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  5. Self‐Powered Intelligent Voice Navigation Tactile Pavement Based on High‐Output Hybrid Nanogenerator

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Article
en
2022

Self‐Powered Intelligent Voice Navigation Tactile Pavement Based on High‐Output Hybrid Nanogenerator

0 Datasets

0 Files

en
2022
Vol 7 (11)
Vol. 7
DOI: 10.1002/admt.202200270

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Dongjie Jiang
Minxing Du
Xuecheng Qu
+6 more

Abstract

Abstract Improving the safety and usability of the blind movement is of great significance. The blind navigation system has always been the focus of attention. However, achieving an unconscious interaction and long‐term operation with high navigation accuracy is an urgent challenge. In this study, a distributed self‐powered intelligent voice navigation tactile pavement (SVP) based on a hybrid nanogenerator for blind navigation is reported. More than 4‐s effective output time is achieved under a single instantaneous pressure to the hybrid nanogenerator. The system is integrated with an inertial storage hybrid nanogenerator (ISNG), RF transmitter module, and voice broadcast module. It has the advantages of outstanding navigation accuracy, fatigue resistance (16 000 cycles), temperature stability (−50 to 50 °C), no required operation, and easy fabrication. The SVP may solve the difficulties of GPS navigation delay and lack of map information and realize the accurate identification and feedback of key locations, providing an effective and unconscious interaction navigation strategy for the blind. Integrating the hybrid nanogenerator under the road can provide an energy supply for the construction of the Internet of Things and smart city in the future.

How to cite this publication

Dongjie Jiang, Minxing Du, Xuecheng Qu, Yansong Gai, Wei Sun, Jiangtao Xue, Yusheng Li, Zhou Li, Zhong Lin Wang (2022). Self‐Powered Intelligent Voice Navigation Tactile Pavement Based on High‐Output Hybrid Nanogenerator. , 7(11), DOI: https://doi.org/10.1002/admt.202200270.

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Publication Details

Type

Article

Year

2022

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/admt.202200270

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