势应用于粒度环境：即使在这些数学上“混乱”的系统中，我们也可以开始有效地分析步态。这项工作的关键概念是，具有复杂的非线性低层次物理的系统通常表现出更“干净”的高层次运动，通常近似于运动学关系。这种高级运动控制器的开发将有助于我们发现颗粒介质中运动的基本生物学原理。因此，我们将开发计算效率的分析工具，颗粒状材料，并将开发技术来研究系统的运动颗粒状介质的表面。