Tactile sensors: A review

Meribout, Mahmoud; Abule Takele, Natnael; Derege, Olyad; Rifiki, Nidal; El Khalil, Mohamed; Tiwari, Varun; Zhong, Jing

doi:10.1016/j.measurement.2024.115332

Measurement

2024

DOI: 10.1016/j.measurement.2024.115332

|View full text |Cite

Tactile sensors: A review

Mahmoud Meribout,

Natnael Abule Takele,

Olyad Derege

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial intelligence assisted nanogenerator applications

Xu,

Manshaii,

Xiao

et al. 2025

J. Mater. Chem. A

View full text Add to dashboard Cite

show abstract

Artificial intelligence assisted nanogenerator applications

Xu,

Manshaii,

Xiao

et al. 2025

J. Mater. Chem. A

View full text Add to dashboard Cite

show abstract

Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception

Zhao,

Zheng,

Chen

2024

Sensors

View full text Add to dashboard Cite

Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing tactile images captured from tactile sensors during interactions, conducting a study on the dynamic stability and instability in operations, and propose a real-time dynamic state sensing network by integrating convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks to capture temporal information. We collect a dataset capturing the entire transition from stable to unstable states during interaction. Employing a sliding window, we sample consecutive frames from the collected dataset and feed them into the network for the state change predictions of robots. The network achieves both real-time temporal sequence prediction at 31.84 ms per inference step and an average classification accuracy of 98.90%. Our experiments demonstrate the network’s robustness, maintaining high accuracy even with previously unseen objects.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Tactile sensors: A review

Cited by 2 publications

References 65 publications

Artificial intelligence assisted nanogenerator applications

Artificial intelligence assisted nanogenerator applications

Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception

Contact Info

Product

Resources

About