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多个自动供电的传感器集成移动操纵器用于智能环境检测.

Yuhang Xue1, Jun Duan1, Wenjing Liu1

  • 1Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China.

ACS applied materials & interfaces
|August 5, 2024
PubMed
概括

这项研究介绍了一种自动供电的移动操纵器,配有用于探索的集成传感器. 它使用机器学习进行准确的环境传感和直观的手势控制,克服能源和界面的限制.

关键词:
基于TENG的自动供电传感器收集环境信息 收集环境信息机器学习辅助的监控系统系统.多传感器系统多传感器系统无线手势控制无线手势控制

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科学领域:

  • 机器人和自动化 机器人和自动化
  • 传感器技术 传感器技术
  • 收集能源 收集能源

背景情况:

  • 现有的勘探设备面临着连续功率,多样化的传感和用户友好的界面的限制.
  • 在具有挑战性的环境中,对自主和适应性系统的需求正在增加.

研究的目的:

  • 开发一个自动供电的移动操纵系统 (MSIMM),解决当前勘探设备的局限性.
  • 为移动探索平台增强传感器数据的可用性和人机交互.

主要方法:

  • 集成基于 triboelectric纳米发电机 (TENG) 的自动供电传感器与生物操纵器和无线手势控制.
  • 使用追踪车辆平台,传感器手套和移动应用程序进行直观的控制和数据采集.
  • 采用机器学习,特别是卷积神经网络,用于信号分类和环境监测.

主要成果:

  • 在环境刺激方面,实现了超过94%的整体信号识别和分类准确度.
  • 单个传感器的高精度:压力为100%,角度为99.55%,材料,滴滴,温度和加速度传感器超过94%.
  • 该MSIMM系统有效地整合了自动传感,机器人操纵和直观控制,以增强探索能力.

结论:

  • 拟议的MSIMM系统为能源独立和多功能环境勘探提供了一个可行的解决方案.
  • 结合TENG传感器,机器学习和手势控制,可显著提高移动探索机器人的性能和可用性.
  • 这项技术有可能在各种领域推进自主传感和交互,包括环境监测和危险地点勘探.