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March 16, 2026Nature Communications3 citationsOpen Access

Ultraflexible photoelectrical impedance tomography-based imager for 3-axis robotic tactile sensing

KQKun QiaoXFXuyang FengLDLinyu Dong

Key Points

  • This research focuses on developing an ultraflexible imaging system for enhanced robotic tactile sensing.
  • Developed a 5-μm-thick PIT imager with quantum dots and metal-oxide heterojunction layer.
  • Integrated flexible components including light-scattering rubber and LEDs.
  • Utilized Gaussian photocurrent analysis for decoding triaxial forces.
  • Achieved a photo-to-dark-current ratio greater than 10⁴ under UV light.
  • Resolved features as fine as 1.5 mm with high accuracy.
  • Demonstrated a normal force sensitivity of 0.04 kPa⁻¹ and topological recognition accuracy of 96.5%.

Abstract

Vision-based robotic triaxial tactile sensing provides superior spatial resolution and rich multimodal data. However, employing rigid CMOS imagers suffers from limitations in mechanical flexibility and large-area scalability. Here we present a large-area ultraflexible photoelectrical impedance tomography (PIT)-based imager that achieves high-fidelity triaxial tactile sensing. The 5-μm-thick PIT imager incorporates a quantum dots/metal-oxide heterojunction layer with 16 peripheral electrodes, significantly reducing interconnects complexity (pixel-to-interconnect ratio >80). The device exhibits a photo-to-dark-current ratio exceeding 10⁴ under ultraviolet illumination, resolves spatiotemporal features as fine as 1.5 mm, and can simultaneously image up to five occluded regions. By integrating a thin light-scattering porous rubber and flexible LEDs, triaxial force decoding is achieved through Gaussian photocurrent analysis. The system achieves over a dynamic range of 80 kPa with a normal force sensitivity of 0.04 kPa⁻¹, a shear displacement resolution of 0.17 μm kPa⁻¹, and a topological recognition accuracy of 96.5%. We anticipate that this technology will enable advanced applications in industrial and humanoid robotics, medical and rehabilitation robotics, and wearable health monitoring and human-machine interaction systems.

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Cite This Study

Qiao et al. (2026) studied this question.

synapsesocial.com/papers/69b79dce8166e15b153ab0bchttps://doi.org/10.1038/s41467-026-70599-6
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