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  • 硕士生导师
  • 教师英文名称:Longteng Yu
  • 教师拼音名称:yulongteng
  • 电子邮箱:
  • 入职时间:2025-01-17
  • 所在单位:杭州国际创新研究院
  • 办公地点:杭州国际校区科研三号楼3128
  • 性别:
  • 联系方式:0571-28881407
  • 在职信息:在职
  • 毕业院校:新加坡国立大学
  • 学科:生物医学工程
    力学
论文
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Soft microtubular sensors as artificial fingerprints for incipient slip detection
  • 影响因子:5.2
  • DOI码:10.1016/j.measurement.2025.117729
  • 发表刊物:Measurement
  • 关键字:Tactile sensing; Soft robots; Sliding friction; Incipient slip; Adaptive grasping
  • 摘要:Incipient slip detection constitutes a crucial aspect of adaptive grasping and dexterous manipulation in robotics. The primary challenge lies in the subtle nature of incipient slip across temporal, spatial, and force dimensions. This work reports a soft robotic finger capable of accurately detecting incipient slip using artificial fingerprints composed of two piezoresistive microtubular sensors. Experimental results reveal distinctive peak patterns in the sensing signals during incipient slip on smooth and rough surfaces. For smooth surfaces, the direction of slip can be determined by the opposite changing trends in the sensing signals. Finite element analysis elucidates that the underlying mechanisms are driven by the asymmetric local geometry around the sensors when sliding on a smooth surface, and by the relative position of the sensors to the surface micro-structure when sliding on a rough surface. A customized program is then developed for real-time incipient slip detection based on peak recognition in de-noised rolling windows. The feasibility of this method is demonstrated through the adaptive grasping of deformable, moving, and weight-unknown objects using a robotic hand integrated with the soft tactile fingers.
  • 论文类型:期刊论文
  • 论文编号:117729
  • 一级学科:力学
  • 文献类型:期刊
  • 卷号:253
  • 是否译文:
  • 发表时间:2025-05-01
  • 收录刊物:SCI
  • 发布期刊链接:https://doi.org/10.1016/j.measurement.2025.117729