A Genetic Fuzzy-Enabled Framework on Robotic Manipulation for In-Space Servicing
This addresses safety and efficiency challenges in satellite maintenance for space operations, but it is incremental as it combines existing methods.
The paper tackled the problem of automating robotic manipulation for in-space satellite servicing by developing a trusted and efficient controller, achieving an 18.5% average performance improvement over optimal LQR with high robustness to uncertainty.
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.