Satellite Collision Avoidance Maneuver Planning in Low Earth Orbit Using Proximal Policy Optimization

The space environment has become more congested and competitive than ever before, making collision-free satellite operation a top priority for both public and private sectors. Specifically, effective collision avoidance involving Residence Space Objects (RSOs) stands out as a critical challenge within the domain of Space Situational Awareness (SSA). This paper addresses this issue by exploring the application of Reinforcement Learning (RL) methods, which shows promising results in addressing the complexities of satellite path planning and collision avoidance. This paper focuses on single-object maneuver planning, where the primary satellite aims to avoid collisions with secondary objects using the Proximal Policy Optimization (PPO) algorithm. The obtained results demonstrate the promising performance of the trained model, establishing a proof of concept for further investigation of RL for maneuver planning and collision avoidance in space.