Simultaneous Visualization of Dynamical and Static Tactile Perception Using Piezoelectric-Ultrasonic Bimodal Electronic Skin Based on <i>In Situ</i> Polarized PVDF–TrFE/2DBP Composites and the TFT Array
Fuyang Wang, Pengfei Yan, Wei Liu, Zhiqiang Li, Zhao Wang, Yong Xiang, Qian Zhang, Xiaoran Hu
- Year
- 2025
- Citations
- 7
Abstract
The key to realizing completed bionic tactile perception of human skin using electronic skin relies on simultaneously distinguishing dynamic and static stimuli and restoring their characteristic information, which is realized by integration of several individual sensors but remains certain limitations including large physical size and high energy consumption. In this study, a piezoelectric-ultrasonic bimodal electronic skin (PUVE) based on in situ polarized PVDF–TrFE/2DBP composites and a thin-film transistor (TFT) array is fabricated. The incorporation of 2DBP into the PVDF–TrFE film and the in situ polarization approach provide excellent piezoelectric and ultrasonic performances of PVDF–TrFE/2DBP composites. PUVE has an ultrahigh sensitivity of 3.2 mV kPa–1 over a wide pressure (0–310 kPa) range, with excellent spatial resolution (50 μm) and response time (40 ms). Meanwhile, the PUVE demonstrated outstanding repeatability and bending stability in 1500 cycles of cyclic pressure and 4000 cycles of 180° bending. The integrated piezoelectric and ultrasonic functions of PUVE can respond individually to dynamic and static tactile stimuli to ensure perceiving and decoupling of the dynamical and static mechanical signals with one single sensor. The PVDF–TrFE/2DBP composites is further integrated with the TFT array, realizing visualization function of contacting objects and restoring their characteristic information including the texture and location. Thus, the PUVE is expected to have a wide range of applications in intelligent robots and human prostheses.
Keywords
Related papers
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham +17 more
2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller +1 more
2013