CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 医疗机器人
A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model
Lina Tong1; Decheng Cui1; Chen Wang2; Liang Peng2
2023
Conference NameIEEE International Conference on Real-time Computing and Robotics
Conference DateJuly 17-20, 2023
Conference PlaceDatong, China
Abstract

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multivelocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.
 

Indexed ByEI
Sub direction classification智能控制
planning direction of the national heavy laboratory人机混合智能
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57496
Collection多模态人工智能系统全国重点实验室_医疗机器人
Corresponding AuthorChen Wang
Affiliation1.China University of Mining and Technology Beijing
2.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lina Tong,Decheng Cui,Chen Wang,et al. A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model[C],2023.
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