Title:
APPROXIMATION ERROR DETECTION DEVICE AND APPROXIMATION ERROR DETECTION PROGRAM
Document Type and Number:
WIPO Patent Application WO/2023/248483
Kind Code:
A1
Abstract:
The present invention makes it possible to detect an approximation error amount in approximating and encoding axis-dependent data that depends on the coordinate value of each axis of an industrial machine. An approximation error detection device 1 comprises an approximation error amount detection unit 11 that detects an approximation error amount with an absolute value greater than or equal to a predetermined threshold among approximation error amounts in performing model approximation encoding of axis-dependent data on the basis of a part of the axis-dependent data that depends on the coordinate value of each axis of an industrial machine, and on a linear combination model that approximates the axis-dependent data as a linear combination of data on each axis of the industrial machine.
Inventors:
KOGA DAIJIROU (JP)
Application Number:
PCT/JP2022/025418
Publication Date:
December 28, 2023
Filing Date:
June 24, 2022
Export Citation:
Assignee:
FANUC CORP (JP)
International Classes:
G05B19/408; G05B19/404; G05B19/4063; H03M7/30
Foreign References:
| JP2011209897A | 2011-10-20 | |||
| JP2000099123A | 2000-04-07 | |||
| JPH08339216A | 1996-12-24 | |||
| JP2021092954A | 2021-06-17 | |||
| CN112558547A | 2021-03-26 | |||
| JP2014113674A | 2014-06-26 |
Other References:
VOLODYMYR MNIH, KORAY KAVUKCUOGLU, DAVID SILVER, ANDREI A. RUSU, JOEL VENESS, MARC G. BELLEMARE, ALEX GRAVES, MARTIN RIEDMILLER, A: "Human-level control through deep reinforcement learning", NATURE, vol. 518, no. 7540, pages 529 - 533, XP055283401, DOI: 10.1038/nature14236
Attorney, Agent or Firm:
SHOBAYASHI Masayuki et al. (JP)
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