Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot

Tengfei Cui; Jingshen Zhao; Lixing Jin; Quanbin Lai; Jiale Huan; Ye Tian; Xingguang Duan

doi:10.1109/ROBIO55434.2022.10011869

Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot

Tengfei Cui, Jingshen Zhao, Lixing Jin, Quanbin Lai, Jiale Huan, Ye Tian, Xingguang Duan^*

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In the application of industrial production, it is crucial for cooperative robots to avoid collision and retain safety. This paper presents an off-line model to assess the level of collision risk based on BP neural network. A detection device was designed to collect the required data composed of force and displacement, while the risk level was assessed according to the relevant standard with collision force and the calculated collision power. The collected data was used for training of neural network and the off-line model is built. The preliminary experiment is carried out, while the performance and the feasible are verified by the results.

Original language	English
Title of host publication	2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	481-488
Number of pages	8
ISBN (Electronic)	9781665481090
DOIs	https://doi.org/10.1109/ROBIO55434.2022.10011869
Publication status	Published - 2022
Event	2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 - Jinghong, China Duration: 5 Dec 2022 → 9 Dec 2022

Publication series

Name	2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

Conference

Conference	2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
Country/Territory	China
City	Jinghong
Period	5/12/22 → 9/12/22

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10.1109/ROBIO55434.2022.10011869

Cite this

Cui, T., Zhao, J., Jin, L., Lai, Q., Huan, J., Tian, Y., & Duan, X. (2022). Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot. In 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 (pp. 481-488). (2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO55434.2022.10011869

Cui, Tengfei ; Zhao, Jingshen ; Jin, Lixing et al. / Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot. 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 481-488 (2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022).

@inproceedings{c481deac89414b3e87ef6a2cd5404072,

title = "Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot",

abstract = "In the application of industrial production, it is crucial for cooperative robots to avoid collision and retain safety. This paper presents an off-line model to assess the level of collision risk based on BP neural network. A detection device was designed to collect the required data composed of force and displacement, while the risk level was assessed according to the relevant standard with collision force and the calculated collision power. The collected data was used for training of neural network and the off-line model is built. The preliminary experiment is carried out, while the performance and the feasible are verified by the results.",

author = "Tengfei Cui and Jingshen Zhao and Lixing Jin and Quanbin Lai and Jiale Huan and Ye Tian and Xingguang Duan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 ; Conference date: 05-12-2022 Through 09-12-2022",

year = "2022",

doi = "10.1109/ROBIO55434.2022.10011869",

language = "English",

series = "2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "481--488",

booktitle = "2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022",

address = "United States",

}

Cui, T, Zhao, J, Jin, L, Lai, Q, Huan, J, Tian, Y & Duan, X 2022, Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot. in 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022. 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022, Institute of Electrical and Electronics Engineers Inc., pp. 481-488, 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022, Jinghong, China, 5/12/22. https://doi.org/10.1109/ROBIO55434.2022.10011869

Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot. / Cui, Tengfei; Zhao, Jingshen; Jin, Lixing et al.
2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 481-488 (2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot

AU - Cui, Tengfei

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AU - Lai, Quanbin

AU - Huan, Jiale

AU - Tian, Ye

AU - Duan, Xingguang

PY - 2022

Y1 - 2022

N2 - In the application of industrial production, it is crucial for cooperative robots to avoid collision and retain safety. This paper presents an off-line model to assess the level of collision risk based on BP neural network. A detection device was designed to collect the required data composed of force and displacement, while the risk level was assessed according to the relevant standard with collision force and the calculated collision power. The collected data was used for training of neural network and the off-line model is built. The preliminary experiment is carried out, while the performance and the feasible are verified by the results.

AB - In the application of industrial production, it is crucial for cooperative robots to avoid collision and retain safety. This paper presents an off-line model to assess the level of collision risk based on BP neural network. A detection device was designed to collect the required data composed of force and displacement, while the risk level was assessed according to the relevant standard with collision force and the calculated collision power. The collected data was used for training of neural network and the off-line model is built. The preliminary experiment is carried out, while the performance and the feasible are verified by the results.

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DO - 10.1109/ROBIO55434.2022.10011869

M3 - Conference contribution

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T3 - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

SP - 481

EP - 488

BT - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

Y2 - 5 December 2022 through 9 December 2022

ER -

Cui T, Zhao J, Jin L, Lai Q, Huan J, Tian Y et al. Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot. In 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 481-488. (2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022). doi: 10.1109/ROBIO55434.2022.10011869

Data-Driven Model of Off-Line Collision Safety Based on Neural Network for Cooperative Robot

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