As autonomous spacecraft take on increasingly critical roles in future missions, the ability to operate safely under uncertainty becomes a key factor towards mission success. Existing trajectory design techniques primarily rely on deterministic formulations, delegating the management of unplanned disturbances to feedback control systems. While effective for nominal operations, this reactive paradigm limits performance and cannot guarantee safety in the presence of significant modeling errors or environmental disturbances without significant performance degradation. The European Research Council (ERC)-funded STochastic Algorithm for Robust Guidance Analysis and Trajectory Estimation (STARGATE) project aims to address this limitation by embedding uncertainty directly within the trajectory optimization process, shifting from reactive robustness to proactive resilience. Building upon and extending the Unscented Guidance approach, the framework seeks to capture arbitrary stochastic processes through efficient uncertainty representations. Focusing on scalable and accurate optimization methods, the proposed approach plans to leverage Optimal Control Theory and Consensus Optimization to guarantee the numerical tractability required for onboard applications. The proposed methodology will be validated on representative hardware through state-of-the-art testbenches, including rocket powered landing and rendezvous scenarios. By generating trajectories that are inherently more robust to uncertainties, STARGATE aims to enhance both safety and autonomy of future space missions.