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Deformation modeling of remote handling EAMA robot by recurrent neural networks

Tao Zhang (Institute of Plasma Physics Chinese Academy of Sciences (ASIPP), Chinese Academy of Sciences, Hefei, China and University of Science and Technology of China, Hefei, China and School of Energy Systems, Lappeenranta University of Technology (LUT), Lappenranta, Finland)
Yuntao Song (Institute of Plasma Physics Chinese Academy of Sciences (ASIPP), Chinese Academy of Sciences, Hefei, China)
Huapeng Wu (Department of Mechanical Engineering, School of Energy Systems, Lappeenranta University of Technology, Lappeenranta, Finland)
Heikki Handroos (Department of Mechanical Engineering, School of Energy Systems, Lappeenranta University of Technology, Lappeenranta, Finland)
Yong Cheng (Huainan New Energy Research Center, Anhui Provincial Key Laboratory of Special Welding, Hefei, China)
Xuanchen Zhang (Institute of Plasma Physics Chinese Academy of Sciences, Hefei, Anhui, China)

Industrial Robot

ISSN: 0143-991X

Article publication date: 7 May 2019

Issue publication date: 7 May 2019

164

Abstract

Purpose

Remote handling (RH) manipulators have been widely studied for maintenance tasks in fusion reactors. Those tasks always require heavy load, high accuracy and large work space for manipulators. Traditionally, the maintenance of fusion devices always depends on manual RH. With the development of calculating ability, the intelligent automatic maintenance makes it possible for a fusion device instead of the previous manual operation. As the flexibility of arm and the deformation of manipulator will cause problems, which are mainly inaccuracy and lower efficiency. This paper aims to study an effective way to promote the arm behavior to solve these problems.

Design/methodology/approach

By making use of the experimental advanced superconducting tokamak articulated maintenance arm as a platform, a series of experiments is designed to measure errors of kinematics and to collect the database of the flexible arm. Through studying the data and the arm structure, recurrent neural network (RNN) method was adopted to estimate the deformation of flexible arm and eventually compensate deformation in robot control to achieve higher accuracy.

Findings

By means of delicate RNN modeling, errors of kinematics have been reduced to a smaller order than the RH mode. This intelligent maintenance method will also reduce complexity of operations in maintenance.

Originality/value

This paper presents the use of an artificial intelligent algorithm to solve a nonlinear deformation problem of the flexible arm. The results demonstrate that it is efficient in dealing with this problem in fusion application. The RNN’s successful application has also shown that intelligent algorithms can be widely applied in fusion maintenance.

Keywords

Citation

Zhang, T., Song, Y., Wu, H., Handroos, H., Cheng, Y. and Zhang, X. (2019), "Deformation modeling of remote handling EAMA robot by recurrent neural networks", Industrial Robot, Vol. 46 No. 2, pp. 300-310. https://doi.org/10.1108/IR-08-2018-0171

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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