Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning
This addresses fuel inefficiency and operational costs in satellite trajectory design, offering a novel approach for complex or unknown scenarios, though it is incremental as it builds on existing AI methods.
The study tackled low-thrust orbital transfer by using model-free reinforcement learning to train an agent without prior knowledge of satellite dynamics, achieving a quasi-optimal guidance law that responds well to uncertainties.
Low-thrust trajectory design and in-flight control remain two of the most challenging topics for new-generation satellite operations. Most of the solutions currently implemented are based on reference trajectories and lead to sub-optimal fuel usage. Other solutions are based on simple guidance laws that need to be updated periodically, increasing the cost of operations. Whereas some optimization strategies leverage Artificial Intelligence methods, all of the approaches studied so far need either previously generated data or a strong a priori knowledge of the satellite dynamics. This study uses model-free Reinforcement Learning to train an agent on a constrained pericenter raising scenario for a low-thrust medium-Earth-orbit satellite. The agent does not have any prior knowledge of the environment dynamics, which makes it unbiased from classical trajectory optimization patterns. The trained agent is then used to design a trajectory and to autonomously control the satellite during the cruise. Simulations show that a dynamics-agnostic agent is able to learn a quasi-optimal guidance law and responds well to uncertainties in the environment dynamics. The results obtained open the door to the usage of Reinforcement Learning on more complex scenarios, multi-satellite problems, or to explore trajectories in environments where a reference solution is not known