首页|Graph-geometric message passing via a graph convolution transformer for FKP regression
Graph-geometric message passing via a graph convolution transformer for FKP regression
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
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 large-scale 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.
deep learninggraph-structured learninggraph convolution transformerforward kinematics problemGough-Stewart platform
Huizhi ZHU、Wenxia XU、Jian HUANG、Baocheng YU
展开 >
Hubei Key Laboratory of Intelligent Robot,Wuhan Institute of Technology,Wuhan 430205,China
School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
万方数据
维普
