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Updated: Jun 1, 2025

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多功能优雅的降解框架,用于生物灵感的自身感应,多余的软传感器
Taku Sugiyama1, Kyo Kutsuzawa1, Dai Owaki1
1Neuro-robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Frontiers in robotics and AI
|January 21, 2025
概括
这项研究引入了软传感器系统的新框架,即使在传感器退化时,也可以保持精确的自身感知. 该方法通过使用先进的神经网络来处理传感器数据,确保软机器人在现实应用中保持可靠性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 软传感器对于智能软机器人至关重要,但容易受损.
- 在传感器退化期间保持精确的自身感知是一个重大挑战.
- 冗余传感器配置提供部分解决方案,但缺乏一致的可靠性.
研究的目的:
- 开发一种用于冗余软传感器系统优雅降解的新框架.
- 为了提高软机器人的可靠性和强度,在现实世界的应用中.
- 为了提高自身感知准确度,尽管有传感器损坏.
主要方法:
- 提出了一个整合静态长短期记忆 (LSTM) 和时间延迟前神经网络 (TDFNN) 的框架.
- LSTM估计健康的传感器读数进行比较,并识别异常数据.
- 异常的传感器读数被归零,TDFNN通过处理的数据进行自身感知.
主要成果:
- 使用40个传感器肌肉骨腿的模拟表明了框架的有效性.
- 在四个退化场景中,膝盖角度自感知精度保持不变.
- 即使有显著的传感器退化,平均自身感知误差也增加了不到1.91° (1.36%).
结论:
- 拟议的框架大大提高了软传感器自身感应的可靠性.
- 这导致软机器人在不可预测的环境中运行的强度提高.
- 该方法为在传感器损坏的情况下保持软机器人功能提供了可行的解决方案.
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