Optimization of Weak Stability Boundary Transfers Under Uncertainty: the LUMIO Mission Case

The rise of CubeSats has highlighted the need for mission analysis that integrates uncertainties inside the optimization phase. This work incorporates uncertainties in initial conditions, maneuvers, and navigation into a stochastic trajectory optimization process. It uses a novel implementation of the unscented transformation, avoiding sigma points resampling by employing a continuous propagation map. The problem is formulated as a nonlinear programming problem and applied to weak stability boundary trajectories, with the LUMIO mission as a case study. The method yields more fuel-efficient and robust trajectories compared to deterministic optimization. Monte Carlo analyses validate the effectiveness and accuracy of the approach.