An End-to-End Grasping Stability Prediction Network for Multiple Sensors

Author:

Shu Xin,Liu Chang,Li TongORCID

Abstract

As we all know, the output of the tactile sensing array on the gripper can be used to predict grasping stability. Some methods utilize traditional tactile features to make the decision and some advanced methods use machine learning or deep learning ways to build a prediction model. While these methods are all limited to the specific sensing array and have two common disadvantages. On the one hand, these models cannot perform well on different sensors. On the other hand, they do not have the ability of inferencing on multiple sensors in an end-to-end manner. Thus, we aim to find the internal relationships among different sensors and inference the grasping stability of multiple sensors in an end-to-end way. In this paper, we propose the MM-CNN (mask multi-head convolutional neural network), which can be utilized to predict the grasping stability on the output of multiple sensors with the weight sharing mechanism. We train this model and evaluate it on our own collected datasets. This model achieves 99.49% and 94.25% prediction accuracy on two different sensing arrays, separately. In addition, we show that our proposed structure is also available for other CNN backbones and can be easily integrated.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference15 articles.

1. Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip;Johansson;Exp. Brain Res.,1987

2. Tactile sensing technologies;Dahiya,2013

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Robot Grasping Stability Prediction Network based on Feature-fusion and Feature-reconstruction of Tactile Information;2022 IEEE International Conference on Mechatronics and Automation (ICMA);2022-08-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3