Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

The working accuracy of multi-linkage robot is seriously affected by the errors at the joints caused by the uncertainty factors such as vibration, wear, deformation, and manufacturing clearance. In order to improve the working accuracy, the joint motion prediction including these errors is researche...

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Autores principales:	Hu Wu, Xinning Li, Xianhai Yang, Ting Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Graph convolutional network gated recurrent unit joint trajectory spatial-temporal prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e67b5c573f214cd7be10de7d8ba16050
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id	oai:doaj.org-article:e67b5c573f214cd7be10de7d8ba16050
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spelling	oai:doaj.org-article:e67b5c573f214cd7be10de7d8ba160502021-11-19T00:06:40ZJoint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network2169-353610.1109/ACCESS.2020.3042241https://doaj.org/article/e67b5c573f214cd7be10de7d8ba160502020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9279337/https://doaj.org/toc/2169-3536The working accuracy of multi-linkage robot is seriously affected by the errors at the joints caused by the uncertainty factors such as vibration, wear, deformation, and manufacturing clearance. In order to improve the working accuracy, the joint motion prediction including these errors is researched, which can realize the follow-up errors pre-compensation. According to the spatial correlation and time dependence between the joints of the robot, the joints can be represented as a graph. This work proposes a joint trajectory prediction method based on graph convolutional neural network (GCN) and gated recurrent unit (GRU). A real experimental dataset is built to verify the effectiveness, including the uncertain errors. The method is validated by means of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Accuracy, and R-square. Experimental results demonstrate that the method obtains the highest performance in the joints trajectories prediction, compared with Historical Average (HA), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). In the case of Accuracy, the accuracy of the proposed method is 91.549%, which is 33.40%, 7.32% and 3.19% higher than that of HA, ARIMA and SVR, respectively. The method can effectively predict the joints trajectories of multi-linkage robot with uncertain error at the joint, and provide theoretical support for further error compensation, obstacle prediction, and obstacle avoidance control of robot.Hu WuXinning LiXianhai YangTing WangIEEEarticleGraph convolutional networkgated recurrent unitjoint trajectoryspatial-temporal predictionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 221077-221092 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Graph convolutional network gated recurrent unit joint trajectory spatial-temporal prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Graph convolutional network gated recurrent unit joint trajectory spatial-temporal prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hu Wu Xinning Li Xianhai Yang Ting Wang Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
description	The working accuracy of multi-linkage robot is seriously affected by the errors at the joints caused by the uncertainty factors such as vibration, wear, deformation, and manufacturing clearance. In order to improve the working accuracy, the joint motion prediction including these errors is researched, which can realize the follow-up errors pre-compensation. According to the spatial correlation and time dependence between the joints of the robot, the joints can be represented as a graph. This work proposes a joint trajectory prediction method based on graph convolutional neural network (GCN) and gated recurrent unit (GRU). A real experimental dataset is built to verify the effectiveness, including the uncertain errors. The method is validated by means of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Accuracy, and R-square. Experimental results demonstrate that the method obtains the highest performance in the joints trajectories prediction, compared with Historical Average (HA), Autoregressive Integrated Moving Average (ARIMA), and Support Vector Regression (SVR). In the case of Accuracy, the accuracy of the proposed method is 91.549%, which is 33.40%, 7.32% and 3.19% higher than that of HA, ARIMA and SVR, respectively. The method can effectively predict the joints trajectories of multi-linkage robot with uncertain error at the joint, and provide theoretical support for further error compensation, obstacle prediction, and obstacle avoidance control of robot.
format	article
author	Hu Wu Xinning Li Xianhai Yang Ting Wang
author_facet	Hu Wu Xinning Li Xianhai Yang Ting Wang
author_sort	Hu Wu
title	Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
title_short	Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
title_full	Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
title_fullStr	Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
title_full_unstemmed	Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network
title_sort	joint trajectory prediction of multi-linkage robot based on graph convolutional network
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/e67b5c573f214cd7be10de7d8ba16050
work_keys_str_mv	AT huwu jointtrajectorypredictionofmultilinkagerobotbasedongraphconvolutionalnetwork AT xinningli jointtrajectorypredictionofmultilinkagerobotbasedongraphconvolutionalnetwork AT xianhaiyang jointtrajectorypredictionofmultilinkagerobotbasedongraphconvolutionalnetwork AT tingwang jointtrajectorypredictionofmultilinkagerobotbasedongraphconvolutionalnetwork
_version_	1718420642189017088

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

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