NRI: Small: Compliant Multifunctional Robotic Structures for Safety and Communication by Touch

这项研究的目的是通过使用新的顺应性多功能结构使机器人能够通过人类触摸进行物理通信，从而更好地训练机器人。 为了实现这一目标，将开发导电聚合物阵列来形成一个类似于人类神经系统的系统，可以感知形状和力分布。该传感器阵列将使用可扩展的增材制造工艺集成到复合泡沫结构中。 为了支持模型的开发，并作为机器人平台上这些多功能结构的概念验证，将使用与不同顺应性的物体相互作用的机器人手臂进行模拟合作机器人实验。 相关接触力学的实验细节将使用数字图像相关和传统视频成像进行实时量化。 然后，从传感器阵列的输出将有关的形状和力的分布，通过求解非线性逆问题，使用一种新的奇异值分解方法。研究结果将被记录和传播，实验将转化为STEM演示，旨在教育年轻女孩。这项研究将导致新的兼容性，可扩展性，传感结构，同时实时监测全局和局部形状，以及力的分布。 由于顺应性多功能传感结构还不存在的机器人，据设想，所提出的工作将使实现新的生物启发的控制原则，训练机器人。 这将通过区分机器人与人类的交互和环境中的其他物体，大大提高合作机器人进行更安全交互和决策的能力。 拟议的研究和教育整合将培养新的机械工程师，以创造多功能产品，使新产品和新功能在现有产品的关键领域，如医疗保健。 新的制造方法将使这些结构能够以具有成本竞争力的方式在美国制造，从而增加就业。

The objective of this research is to enable better training of robots by enabling them to physically communicate via human touch using new compliant multifunctional structures. To achieve this, arrays of conducting polymers will be developed to form a system similar to the human nervous system that can sense shape and forces distributions. This sensor array will be integrated into composite foam structures using a scalable additive manufacturing process. To support development of models and to serve as proof-of-concept for these multifunctional structures on robotic platforms, simulated co-robotics experiments will be conducted using a robotic arm interacting with objects of varying compliance. Experimental details of the associated contact mechanics will be quantified in real-time using Digital Image Correlation and conventional video imaging. Output from the sensor array will then be related to shape and force distributions by solving the nonlinear inverse problem using a novel Singular Value Decomposition method. Research results will be documented and disseminated, and the experiments will be converted to STEM demonstrations targeted at educating young girls.This research will lead to new compliant, scalable, sensing structures that simultaneously monitor in real-time both global and local shapes, as well as force distributions. Since compliant multifunctional sensing structures do not yet exist for robots, it is envisioned that the proposed work will enable realization of new bio-inspired control principles for training robots. This will significantly advance the ability to make safer interactions and decisions in co-robotics by differentiating robotic interactions with humans from other objects in their environment. The proposed integration of research and education will train new mechanical engineers to create multifunctional products that enable new products and new capabilities in existing products in critical areas such as healthcare. The new fabrication methods will enable these structures to be manufactured in the United States in a cost-competitive manner, increasing employment.