Optimizing Earth-Moon Transfer and Cislunar Navigation: Integrating Low-Energy Trajectories, AI Techniques and GNSS-R Technologies

TL;DR

This paper reviews innovative trajectory optimization, navigation, and remote sensing technologies for cost-effective and autonomous cislunar space missions, emphasizing AI, GNSS-R, and advanced PNT systems.

Contribution

It introduces integrated approaches combining low-energy transfer strategies, AI techniques, and GNSS-R technologies to enhance autonomy and sustainability in cislunar exploration.

Findings

AI supports automated crater recognition and terrain modeling.

Deep reinforcement learning improves descent and landing trajectories.

GNSS-R extends navigation and surface mapping capabilities beyond geostationary orbit.

Abstract

The rapid growth of cislunar activities, including lunar landings, the Lunar Gateway, and in-space refueling stations, requires advances in cost-efficient trajectory design and reliable integration of navigation and remote sensing. Traditional Earth-Moon transfers suffer from rigid launch windows and high propellant demands, while Earth-based GNSS systems provide little to no coverage beyond geostationary orbit. This limits autonomy and environmental awareness in cislunar space. This review compares four major transfer strategies by evaluating velocity requirements, flight durations, and fuel efficiency, and by identifying their suitability for both crewed and robotic missions. The emerging role of artificial intelligence and machine learning is highlighted: convolutional neural networks support automated crater recognition and digital terrain model generation, while deep reinforcement learning enables adaptive trajectory refinement during descent and landing to reduce risk and decision latency. The study also examines how GNSS-Reflectometry and advanced Positioning, Navigation, and Timing architectures can extend navigation capabilities beyond current limits. GNSS-R can act as a bistatic radar for mapping lunar ice, soil properties, and surface topography, while PNT systems support autonomous rendezvous, Lagrange point station-keeping, and coordinated satellite swarm operations. Combining these developments establishes a scalable framework for sustainable cislunar exploration and long-term human and robotic presence.