用Transformer预测最优轨迹的紧约束，加速6自由度着陆导航计算。
Tight Constraint Prediction of Six-Degree-of-Freedom Transformer-based Powered Descent Guidance
- 基于Transformer学习紧约束集，减少优化问题规模。
- 在火星着陆任务中实现毫秒级实时引导计算。
- 适合需要快速可靠着陆规划的航天器系统应用。
本文提出基于Transformer的连续凸化方法（T-SCvx），扩展了先前的基于Transformer的推进下降制导（T-PDG）框架，适用于高效的六自由度（6-DoF）燃料最优推进下降轨迹生成。通过采用旋转不变的数据集变换，该方法显著提升了非凸推进下降制导的样本效率和解的质量。T-PDG此前已用于3-DoF最小燃料下降问题，相比无损凸化（LCvx）将求解时间缩短一个数量级。T-SCvx通过学习最优控制问题解中的紧约束集合，构建仅包含紧约束的最小化缩减问题，并利用该缩减问题的解作为直接优化求解器的热启动初始值。6-DoF推进下降因问题的非线性与非凸性、离散化方案对解有效性影响大、参考轨迹初始化决定算法收敛或发散而难以快速可靠求解。本工作通过将T-PDG扩展至6-DoF连续凸化（SCvx）形式，学习紧约束集并同时学习可行且局部最优的参考轨迹，以促进从初始猜测的收敛。T-SCvx实现了机载实时引导轨迹计算，在6-DoF火星着陆应用问题中得到验证。
原文摘要 · Abstract (English)
This work introduces Transformer-based Successive Convexification (T-SCvx), an extension of Transformer-based Powered Descent Guidance (T-PDG), generalizable for efficient six-degree-of-freedom (DoF) fuel-optimal powered descent trajectory generation. Our approach significantly enhances the sample efficiency and solution quality for nonconvex-powered descent guidance by employing a rotation invariant transformation of the sampled dataset. T-PDG was previously applied to the 3-DoF minimum fuel powered descent guidance problem, improving solution times by up to an order of magnitude compared to lossless convexification (LCvx). By learning to predict the set of tight or active constraints at the optimal control problem's solution, Transformer-based Successive Convexification (T-SCvx) creates the minimal reduced-size problem initialized with only the tight constraints, then uses the solution of this reduced problem to warm-start the direct optimization solver. 6-DoF powered descent guidance is known to be challenging to solve quickly and reliably due to the nonlinear and non-convex nature of the problem, the discretization scheme heavily influencing solution validity, and reference trajectory initialization determining algorithm convergence or divergence. Our contributions in this work address these challenges by extending T-PDG to learn the set of tight constraints for the successive convexification (SCvx) formulation of the 6-DoF powered descent guidance problem. In addition to reducing the problem size, feasible and locally optimal reference trajectories are also learned to facilitate convergence from the initial guess. T-SCvx enables onboard computation of real-time guidance trajectories, demonstrated by a 6-DoF Mars powered landing application problem.
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