使用新型压阻式触觉传感器和 DCNN 进行人形机器人触觉物体识别

Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN.

机构信息

Department of Electronics and Telecommunication Engineering, Rajamangala University of Technology Thanyaburi, Khlong Luang 12110, Thailand.

出版信息

Sensors (Basel). 2021 Sep 8;21(18):6024. doi: 10.3390/s21186024.

DOI:10.3390/s21186024

PMID:34577230

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8473115/

Abstract

A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

摘要

触觉传感器阵列是将物理传感器应用于人形机器人的关键组件。本工作专注于开发一种手掌大小的触觉传感器阵列（56.0mm×56.0mm），以应用于人形机器人手的物体识别。该传感器基于采用压阻原理的 PCB 技术。使用导电聚合物复合材料片作为传感元件，该传感器的矩阵阵列为 16×16 像素。评估了该传感器的灵敏度，并将其安装在机器人手上。使用双三次插值获得的 20 类分辨率增强的触觉图像，用于训练和测试 19 个不同的 DCNN。InceptionResNetV2 提供了 91.82%的准确率，性能优异。然而，使用包括 InceptionResNetV2 和 XceptionNet 的多模态学习方法，实现了 92.73%的最高识别率。此外，当应用物体探索来演示时，识别率得到了提高。