🤖 AI Summary
This work proposes an end-to-end approach leveraging large language models to automatically translate natural language descriptions of space mission intent into solvable trajectory optimization formulations, substantially reducing reliance on domain experts. It represents the first application of large language models to the semantic-to-formal modeling pipeline in spacecraft trajectory optimization, integrating natural language understanding, convex optimization, and spacecraft rendezvous dynamics to generate executable optimization code directly from high-level mission specifications. Evaluated in spacecraft rendezvous scenarios, the system demonstrates a high success rate in reconstructing feasible convex optimization problems, thereby validating its effectiveness, practical utility, and significant enhancement of both mission design flexibility and development efficiency.
📝 Abstract
Trajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration. As space missions increase in frequency, complexity, and scope, there is a growing need to rapidly formulate mathematically sound trajectory optimization problems that accurately reflect mission objectives and operational constraints. However, translating mission intent into tractable analytical formulations for trajectory optimization requires substantial domain expertise. This paper presents a framework that leverages large language models (LLMs) to translate natural language descriptions of mission requirements and constraints into executable trajectory optimization code and corresponding mathematical formulations. Experiments in spacecraft rendezvous scenarios demonstrate a high success rate in reconditioning a convex trajectory optimization problem from semantic mission requirements. Ultimately, this work highlights the potential of LLMs to bridge high-level intent and formal optimization models, enabling more flexible and efficient trajectory design of spacecraft.