Neural networks based single robot arm control for visual servoing

Shuai Li, Yinyan Zhang

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

In this chapter, we investigate the kinematic control of a single robot arm with an eye-in-hand camera for visual servoing by using neural networks. The visual servoing problem is formulated as a constrained quadratic program, which is then solved via a recurrent neural network. By this approach, the visual servoing with respect to a static point object is achieved with the feature coordinate errors in the image space converging to zero. Besides, joint angle and velocity limits of the robot arm are satisfied, which thus enhances the safety of the robot arm during the visual servoing process. The performance of the approach is guaranteed via theoretical analysis and validated via a simulative example.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
PublisherSpringer-Verlag
Pages1-11
Number of pages11
Edition9789811070365
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameSpringerBriefs in Applied Sciences and Technology
Number9789811070365
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

ASJC Scopus subject areas

  • Biotechnology
  • Chemical Engineering(all)
  • Mathematics(all)
  • Materials Science(all)
  • Energy Engineering and Power Technology
  • Engineering(all)

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