Collaborative Research: Embedded Mechano-Intelligence for Soft Robotics

合作研究：软机器人的嵌入式机械智能

• 批准号: 2314560
• 负责人: Kon-Well Wang
• 金额: $ 45.4万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314560&HistoricalAwards=false
• 关键词: Collaborative; Research; Embedded; Mechano; Intelligence

This collaborative Foundational Research in Robotics (FRR) project will create soft materials with integrated sensors, interconnections, logic circuits, and actuators. These materials will enable the development of soft robots with perception, processing, and responsiveness distributed throughout their structures. These capabilities will be demonstrated by a soft robotic platform that can recognize and sort simple objects by their shape, size, and weight, using only these novel materials and without requiring any additional sensing or computing. This soft material-based manipulator will be the first step towards a new class of soft robotic components that can coordinate and intelligently engage with objects in their environment, for a range of practical purposes. Such devices represent a major step for the field of soft robotics and will broadly advance the future of motion control systems, autonomous haptic devices, self-aware sensor-actuator networks, and more. Potential impacts may be felt in societally important application areas such as manufacturing, transportation, and biomedical devices. The research will be coupled with an extensive outreach, education, and mentoring program that integrates the research concepts into classroom and engagement activities among multiple diverse student groups.The research goal of this project is to establish a fundamental synthesis of material and functional components in soft matter to embody intelligence, endowing robots with new capabilities that will significantly enhance their autonomy as compared to the current systems that heavily depend on add-on hardware. The new system will require less electric power and have faster reactions and better survivability than current systems. This project will culminate in a soft robotic sorting manipulator that autonomously detects physical characteristics of items and positions those items into proximity with objects having similar features. This goal will be achieved by a novel integration of embedded mechano-intelligence and field-responsive polymers. Together, these constituents will process information regarding item shape and weight and will trigger reconfiguration of the manipulator so as to position the items into distinct categories. By requiring only a low-voltage input to function, the embedded mechano-intelligence employs only the necessary computational power and eliminates conventional controllers and failure-prone electrical wiring in soft materials. The field-responsive polymers will be used in conjunction with principles of elastic stability theory to minimize the actuating authority required to reconfigure the load-bearing manipulator.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.

该机器人技术（FRR）项目的合作基础研究将与集成传感器，互连，逻辑电路和执行器创建软材料。这些材料将通过感知，加工和响应能力在其整个结构中分发。这些功能将通过一个软机器人平台来证明，该平台只能使用这些新颖的材料，而无需任何其他感应或计算，可以通过其形状，大小和重量来识别和对简单对象进行排序。这种基于材料的柔软的操纵器将是迈向新的软机器人组件的第一步，这些组件可以用于一系列实际目的，它们可以协调并智能地与环境中的物体互动。此类设备代表了软机器人技术领域的主要步骤，并将广泛推动运动控制系统，自主触觉设备，自我意识的传感器传感器网络等的未来。在社会上重要的应用领域（例如制造，运输和生物医学设备）中，可能会产生潜在的影响。这项研究将与广泛的外展，教育和指导计划相结合，将研究概念整合到多种多样的学生群体之间。该项目的研究目标是建立对软物质中材料和功能组成的基本综合，以将智能的智能体现，从而使机器人与新的能力相比，将其依赖于当前的系统依赖于当前的系统。 与当前系统相比，新系统将需要更少的电力，并具有更快的反应和更好的生存能力。 该项目将以软机器人分类操纵器的形式达到高潮，该操作器自主检测物品的物理特征，并将这些项目与具有相似特征的对象定位在附近。该目标将通过嵌入的机械智能和田间响应聚合物的新型整合来实现。这些成分将共同处理有关项目形状和重量的信息，并将触发操纵器的重新配置，从而将项目定位为不同的类别。通过仅需要低压输入才能功能，嵌入式的机械智能仅采用必要的计算功率，并消除了柔软材料中的常规控制器和容易失败的电线。现场响应性聚合物将与弹性稳定理论的原理结合使用，以最大程度地减少重新配置负载操纵器所需的执行权限。该奖项反映了NSF的法定任务，并认为通过使用基金会的知识分子和更广泛的影响来审查Criteria的评估，并被视为值得通过评估的支持。