DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning
  of Gough-Stewart Platform
Huizhi Zhu Wenxia Xu Jian Huang Jiaxin Li
In this paper, we propose a graph neural network, DisGNet, for learning the
graph distance matrix to address the forward kinematics problem of the
Gough-Stewart platform. DisGNet employs the k-FWL algorithm for
message-passing, providing high expressiveness with a small parameter count,
making it suitable for practical deployment. Additionally, we introduce the
GPU-friendly Newton-Raphson method, an efficient parallelized optimization
method executed on the GPU to refine DisGNet's output poses, achieving
ultra-high-precision pose. This novel two-stage approach delivers ultra-high
precision output while meeting real-time requirements. Our results indicate
that on our dataset, DisGNet can achieves error accuracys below 1mm and 1deg at
79.8\% and 98.2\%, respectively. As executed on a GPU, our two-stage method can
ensure the requirement for real-time computation. Codes are released at
https://github.com/FLAMEZZ5201/DisGNet.