拡散モデルの合成によるモジュラー制約下での宇宙機ランデブ軌道生成
Spacecraft Rendezvous Trajectory Generation with Modular Constraints via Diffusion Model Composition
拡散モデルを組み合わせることで、接近コーンやセンサ視線などの複数のミッション制約を満たすランデブ・近傍運用軌道を生成する手法を提案し、再学習なしで制約の組み合わせを変更できることを示した。
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: Mariko A. Storey-Matsutani, Richard Linares
分類: cs.RO, cs.LG
原文アブストラクト
Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adaptable to a variety of mission scenarios. We present a diffusion-based trajectory generation approach for rendezvous and proximity operations (RPO) that enables flexible configuration of mission constraints. First, individual energy-based diffusion models are trained to satisfy distinct constraints such as approach cone and sensor line-of-sight from a set of optimized trajectories. Then, at inference time, the learned energy models can be composed with one another, or with an analytically defined energy field, to enforce specific constraint combinations. We validate this framework with the composition of a learned approach cone model and a learned sensor line-of-sight model, as well as a learned approach cone model and synthetic obstacle avoidance model, both of which yield constraint satisfaction rates that are within 1 percentage point of the single-constraint models or higher. These results indicate that our compositional diffusion framework can provide a modular approach to RPO trajectory design and enable reconfiguration for new constraint combinations without requiring model retraining.
関連論文
- 柔軟な制約に対応する合成拡散モデルによる動力降下軌道生成軌道生成/拡散モデル
