Cooperative Motion Generation in a Distributed Network of Redundant Robot Manipulators with Noises

Long Jin, Shuai Li, Lin Xiao, Rongbo Lu, Bolin Liao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

94 Citations (Scopus)

Abstract

In this paper, a distributed scheme is proposed for the cooperative motion generation in a distributed network of multiple redundant manipulators. The proposed scheme can simultaneously achieve the specified primary task to reach global cooperation under limited communications among manipulators and optimality in terms of a specified optimization index of redundant robot manipulators. The proposed distributed scheme is reformulated as a quadratic program (QP). To inherently suppress noises originating from communication interferences or computational errors, a noise-tolerant zeroing neural network (NTZNN) is constructed to solve the QP problem online. Then, theoretical analyses show that, without noise, the proposed distributed scheme is able to execute a given task with exponentially convergent position errors. Moreover, in the presence of noise, the proposed distributed scheme with the aid of NTZNN model has a satisfactory performance. Furthermore, simulations and comparisons based on PUMA560 redundant robot manipulators substantiate the effectiveness and accuracy of the proposed distributed scheme with the aid of NTZNN model.

Original languageEnglish
Article number7911356
Pages (from-to)1715-1724
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number10
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • Distributed control
  • kinematic control
  • motion generation
  • noise-tolerant zeroing neural network (NTZNN)
  • redundancy resolution

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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