Neural-based Control for CubeSat Docking Maneuvers
This addresses the problem of robust and adaptable control for small spacecraft docking, though it is incremental as it applies existing RL methods to a specific domain.
The paper tackled autonomous rendezvous and docking for CubeSats by using neural networks trained with reinforcement learning, achieving validation through extensive Monte Carlo simulations and hardware tests that demonstrated deployment feasibility.
Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance and control during the final phase of the rendezvous maneuver. The proposed strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience rather than relying on predefined models. Extensive Monte Carlo simulations within a relevant environment are conducted in 6DoF settings to validate our approach, along with hardware tests that demonstrate deployment feasibility. Our findings highlight the efficacy of RL in assuring the adaptability and efficiency of spacecraft RVD, offering insights into future mission expectations.