Towards Robust Spacecraft Trajectory Optimization via Transformers
<|reference_start|>Towards Robust Spacecraft Trajectory Optimization via Transformers: Future multi-spacecraft missions require robust autonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solving non-convex optimal control problems in real time, although traditional iterative methods such as sequential convex programming impose significant computational challenges. To mitigate this burden, the Autonomous Rendezvous Transformer introduced a generative model trained to provide near-optimal initial guesses. This approach provides convergence to better local optima (e.g., fuel optimality), improves feasibility rates, and results in faster convergence speed of optimization algorithms through warm-starting. This work extends the capabilities of ART to address robust chance-constrained optimal control problems. Specifically, ART is applied to challenging rendezvous scenarios in Low Earth Orbit (LEO), ensuring fault-tolerant behavior under uncertainty. Through extensive experimentation, the proposed warm-starting strategy is shown to consistently produce high-quality reference trajectories, achieving up to 30% cost improvement and 50% reduction in infeasible cases compared to conventional methods, demonstrating robust performance across multiple state representations. Additionally, a post hoc evaluation framework is proposed to assess the quality of generated trajectories and mitigate runtime failures, marking an initial step toward the reliable deployment of AI-driven solutions in safety-critical autonomous systems such as spacecraft.<|reference_end|>
@article{takubo2024towards,
    title={Towards Robust Spacecraft Trajectory Optimization via Transformers},
    author={Yuji Takubo, Tommaso Guffanti, Daniele Gammelli, Marco Pavone, Simone
  D'Amico},
    journal={arXiv preprint arXiv:2410.05585},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05585},
    primaryClass={math.OC cs.AI cs.RO}
}