Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo
This work addresses a domain-specific problem in aerospace engineering for trajectory optimization, offering an incremental improvement by integrating existing methods (diffusion models and MCMC) with a novel fine-tuning approach.
The paper tackles the challenge of optimizing long-duration, low-thrust spacecraft trajectories by using conditional diffusion models with Markov Chain Monte Carlo (MCMC) and self-supervised fine-tuning to generate initial guesses for costate variables, demonstrating improved sample quality and Pareto front completion in multibody environments like Jupiter-Europa and Saturn-Titan transfers.
Global search and optimization of long-duration, low-thrust spacecraft trajectories with the indirect method is challenging due to a complex solution space and the difficulty of generating good initial guesses for the costate variables. This is particularly true in multibody environments. Given data that reveals a partial Pareto optimal front, it is desirable to find a flexible manner in which the Pareto front can be completed and fronts for related trajectory problems can be found. In this work we use conditional diffusion models to represent the distribution of candidate optimal trajectory solutions. We then introduce into this framework the novel approach of using Markov Chain Monte Carlo algorithms with self-supervised fine-tuning to achieve the aforementioned goals. Specifically, a random walk Metropolis algorithm is employed to propose new data that can be used to fine-tune the diffusion model using a reward-weighted training based on efficient evaluations of constraint violations and missions objective functions. The framework removes the need for separate focused and often tedious data generation phases. Numerical experiments are presented for two problems demonstrating the ability to improve sample quality and explicitly target Pareto optimality based on the theory of Markov chains. The first problem does so for a transfer in the Jupiter-Europa circular restricted three-body problem, where the MCMC approach completes a partial Pareto front. The second problem demonstrates how a dense and superior Pareto front can be generated by the MCMC self-supervised fine-tuning method for a Saturn-Titan transfer starting from the Jupiter-Europa case versus a separate dedicated global search.