Open AccessInverse dynamics modeling of robots based on sparse spectral gaussian process regression
Author(s) -
Cuiyi Huang,
Yang Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012136
Subject(s) - kriging , gaussian process , inverse dynamics , inverse gaussian distribution , inverse , computer science , regression , gaussian , artificial intelligence , regression analysis , process (computing) , inverse problem , algorithm , machine learning , mathematical optimization , mathematics , statistics , physics , mathematical analysis , geometry , kinematics , distribution (mathematics) , classical mechanics , quantum mechanics , operating system
In robot control systems, inverse dynamics calculation of the robotic arm is essential. In this paper, we propose a different inverse dynamics modeling method—sparse spectral Gaussian process regression (SSGPR). Since using Gaussian process regression for robot inverse dynamics modeling is computationally intensive, to address this drawback, it is proposed to apply sparse spectral Gaussian process regression on robot inverse dynamics modeling, the core of which is the sparsification of the GP’s spectrum for the purpose of improving computational efficiency. Experiments show that sparse spectral Gaussian process regression can improve the computational efficiency and outperform other regression methods while ensuring the computational accuracy.