Dynamic Trajectory Optimization for Lunar Orbital Rendezvous with Stochastic Propellant Depletion Modeling

**Abstract:** This paper introduces a novel trajectory optimization framework for Lunar Orbital Rendezvous (LOR) maneuvers considering stochastic propellant depletion. Current LOR planning methodologies often rely on deterministic propellant models, failing to account for the inherent uncertainties in engine performance and propellant consumption. We propose a hybrid approach combining Model Predictive Control (MPC) with a Markov chain Monte Carlo (MCMC) simulation to dynamically update propellant estimates and optimize trajectories in real-time. This allows for robust LOR planning even under evolving conditions and significantly reduces the risk of mission failure due to propellant exhaustion. The framework is presented with considerations for near-term commercial lunar transport missions, and represents a significant advancement in mission design.