Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network

LIAO Zhaoyang; HU Ruihan; ZHOU Xuefeng; XU Zhihao; QU Hongyi; XIE Hailong

doi:10.11999/JEIT211381

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1539-1547

LIAO Zhaoyang, HU Ruihan, ZHOU Xuefeng, XU Zhihao, QU Hongyi, XIE Hailong. Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1539-1547. doi: 10.11999/JEIT211381

Citation:

LIAO Zhaoyang, HU Ruihan, ZHOU Xuefeng, XU Zhihao, QU Hongyi, XIE Hailong. Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1539-1547. doi: 10.11999/JEIT211381

Citation:

LIAO Zhaoyang, HU Ruihan, ZHOU Xuefeng, XU Zhihao, QU Hongyi, XIE Hailong. Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1539-1547. doi: 10.11999/JEIT211381

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Prediction and Compensation Method of Robot Positioning Error Based on Spatio-temporal Graph Convolution Neural Network

doi: 10.11999/JEIT211381

1.
Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
2.
Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China
3.
School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China

Funds: Guangdong Basic and Applied Basic Research Foundation (2021A1515110898), Key Areas R&D Program of Guangdong Province (2020B090925001), Key R&D Program of Guangzhou City (202103020004), GDAS' Project of Science and Technology Development (2021GDASYL-20210103087)

Received Date: 2021-11-30
Rev Recd Date: 2022-04-02

Available Online: 2022-04-09

Publish Date: 2022-05-25

Abstract

Abstract

As an important carrier of intelligent manufacturing, industrial robot has great potential in large-scale and complex tasks. However, the problem of low positioning accuracy and difficulty to control hinders the further popularization of robots in high-precision tasks. In order to improve the accuracy of robot operation, a robot positioning error prediction and compensation method based on spatio-temporal convolution graph network is proposed in this work. Firstly, through the design of graph relation coding module and spatio-temporal feature decoding module, the prediction model of the robot position and orientation error based on graph convolution network is constructed; Then, to solve the problem of low efficiency caused by too many times of robotic inverse kinematics solution in traditional iteration compensation methods, the problem of compensation for positioning errors is transformed into optimization problem, and the genetic algorithm is used to compensate the position and attitude errors simultaneously; Finally, the training set is obtained by Latin hypercube sampling method to realize the training of robot positioning error prediction model, and the accuracy of positioning error prediction and the effect of compensation are verified by the experiments.
- Industrial robot,
- Positioning error prediction,
- Spatio-temporal graph convolution network,
- Robot error compensation

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References(25)

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