Title: Adaptive Trajectory Optimization Through Semantic Constraint Synthesis Using Large Language Models

Abstract

Ensuring the safety and reliability of autonomous activities in space exploration hinges on effective trajectory optimization. With space missions becoming more frequent, complex, and expansive, there is an urgent demand for the rapid creation of mathematically robust trajectory optimization problems that precisely capture mission goals and operational limitations. However, converting mission intent into tractable analytical formulations for trajectory optimization typically demands significant domain expertise. To address this, we introduce a framework that utilizes large language models (LLMs) to convert natural language descriptions of mission requirements and constraints into executable trajectory optimization code alongside corresponding mathematical formulations. Our experiments, conducted within spacecraft rendezvous scenarios, reveal a high success rate in reformulating convex trajectory optimization problems based on semantic mission requirements. This study underscores the capability of LLMs to connect high-level intent with formal optimization models, thereby facilitating more flexible and efficient spacecraft trajectory design.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC