用大模型把任务描述自动转为可执行的轨迹优化代码
Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
- 用大模型将自然语言任务需求转为数学优化公式和代码
- 在航天器对接场景中成功重构凸优化问题，成功率高
- 适合需要快速设计轨道的航天任务团队使用
轨迹优化是实现太空探索中安全可靠自主操作的关键。随着太空任务频率、复杂性和范围不断提升，亟需快速构建准确反映任务目标与运行约束的数学上合理的轨迹优化问题。然而，将任务意图转化为可求解的分析形式需大量领域专业知识。本文提出一种框架，利用大语言模型（LLMs）将任务要求与约束的自然语言描述转化为可执行的轨迹优化代码及对应的数学表达式。在航天器交会对接场景中的实验表明，该方法能高效地从语义任务需求重构出凸优化问题，成功率较高。本研究展示了大语言模型在连接高层任务意图与正式优化模型方面的潜力，有助于实现更灵活高效的航天器轨迹设计。
原文摘要 · Abstract (English)
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.
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