The libration points in the Earth-Moon and Sun-Earth systems are widely regarded as cost-
effective gateways into interstellar space. The associated manifold tubes have been utilized …

Multi-objective reinforcement learning is used to uncover a subset of the multi-objective
solution space for low-thrust transfers between two L 2 southern halo orbits in the Earth …

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 …

Low-energy transfers reduce the total∆ V in deep space missions by exploiting multibody
dynamics. However, searching for initial guesses in highly nonlinear systems is often a …

This paper investigates the trajectory design problem in the scenario of a multiple Sun-
synchronous Orbit (SSO) space debris flyby mission from a DRO space station. At first, the …

The purpose of this paper is to explore potential methods for improving approximation
methods of invariant manifolds. The ultimate goal is to produce a set of methods and …

Leveraging dynamical structures found in the three-body problem provides opportunities to
explore transfers that utilize considerably less fuel, thus gaining the moniker of low-energy …

When designing spacecraft trajectories, there exist cases where a trade-off between time-of-
flight and fuel become crucial to the mission design scenario, especially for unmanned …

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