Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers

TL;DR

This paper introduces a transformer-based multimodal learning framework for spacecraft trajectory generation that generalizes across diverse scenarios, improving optimization convergence and robustness in practical, reconfigurable environments.

Contribution

The novel framework integrates transformer neural networks with multimodal data to enable generalizable, scenario-adaptive trajectory generation for spacecraft.

Findings

Achieved up to 30% cost reduction in simulations.

Reduced infeasible cases by 80% compared to traditional methods.

Demonstrated robust generalization across diverse scenarios.

Abstract

Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.