.png){kind=icon}
返回
Material Recognition Using Robotic Hand With Capacitive Tactile Sensor Array and Machine Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2024.3383886.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Autonomous manipulation using robot hands can benefit from tactile sensing, as it can collect information on variations in applied force and surface properties. This article presents a capacitive tactile sensor placed on the robot's hand fingers. Due to its unique structure and high sensitivity to material permittivity, this sensing system can obtain capacitive data both when a robot finger is approaching an object and when it has touched the object. With three-dimensional reduction methods, that is, principal component analysis (PCA), independent component analysis (ICA), and multidimensional scaling (MDS), a dataset is transformed to be 2-D and then fed into two supervised classifications algorithms, that is, k-nearest neighbors (KNNs) and support vector machines (SVMs). In comparison to previous studies, the MDS-based SVM achieves high material recognition accuracy, up to 98% for recognition of three different material classes, that is, plastic, paper, and glass using capacitance data only. Furthermore, it performs well in recognition of five different materials, that is, dry plastic, plastic with water drops, paper, dry glass, and glass with water drops. The recognition accuracy is as high as 93%. Computational time can be reduced by about 60% by combining the dimension reduction methods with classification algorithms. The results indicate that different material properties can be identified efficiently using the proposed method.
Keyword:
Sensors
Robots
Tactile sensors
Capacitance
Sensor arrays
Plastics
Glass
Capacitive tactile sensor
material recognition
multidimensional scaling (MDS)
support vector machine (SVM)
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
机构
引用论文
Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing基于深度神经网络的大面积机器人触觉电阻抗层析传感方法
OAAI
OAAI分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏A dynamic programming approach to missing data estimation using neural networks使用神经网络进行缺失数据估计的动态规划方法
PREAI
分享收藏分享收藏
查看更多内容