Abstract
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 language | English |
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Pages (from-to) | 4021-4026 |
Number of pages | 6 |
Journal | transactions of the japan society of mechanical engineers series c |
Volume | 61 |
Issue number | 590 |
DOIs | |
Publication status | Published - 1995 |
Externally published | Yes |
Keywords
- Learning Control
- Manipulator
- Motion Control
- Robot
- Weaving Motion
- Welding
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering