用自然语言生成航天器安全轨迹，减少人工干预。
Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous
- 将自然语言指令转为满足非凸约束的飞行路径
- 在多种场景下实现超90%语义行为一致性
- 适合需快速响应的航天任务操作员使用
可靠实时的轨迹生成对未来的自主航天器至关重要。尽管非凸导航与控制的最新进展正推动机载自主轨迹优化，但这些方法仍依赖大量专家输入（如航点、约束条件、任务时间表等），限制了复杂任务（如交会对接和近距操作）中的可扩展性。本文提出SAGES（语义自主航天引导引擎），一个将自然语言命令转化为反映高层意图且遵守非凸约束的航天器轨迹的框架。在两类场景中进行实验：具有连续时间约束执行的容错近距操作，以及自由飞行机器人平台，结果表明SAGES能可靠生成与人类指令一致的轨迹，在不同行为模式下实现了超过90%的语义-行为一致性。该工作标志着迈向语言驱动、约束感知的航天器轨迹生成的重要一步，使操作员可通过直观的自然语言指令交互式控制安全与行为，显著降低专家负担。项目网站：https://semantic-guidance4space.github.io/
原文摘要 · Abstract (English)
Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/
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