DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform
<|reference_start|>DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform: 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.<|reference_end|>
@article{zhu2024disgnet:,
    title={DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning
  of Gough-Stewart Platform},
    author={Huizhi Zhu, Wenxia Xu, Jian Huang and Jiaxin Li},
    journal={arXiv preprint arXiv:2402.09077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09077},
    primaryClass={cs.RO cs.LG}
}