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
Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
拡散モデルで6自由度の動力降下軌道を生成し、複数の制約を組み合わせて最適化の初期値に使うことで、頑健性と速度を向上させた研究。
著者: Julia Briden, Yilun Du, Enrico M. Zucchelli, Richard Linares
分類: cs.RO, cs.LG, cs.SY, eess.SY, math.OC
原文アブストラクト
This work introduces TrajDiffuser, a compositional diffusion-based flexible and concurrent trajectory generator for 6 degrees of freedom powered descent guidance. TrajDiffuser is a statistical model that learns the multi-modal distributions of a dataset of simulated optimal trajectories, each subject to only one or few constraints that may vary for different trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning, and constraints can be composed together, increasing the model's generalizability and decreasing the training data required. The generated trajectory is then used to initialize an optimizer, increasing its robustness and speed.
関連論文
- 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軌道生成/拡散モデル
