Modeling and Fabrication of Scalable Tactile Sensor Arrays for Flexible Robot Skins

Saadatzi, Mohammad Nasser; Baptist, Joshua R.; Zhong, Yi; Popa, Dan O.

doi:10.1109/jsen.2019.2915362

Cited by 29 publications

(10 citation statements)

References 56 publications

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“…77 Thanks to its stretchability, it has already been widely used in wearable devices and electronic skins. 78 To increase the range of applications of conductive polymers in intelligent wearables, improvements with respect to processing and preparation are needed.…”

Section: Suitable Flexible Materialsmentioning

confidence: 99%

Recent Progress of Wearable Piezoelectric Nanogenerators

Zhang

Fan

Wang

et al. 2021

ACS Appl. Electron. Mater.

109

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Considering the limited supply of fossil fuel, pollution, problems with chemical batteries, and the many potential applications of wearable electronics, the development of more effective lightweight energy sources is important. Piezoelectric nanogenerators (PENGs) can convert a mechanical energy into an electric energy and sometimes may even have the potential to replace conventional chemical batteries. The combination of conventional textiles with PENGs leads to so-called "smart textiles", in other words, textile-based PENGs. More generally, textile-based PENGs can endow conventional textiles with special functionalities such as energy conversion and online health testing (using sensors), while the used conventional textiles can provide platforms for their deployment. This paper reviews the research progress of textile-based PENGs, discusses the classification of piezoelectric materials and electrode materials, and summarizes ways to improve their piezoelectric performance. In addition, several preparation methods of different device structures of textile-based PENGs are compared. Finally, the most important obstacles for the application of textile-based PENGs are addressed at the end of this paper. This compact but relatively comprehensive review will not only aid researchers to better understand textile-based PENGs but also attract the attention of related fields and perhaps inspire more exciting research and progress of wearable textile-based PENGs.

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Section: Suitable Flexible Materialsmentioning

confidence: 99%

Recent Progress of Wearable Piezoelectric Nanogenerators

Zhang

Fan

Wang

et al. 2021

ACS Appl. Electron. Mater.

109

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show abstract

“…The authors have selected N frames from the several-minute recordings by either random selection or clustering to construct the input samples to a neural network for object classification. The dataset is publicly available 2 and we name it MIT-STAG dataset in this paper for clarity.…”

Section: B Related Datasetmentioning

confidence: 99%

Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications

Wang¹,

Geiger²,

Niculescu³

et al. 2022

Preprint

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“…For human beings, tactile feedback is the second most important signal receptor after vision, which plays an important role in life. With the development of tactile sensor technology [209][210][211][212][213], researchers hope that robots can also have the same tactile perception ability as humans and further realize intelligence. It is a good idea to apply tactile technology to the robot alone, which can avoid the fusion of different signals and improve the processing speed of the system.…”

Section: A Tactile Feedbackmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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Modeling and Fabrication of Scalable Tactile Sensor Arrays for Flexible Robot Skins

Cited by 29 publications

References 56 publications

Recent Progress of Wearable Piezoelectric Nanogenerators

Recent Progress of Wearable Piezoelectric Nanogenerators

Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

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