LLM-Based Framework Translates Natural Language into Spacecraft Trajectory Optimization Code
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[Submitted on 2 Jun 2026]
A good honest bake. Not flashy, but you'll finish the whole bagel.
Summary
This paper presents a framework that uses large language models (LLMs) to translate natural language descriptions of mission requirements and constraints into executable trajectory optimization code and mathematical formulations for spacecraft. The work addresses the growing need for rapid formulation of mathematically sound trajectory optimization problems in space exploration, where translating mission intent into analytical formulations typically requires substantial domain expertise. Experiments in spacecraft rendezvous scenarios demonstrate high success rates in reconditioning convex trajectory optimization problems from semantic mission requirements, highlighting the potential of LLMs to bridge high-level intent and formal optimization models for more flexible and efficient spacecraft trajectory design.
Key quotes
· 5 pulledTrajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration.
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.
Experiments in spacecraft rendezvous scenarios demonstrate a high success rate in reconditioning a convex trajectory optimization problem from semantic mission requirements.
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.
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