Towards Robust Spacecraft Trajectory Optimization via Transformers

TL;DR

This paper introduces an advanced transformer-based model to generate high-quality initial guesses for robust, real-time spacecraft trajectory optimization under uncertainty, significantly improving efficiency and safety in autonomous rendezvous missions.

Contribution

It extends the Autonomous Rendezvous Transformer (ART) to handle chance-constrained optimal control, enhancing robustness and performance in uncertain space rendezvous scenarios.

Findings

Achieves up to 30% cost reduction in trajectory planning.

Reduces infeasible cases by 50% compared to traditional methods.

Demonstrates robust performance across multiple state representations.

Abstract

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 (ART) 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.