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基于虚拟现实演示的装配活动实时学习与识别。

Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration.

机构信息

State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Jiangxi Research Institute, Beihang University, Nanchang 330096, China.

出版信息

Sensors (Basel). 2021 Sep 16;21(18):6201. doi: 10.3390/s21186201.

DOI:10.3390/s21186201
PMID:34577409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8472799/
Abstract

Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities demonstration, and using the motion data of the virtual demonstration system, the human demonstration is deduced into an activity sequence that can be performed by the robot. Through experimentation, the virtual reality demonstration system in this paper can achieve a 95% correct rate of activity recognition. We also created a simulated ur5 robotic arm grasping system to reproduce the inferred activity sequence.

摘要

通过人类示范来教授机器人学习是一种自然而直接的方法,而虚拟现实技术是实现快速而真实示范的有效途径。在本文中,我们构建了一个虚拟现实示范系统,该系统使用虚拟现实设备进行装配活动示范,并使用虚拟示范系统的运动数据,将人类示范推导为机器人可以执行的活动序列。通过实验,本文中的虚拟现实示范系统可以实现 95%的活动识别正确率。我们还创建了一个模拟的 ur5 机械臂抓取系统,以重现推断出的活动序列。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/e3da7a18a896/sensors-21-06201-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/2d2119a7929b/sensors-21-06201-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/dc81b2c502ff/sensors-21-06201-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/137289afa0e2/sensors-21-06201-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/70ce28b7a7fa/sensors-21-06201-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/66821132d9e6/sensors-21-06201-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/227c4410577c/sensors-21-06201-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/533a42973288/sensors-21-06201-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/e3da7a18a896/sensors-21-06201-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/2d2119a7929b/sensors-21-06201-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/dc81b2c502ff/sensors-21-06201-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/137289afa0e2/sensors-21-06201-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/70ce28b7a7fa/sensors-21-06201-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/66821132d9e6/sensors-21-06201-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/227c4410577c/sensors-21-06201-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/533a42973288/sensors-21-06201-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d9/8472799/e3da7a18a896/sensors-21-06201-g008.jpg

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本文引用的文献

1
A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19.COVID-19疫情下的一种数字孪生驱动的人机协作装配方法。
J Manuf Syst. 2021 Jul;60:837-851. doi: 10.1016/j.jmsy.2021.02.011. Epub 2021 Feb 25.