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A Velocity-Level Bi-Criteria Optimization Scheme for Coordinated Path Tracking of Dual Robot Manipulators Using Recurrent Neural Network

Overview of attention for article published in Frontiers in Neurorobotics, September 2017
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Title
A Velocity-Level Bi-Criteria Optimization Scheme for Coordinated Path Tracking of Dual Robot Manipulators Using Recurrent Neural Network
Published in
Frontiers in Neurorobotics, September 2017
DOI 10.3389/fnbot.2017.00047
Pubmed ID
Authors

Lin Xiao, Yongsheng Zhang, Bolin Liao, Zhijun Zhang, Lei Ding, Long Jin

Abstract

A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. After that, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively the unified QP problem. At last, computer simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the velocity-level optimization scheme for coordinated path tracking using the recurrent neural network.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 38%
Student > Postgraduate 1 13%
Student > Doctoral Student 1 13%
Student > Ph. D. Student 1 13%
Unknown 2 25%
Readers by discipline Count As %
Engineering 3 38%
Computer Science 1 13%
Business, Management and Accounting 1 13%
Unknown 3 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 October 2017.
All research outputs
#18,571,001
of 23,001,641 outputs
Outputs from Frontiers in Neurorobotics
#582
of 876 outputs
Outputs of similar age
#242,036
of 315,686 outputs
Outputs of similar age from Frontiers in Neurorobotics
#15
of 19 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 876 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.