用扩散模型生成可灵活组合约束的着陆轨迹，提升规划稳定性和效率。
Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
- 基于扩散模型学习多模态轨迹分布，支持并发生成。
- 可同时处理多种约束，减少对训练数据的需求。
- 适合航天器着陆轨迹规划，提升优化器鲁棒性与速度。
本文提出TrajDiffuser，一种基于扩散模型的六自由度动力下降制导轨迹生成方法。该模型为统计模型，学习由仿真生成的最优轨迹数据集中的多模态分布，每条轨迹仅受一个或少数约束影响，且约束条件在不同轨迹间可变。推理时，轨迹在时间上并行生成，实现稳定的长时程规划；支持多约束组合，增强模型泛化能力，降低训练数据需求。生成的轨迹用于初始化优化器，显著提升优化过程的鲁棒性与求解速度。
原文摘要 · Abstract (English)
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.
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