Magnetic crack-based piezoinductive mechanical sensors: way to extreme robustness and ultra-sensitivity
Yulian Peng, Zhengyan Wang, Houping Wu, Junchen Luo, Xinxin Chang, Yufeng Wang, Shiwu Zhang, Zhi Hua Feng, Unyong Jeong, Hongbo Wang
- 发表年份
- 2025
- 引用次数
- 6
- 访问权限
- 开放获取
摘要
Soft mechanical sensors with high performance, mechanical robustness, and manufacturing reproducibility are crucial for robotics perception, but simultaneously satisfying these criteria is rarely achieved. Here, we suggest a magnetic crack-based piezoinductive sensor (MC-PIS) which exploits the strain modulation of magnetic flux in cracked ferrite films. The MC-PIS is insensitive to fatigue-induced crack propagation and environmental changes, showing same performance even when scratched in half or run over by a car. It can detect bidirectional bending with a precision of 0.01° from −200° to 327°, allowing for real-time reconstruction of dynamic shape changes of a flexible ribbon. We demonstrate an artificial finger recognizing surface topology and musical notes via vibrations, a crawling robot responding appropriately to external stimuli, a tree-planting gripper performing consecutive tasks from digging soil, removing stones, to placing trees. The MC-PIS opens a new paradigm to develop ultrasensitive yet highly robust sensors in real-world robotics applications. The authors present a magnetic crack-based piezoinductive sensor which detects bidirectional bending with a precision of 0.01° from −200° to 327°, showing maintained performance when submerged in hot and cold water, scratched in half, or run over by a car.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002