Abstract Space mission design places a premium on cost and operational efficiency. The
search for new science and life beyond Earth calls for spacecraft that can deliver scientific …

This paper presents and analyzes Reinforcement Learning (RL) based approaches to solve
spacecraft control problems. Different application fields are considered, eg, guidance …

This Review discusses the main results obtained in training end-to-end neural architectures
for guidance and control of interplanetary transfers, planetary landings, and close-proximity …

This work is devoted to generating optimal guidance commands in real time for attitude-
constrained solar sailcrafts in coplanar circular-to-circular interplanetary transfers. Firstly, a …

Successive convex programming is a promising technique for onboard applications thanks
to its speed and guaranteed convergence. Hence it can be an enabler for future missions …

Spacecraft trajectory optimization is essential for all the different phases of a space mission,
from its launch to end-of-life disposal. Due to the increase in the number of satellites and …

To overcome the time-consuming training caused by the sparse reward function in
reinforcement learning, an efficient dense reward framework for robust low-thrust trajectory …

This work proposes an adaptation of the facility location problem for the optimal placement
of on-orbit servicing depots for satellite constellations in high-altitude orbit. The high-altitude …

Spacecraft trajectory design is critical to successful space missions, particularly with the
increasing number of spacecraft and mission complexity. Satellite constellation deployment …

Multi-Reward Proximal Policy Optimization (MRPPO) is a multi-objective reinforcement
learning algorithm used to construct low-thrust transfers between periodic orbits in multi …