Graph-geometric message passing via a graph convolution transformer for FKP regression  

作　　者：Huizhi ZHU Wenxia XU Jian HUANG Baocheng YU 

机构地区：[1]Hubei Key Laboratory of Intelligent Robot,Wuhan Institute of Technology,Wuhan 430205,China [2]School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Science China(Information Sciences)》2024年第12期172-186,共15页中国科学(信息科学)(英文版)

基　　金：supported by National Natural Science Foundation Youth Fund of China (Grant No.61803286);Innovation Fund Project of Hubei Key Laboratory of Intelligent Robot (Grant No.HBIRL202210)。

摘　　要：In this paper, the forward kinematics problem(FKP) of the Gough-Stewart platform(GSP) with six degrees of freedom(6 DoFs) is estimated via deep learning. We propose a graph convolution transformer model by systematically analyzing some challenges encountered with using deep learning regression on largescale data. We attempt to leverage the graph-geometric message as input and singular value decomposition(SVD) orthogonalization for SO(3) manifold learning. This study is the first in which a robot with a sophisticated closed-loop mechanism is described by a graph structure and a specific deep learning model is proposed to solve the FKP of the GSP. Qualitative and quantitative experiments on our dataset demonstrate that our model is feasible and superior to other methods. Our method can guarantee error percentages of translation and rotation less than 1 mm and 1° of 81.9% and 96.7%, respectively.

关 键 词：deep learning graph-structured learning graph convolution transformer forward kinematics problem Gough-Stewart platform 

分 类 号：O17[理学—数学]