ArticleSensors (Basel, Switzerland)2021
Texture Recognition Based on Perception Data from a Bionic Tactile Sensor.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Quantitative identification of regular texture spacing via spatiotemporal signal analysis of pressure sensor responses.Frontiers in neuroscience · 2026Article
- Texture and Friction Classification: Optical TacTip vs. Vibrational Piezoeletric and Accelerometer Tactile Sensors.Sensors (Basel, Switzerland) · 2025Article
- A deep neural network for tactile perception in open scenes.iScience · 2025Article
- Multimodal Material Classification Using Visual Attention.Sensors (Basel, Switzerland) · 2024Article
- Deep learning-assisted object recognition with hybrid triboelectric-capacitive tactile sensor.Microsystems & nanoengineering · 2024Article
- Review
- Healing Function for Abraded Fingerprint Ridges in Tactile Texture Sensors.Sensors (Basel, Switzerland) · 2024Article
- Design and Manufacture of Multifunctional 3-D Smart Skins with Embedded Sensor Networks for Robotic Applications.Sensors (Basel, Switzerland) · 2024Article
- Active learning strategies for robotic tactile texture recognition tasks.Frontiers in robotics and AI · 2024Article
- Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition.Sensors (Basel, Switzerland) · 2023Article
- Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.Micromachines · 2023Article
- A Model for Estimating Tactile Sensation by Machine Learning Based on Vibration Information Obtained while Touching an Object.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
Funding
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Abstract
Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.
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