大規模言語モデルによる適応的軌道最適化のための意味的制約合成
Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
大規模言語モデルを用いて、自然言語で記述されたミッション要件を実行可能な軌道最適化コードと数式に変換するフレームワークを提案し、宇宙機ランデブーシナリオで有効性を実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Eleanor Brosius, Yuji Takubo, Daniele Gammelli, Simone D'Amico, Marco Pavone
分類: math.OC, cs.AI, cs.RO
原文アブストラクト
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.
