Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
2026-06-02 • Artificial Intelligence
Artificial IntelligenceRobotics
AI summaryⓘ
The authors developed a method that uses large language models (LLMs) to turn plain English descriptions of space mission goals into math problems that help plan spacecraft paths. This makes it easier to create accurate trajectory plans without needing deep expertise in math or space operations. They tested their approach with spacecraft rendezvous tasks and found it worked well. Their work shows that LLMs can help connect mission ideas with the math needed for spacecraft navigation.
trajectory optimizationlarge language modelsspacecraft rendezvousmathematical formulationautonomous operationsconvex optimizationmission requirementsspace exploration
Authors
Eleanor Brosius, Yuji Takubo, Daniele Gammelli, Simone D'Amico, Marco Pavone
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.