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  5. Transparent Soft Robots for Effective Camouflage

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Article
en
2019

Transparent Soft Robots for Effective Camouflage

0 Datasets

0 Files

en
2019
Vol 29 (37)
Vol. 29
DOI: 10.1002/adfm.201901908

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Lei Zhang
Lei Zhang

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Pengcheng Li
Yuzhe Wang
Ujjaval Gupta
+6 more

Abstract

Abstract Transparency is a surprisingly effective method to achieve camouflage and has been widely adapted by natural animals. However, it is challenging to replicate in synthetic systems. Herein, a transparent soft robot is developed, which can achieve effective camouflage. Specifically, this robot is driven by transparent dielectric elastomer actuators (DEAs). Transparent and stretchable conductive polymers, based on blends of poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and water‐borne polyurethane (WPU), are employed as compliant solid‐state electrodes in the DEAs. The electrode exhibits large stretchability, low stiffness, excellent conductivity at large strain, and high transmittance. Consequently, the DEA can achieve a large voltage‐induced area strain of 200% and a high transmittance of 85.5%. Driven by these soft actuators, the robot can realize translations using its asymmetric vibration mode, which can be explained by dynamics analysis and is consistent with finite element modeling. This soft robot can achieve effective camouflage, due to its high transparency as well as thin structure. Furthermore, the robot can become completely flat for even better camouflage by converting its 3D structure to 2D. The transparent soft robot is potentially useful in many applications such as robots for battlefield, reconnaissance, and security surveillance, where effective camouflage is required in dynamic and/or unstructured environments.

How to cite this publication

Pengcheng Li, Yuzhe Wang, Ujjaval Gupta, Jun Liu, Lei Zhang, Donghe Du, Choon Chiang Foo, Jianyong Ouyang, Jian Zhu (2019). Transparent Soft Robots for Effective Camouflage. , 29(37), DOI: https://doi.org/10.1002/adfm.201901908.

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Publication Details

Type

Article

Year

2019

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/adfm.201901908

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