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 the operational scalability in real rendezvous missions.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.

翻译：可靠的实时轨迹生成对未来自主航天器至关重要。尽管非凸制导与控制领域的最新进展为星载自主轨迹优化铺平了道路，但这些方法仍严重依赖大量专家输入（例如航点、约束条件、任务时间线等），这限制了在实际交会任务中的操作可扩展性。本文提出SAGES（语义自主航天制导引擎），一种将自然语言指令转化为航天器轨迹的生成框架，该框架在遵循非凸约束的同时，能够反映高层意图。在两个场景下的实验——具有连续时间约束执行的容错邻近操作以及自由飞行机器人平台——表明，SAGES能够可靠地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。最终，这项工作标志着朝向语言条件化、约束感知的航天器轨迹生成迈出了初步一步，使操作员能够通过直观的自然语言指令交互式地引导安全性和行为，同时减轻专家负担。