基于卷积神经网络的FBG触觉感知形状识别的研究
Guan Lu1, Zhihui Shen1, Ting Cai1
1School of Mechanical Engineering, Nantong University, Nantong 226000, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
一个新的纤维布拉格格 (FBG) 触觉传感阵列与卷积神经网络 (CNN) 结合,显著改善了机器人的形状识别. 这种方法在灵活的皮肤应用中实现了高精度.
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:
- 机器人的感知很大程度上依赖于形状识别.
- 现有的灵活皮肤纤维触觉传感器在效率和识别类型方面存在局限性.
研究的目的:
- 提出和评估基于卷积神经网络 (CNN) 的形状识别方法,使用纤维布拉格格 (FBG) 触觉传感阵列.
- 为了提高形状识别能力,在机器人应用中实现灵活的触觉感应.
主要方法:
- 使用柔性树脂和3D打印制造触觉传感器阵列.
- 构建一个形状识别系统来收集触觉数据.
- 使用CNN,随机森林,支持向量机和K-最近邻近算法进行形状分类.
主要成果:
- FBG触觉传感阵列表现出良好的灵敏度和感知能力.
- 美国有线电视新闻网 (CNN) 实现了96.58%的形状识别精度,超过了其他方法.
- 美国有线电视新闻网 (CNN) 的F1得分为96.95%,也优于比较算法.
结论:
- 拟议的基于CNN的FBG触觉传感阵列为灵活的机器人皮肤中的形状识别提供了强大的解决方案.
- 这项研究为触觉感知系统中先进的形状感知提供了实验基础.
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