Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms

Long Jin; Shuai Li; Xin Luo; Ming Sheng Shang

doi:10.1109/IJCNN.2017.7966382

Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms

Long Jin, Shuai Li, Xin Luo, Ming Sheng Shang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

Original language	English
Title of host publication	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4165-4170
Number of pages	6
ISBN (Electronic)	9781509061815
DOIs	https://doi.org/10.1109/IJCNN.2017.7966382
Publication status	Published - 30 Jun 2017
Event	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: 14 May 2017 → 19 May 2017

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2017-May

Conference

Conference	2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/Territory	United States
City	Anchorage
Period	14/05/17 → 19/05/17

ASJC Scopus subject areas

Software
Artificial Intelligence

More information

10.1109/IJCNN.2017.7966382

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Jin, L., Li, S., Luo, X., & Shang, M. S. (2017). Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 4165-4170). Article 7966382 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966382

Jin, Long ; Li, Shuai ; Luo, Xin et al. / Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4165-4170 (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{7a15556002d145ab8c55ceba62f10f1f,

title = "Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms",

abstract = "This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.",

author = "Long Jin and Shuai Li and Xin Luo and Shang, {Ming Sheng}",

year = "2017",

month = jun,

day = "30",

doi = "10.1109/IJCNN.2017.7966382",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

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

pages = "4165--4170",

booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",

note = "2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",

}

Jin, L, Li, S, Luo, X & Shang, MS 2017, Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings., 7966382, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, Institute of Electrical and Electronics Engineers Inc., pp. 4165-4170, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7966382

Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms. / Jin, Long; Li, Shuai; Luo, Xin et al.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4165-4170 7966382 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

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Jin L, Li S, Luo X, Shang MS. Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4165-4170. 7966382. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2017.7966382

Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms

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