Abstract
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.
维普
万方数据