In this paper, a survey on the machine learning techniques in spacecraft control design is
given. Among the applications of machine learning on the subject are the design of optimal …

The task assignment issue and the path planning problem of Multiple Unmanned Aerial
Vehicles (Multi-UAV) are collectively referred to as the Mission Planning Problem (MPP) …

Reinforcement learning (RL) is used to design impulsive stationkeeping maneuvers for a
spacecraft operating near an L 2 quasi-halo trajectory in a Sun–Earth–Moon point mass …

This paper investigates the use of reinforcement learning for the optimal guidance of a
spacecraft during a time-free low-thrust transfer between two libration point orbits in the …

Reinforcement learning is used to plan station-keeping maneuvers for a spacecraft
operating near a Sun-Earth L2 halo orbit and subject to perturbations from momentum …

Multi-Reward Proximal Policy Optimization, a multiobjective deep reinforcement learning
algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat …

This paper investigates the use of reinforcement learning for the fuel-optimal guidance of a
spacecraft during a time-free low-thrust transfer between two libration point orbits in the …

We explore space traffic management as an application of collision-free navigation in multi-
agent systems where vehicles have limited observation and communication ranges. We …

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

Strategies for rapid trajectory design within multi-body systems typically focus on leveraging
dynamical systems and traditional optimization theory for analysis and initial guess …