用行为抽象连接大模型推理与航天器安全导航
Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models
- 分三步：先预测意图行为，再生成航点约束，最后优化安全轨迹
- 近距操作下90%以上任务成功收敛，性能指标达标率提升1.5倍
- 适合需要高自主性且安全优先的深空探测任务
未来航天任务需要能理解高层任务意图并保障安全的自主能力。现有轨迹优化仍严重依赖专家设计的公式，难以支持基于意图的决策。本文提出一种意图对齐的航天器引导框架，通过行为序列和航点约束等显式中间抽象，将高层推理与安全轨迹优化相连接。首先由基础模型预测意图对齐的行为计划，再通过航点生成模型转化为航点约束，最后通过优化计算安全轨迹。该分解结构实现可扩展监督而不牺牲安全性。在近距离操作场景的数值实验表明，所提流程达到超过90%的SCP收敛率，并使满足最高优先级性能标准的轨迹生成率比启发式方法提高1.5倍。结果支持将中间行为抽象作为大模型推理与安全关键星载自主系统之间的实用接口。
原文摘要 · Abstract (English)
Future spacecraft operations require autonomy that can interpret high-level mission intent while preserving safety. However, existing trajectory optimization still relies heavily on expert-crafted formulations and does not support intent-conditioned decision-making. This paper proposes an intent-aligned spacecraft guidance framework that links high-level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints. A foundation model first predicts an intent-aligned behavior plan, a waypoint generation model then converts it into waypoint constraints, and the safe trajectory is computed via optimization. This decomposition enables scalable supervision without sacrificing safety. Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90\% SCP convergence and yields a $1.5\times$ higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making. These results support the use of intermediate behavior abstraction as a practical interface between foundation-model reasoning and safety-critical onboard spacecraft autonomy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。