An Experimental Study on Improvement of Weaving Motion of Welding Robots by Learning

Satoshi Tadokoro, Naoto Kobayashi, Nobukazu Kawasaki, Noriaki Miyazaki, Toshi Takamori

Research output: Contribution to journalArticlepeer-review


In this paper, weaving trajectories of welding robots are improved by incorporating learning methods. Using functions for evaluating trajectory error based on data from accelerometers, a learning method of one-dimensional optimization and that based on a model of weaving amplitudes and phases were adopted. Experimental results showed that learning methods effectively improved the weaving trajectories of welding robots; accurate weaving trajectories were obtained by only 30 learning repetitions at most. It was also revealed that continuous weaving motion along welding lines was possible by interpolation of parameter values.

Original languageEnglish
Pages (from-to)4021-4026
Number of pages6
Journaltransactions of the japan society of mechanical engineers series c
Issue number590
Publication statusPublished - 1995
Externally publishedYes


  • Learning Control
  • Manipulator
  • Motion Control
  • Robot
  • Weaving Motion
  • Welding

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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