4.8 Article

Multifunctional Soft Robotic Finger Based on a Nanoscale Flexible Temperature-Pressure Tactile Sensor for Material Recognition

Journal

ACS APPLIED MATERIALS & INTERFACES
Volume 13, Issue 46, Pages 55756-55765

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c17923

Keywords

nanowire; tactile sensor; soft robotics; flexible sensor; material recognition

Funding

  1. National Natural Science Foundation of China [62001325, 91743110, 52075384, 21861132001]
  2. National Key R&D Program of China [2018YFE0118700]
  3. Tianjin Applied Basic Research and Advanced Technology [17JCJQJC43600]
  4. Foundation for Talent Scientists of Nanchang Institute for Microtechnology of Tianjin University
  5. 111 Project [B07014]

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A multifunctional soft robotic finger with an embedded nanoscale sensor was developed in this study to aid in material recognition through tactile perception, achieving high accuracy through the combination of tactile information and artificial neural networks. The research demonstrates the potential application of this multifunctional soft robotic finger in material recognition.
Robotic hands with tactile perception can perform more advanced and safer operations, such as material recognition. Nanowires with high sensitivity, fast response, and low power consumption are suitable for multifunctional flexible tactile sensors to provide the tactile perception of robotic hands. In this work, we designed a multifunctional soft robotic finger with a built-in nanoscale temperature-pressure tactile sensor for material recognition. The flexible multifunctional tactile sensor integrates a nanowire-based temperature sensor and a conductive sponge pressure sensor to measure the temperature change rate and contact pressure simultaneously. The developed nanoscale temperature and conductive sponge pressure sensor can reach a high sensitivity of 1.196%/degrees C and 13.29%/kPa, respectively. With this multifunctional tactile sensor, the soft finger can quickly recognize four metals within three contact pressure ranges and 13 materials within a high contact pressure range. By combining tactile information and artificial neural networks, the soft finger can recognize the materials precisely with a high recognition accuracy of 92.7 and 95.9%, respectively. This work proves the application potential of the multifunctional soft robot finger in material recognition.

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