Low Thrust Transfer

Low-thrust trajectory optimization aims to design fuel-efficient paths for spacecraft, particularly crucial for long-duration missions like asteroid belt exploration or swarm formations. Current research heavily utilizes machine learning, employing neural networks and generative adversarial networks (GANs) to efficiently generate and approximate optimal trajectories, often outperforming traditional analytical methods, especially for complex scenarios with multiple targets or spacecraft. These advancements improve mission design by reducing computational costs and enabling more ambitious exploration strategies, impacting both scientific discovery and the feasibility of future space missions.