发表机构
Massachusetts Inst. of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于扩散模型组合的模块化轨迹生成方法，用于航天器交会与接近操作，通过组合能量模型灵活配置约束，实验验证满足率高且无需重训练。
AI 中文摘要
新兴任务类别，如轨道服务、卫星检查和主动碎片清除，需要能够适应各种任务场景的轨迹设计方法。我们提出了一种基于扩散的轨迹生成方法，用于交会与接近操作（RPO），该方法能够灵活配置任务约束。首先，训练独立的基于能量的扩散模型，以满足不同的约束条件，如接近锥和传感器视线，这些模型基于一组优化轨迹进行训练。然后，在推理时，学习到的能量模型可以相互组合，或与分析定义的能量场组合，以强制执行特定的约束组合。我们通过组合学习到的接近锥模型和学习到的传感器视线模型，以及学习到的接近锥模型和合成障碍物规避模型来验证该框架，两者产生的约束满足率均在单约束模型的一个百分点以内或更高。这些结果表明，我们的组合扩散框架可以为RPO轨迹设计提供模块化方法，并能够在无需重新训练模型的情况下，为新的约束组合实现重新配置。
英文摘要
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.
