Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives
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arXiv
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| Format: | Preprint |
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2024
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| author | Sorrentino, Ines Romualdi, Giulio Bergonti, Fabio ĽErario, Giuseppe Traversaro, Silvio Pucci, Daniele |
| author_facet | Sorrentino, Ines Romualdi, Giulio Bergonti, Fabio ĽErario, Giuseppe Traversaro, Silvio Pucci, Daniele |
| contents | This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks (PINN). This approach eliminates the need for dedicated setups and joint torque sensors by leveraging the roboťs intrinsic model and state data. We present a comprehensive pipeline that includes data acquisition, preprocessing, ground truth generation, and model identification. The effectiveness of the PINN-based friction identification is validated through extensive testing on two different joints of the humanoid robot ergoCub, comparing its performance against traditional static friction models like the Coulomb-viscous and Stribeck-Coulomb-viscous models. Integrating the identified PINN-based friction models into a two-layer torque control architecture enhances real-time friction compensation. The results demonstrate significant improvements in control performance and reductions in energy losses, highlighting the scalability and robustness of the proposed method, also for application across a large number of joints as in the case of humanoid robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives Sorrentino, Ines Romualdi, Giulio Bergonti, Fabio ĽErario, Giuseppe Traversaro, Silvio Pucci, Daniele Robotics This paper presents a scalable method for friction identification in robots equipped with electric motors and high-ratio harmonic drives, utilizing Physics-Informed Neural Networks (PINN). This approach eliminates the need for dedicated setups and joint torque sensors by leveraging the roboťs intrinsic model and state data. We present a comprehensive pipeline that includes data acquisition, preprocessing, ground truth generation, and model identification. The effectiveness of the PINN-based friction identification is validated through extensive testing on two different joints of the humanoid robot ergoCub, comparing its performance against traditional static friction models like the Coulomb-viscous and Stribeck-Coulomb-viscous models. Integrating the identified PINN-based friction models into a two-layer torque control architecture enhances real-time friction compensation. The results demonstrate significant improvements in control performance and reductions in energy losses, highlighting the scalability and robustness of the proposed method, also for application across a large number of joints as in the case of humanoid robots. |
| title | Physics-Informed Learning for the Friction Modeling of High-Ratio Harmonic Drives |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.12685 |