Collaborative Research: Embedded Mechano-Intelligence for Soft Robotics

合作研究:软机器人的嵌入式机械智能

基本信息

项目摘要

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.
这个合作的机器人基础研究(FRR)项目将创建具有集成传感器,互连,逻辑电路和致动器的软材料。这些材料将使软机器人的发展与感知,处理和响应分布在整个结构。这些能力将通过一个软机器人平台来展示,该平台可以根据形状、大小和重量识别和分类简单的物体,只使用这些新材料,而不需要任何额外的传感或计算。这种基于软材料的机械手将是迈向新型软机器人组件的第一步,这些组件可以与环境中的物体进行协调和智能接合,以实现一系列实用目的。这些设备代表了软机器人领域的重要一步,并将广泛推进运动控制系统,自主触觉设备,自我感知传感器-执行器网络等的未来。潜在的影响可能会在社会重要的应用领域,如制造,运输和生物医学设备。该研究将与广泛的推广,教育和指导计划相结合,将研究概念融入课堂和多个不同学生群体的参与活动中。该项目的研究目标是建立软物质中材料和功能成分的基本合成,以体现智能,赋予机器人新的能力,与严重依赖附加硬件的当前系统相比,将显著增强其自主性。 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比目前的系统。 该项目将最终实现一个软机器人分拣机械手,它可以自动检测物品的物理特性,并将这些物品定位在具有类似特征的物体附近。这一目标将通过嵌入式机械智能和场响应聚合物的新型集成来实现。这些组成部分将一起处理关于物品形状和重量的信息,并将触发操纵器的重新配置,以便将物品定位到不同的类别中。通过只需要一个低电压输入功能,嵌入式机械智能只使用必要的计算能力,并消除了传统的控制器和软材料中容易出现故障的电线。场响应聚合物将与弹性稳定性理论的原理结合使用,以最大限度地减少重新配置承重机械手所需的驱动权限。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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Kon-Well Wang其他文献

Dynamic stability analysis of high speed axially moving bands with end curvatures
Discriminative Transition Sequences of Origami Metamaterials for Mechanologic
用于力学的折纸超材料的判别转变序列
  • DOI:
    10.1002/aisy.202200146
  • 发表时间:
    2022-01
  • 期刊:
  • 影响因子:
    7.4
  • 作者:
    Zuolin Liu;Hongbin Fang;Jian Xu;Kon-Well Wang
  • 通讯作者:
    Kon-Well Wang

Kon-Well Wang的其他文献

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{{ truncateString('Kon-Well Wang', 18)}}的其他基金

Collaborative Research: Understanding and Harnessing Complex Dynamics of Coupled Mechanical-Electrical System for an Improved Vibration Energy Harvesting
合作研究:理解和利用耦合机电系统的复杂动力学以改进振动能量收集
  • 批准号:
    1661568
  • 财政年份:
    2017
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant
Collaborative Research: Frequency Selective Structures for High Sensitivity/High Resolution Damage Identification via Impediographic Tomography
合作研究:通过阻抗成像技术进行高灵敏度/高分辨率损伤识别的频率选择结构
  • 批准号:
    1232436
  • 财政年份:
    2012
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant
EFRI-BSBA: Learning from Plants -- Biologically-Inspired Multi-Functional Adaptive Structural Systems
EFRI-BSBA:向植物学习——受生物启发的多功能自适应结构系统
  • 批准号:
    0937323
  • 财政年份:
    2009
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant
SST - Multifunctional Adaptive Piezoelectric Sensory System for Structural Damage Detection
SST - 用于结构损伤检测的多功能自适应压电传感系统
  • 批准号:
    0848166
  • 财政年份:
    2008
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant
SST - Multifunctional Adaptive Piezoelectric Sensory System for Structural Damage Detection
SST - 用于结构损伤检测的多功能自适应压电传感系统
  • 批准号:
    0529029
  • 财政年份:
    2005
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant
ITR: An Agent-Based Negotiation Framework for the Robust Design of Active-Passive Hybrid Piezoelectric Vibration Control Networks
ITR:基于代理的协商框架,用于主动-被动混合压电振动控制网络的鲁棒设计
  • 批准号:
    0218597
  • 财政年份:
    2003
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Continuing Grant
Simultaneous Vibration Confinement and Disturbance Rejection Through Electromechanical Tailoring of Piezoeletric Networks
通过压电网络的机电定制同时限制振动和抑制干扰
  • 批准号:
    0099827
  • 财政年份:
    2001
  • 资助金额:
    $ 45.4万
  • 项目类别:
    Standard Grant

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