A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915252419952640 |
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| author | Steffen, Nathan Louw, Wilhelm Ernest, Nicholas Arnett, Timothy Cohen, Kelly |
| author_facet | Steffen, Nathan Louw, Wilhelm Ernest, Nicholas Arnett, Timothy Cohen, Kelly |
| contents | Automation of robotic systems for servicing in cislunar space is becoming extremely important as the number of satellites in orbit increases. Safety is critical in performing satellite maintenance, so the control techniques utilized must be trusted in addition to being highly efficient. In this work, Genetic Fuzzy Trees are combined with the widely used LQR control scheme via Thales' TrUE AI Toolkit to create a trusted and efficient controller for a two-degree-of-freedom planar robotic manipulator that would theoretically be used to perform satellite maintenance. It was found that Genetic Fuzzy-LQR is 18.5% more performant than optimal LQR on average, and that it is incredibly robust to uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing Steffen, Nathan Louw, Wilhelm Ernest, Nicholas Arnett, Timothy Cohen, Kelly Robotics Artificial Intelligence Systems and Control Automation of robotic systems for servicing in cislunar space is becoming extremely important as the number of satellites in orbit increases. Safety is critical in performing satellite maintenance, so the control techniques utilized must be trusted in addition to being highly efficient. In this work, Genetic Fuzzy Trees are combined with the widely used LQR control scheme via Thales' TrUE AI Toolkit to create a trusted and efficient controller for a two-degree-of-freedom planar robotic manipulator that would theoretically be used to perform satellite maintenance. It was found that Genetic Fuzzy-LQR is 18.5% more performant than optimal LQR on average, and that it is incredibly robust to uncertainty. |
| title | A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2504.15226 |