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Title: Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms
Authors: Jin, L 
Li, S 
Luo, X
Shang, MS
Issue Date: 2017
Source: Proceedings of the International Joint Conference on Neural Networks, 2017, 7966382, p. 4165-4170
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.
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 9781509061815
DOI: 10.1109/IJCNN.2017.7966382
Appears in Collections:Conference Paper

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