发表机构
Stanford University; Italian Institute of Artificial Intelligence (AI4I); NVIDIA（斯坦福大学; 意大利人工智能研究院（AI4I）; 英伟达）
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PLUTO框架，利用智能体AI编码和序列凸规划，将自然语言交会任务提示自动转化为满足约束的轨迹，在50个提示中94%满足要求。
AI 中文摘要
现代太空任务需要能够高效适应多样化任务目标和操作约束的轨迹设计方法。尽管非凸轨迹优化技术已经成熟，但其更广泛的采用仍受到大量领域专业知识的限制，这些知识需要针对不同任务场景定制问题公式。大型语言模型（LLM）推理和智能体AI编码能力的最新进展为提升先进轨迹优化技术的可及性提供了一条有前景的途径。本文介绍了PLUTO（轨迹优化的自然语言理解），一个交互式轨迹设计框架，它将智能体AI编码工具集成到序列凸规划（SCP）架构中。所提出的系统将高层语义输入映射到定义良好的数学约束，同时保留适合SCP的结构化表示。这些约束通过自动凸化流程处理，并直接纳入可执行的最优控制公式，从而系统地将用户意图与轨迹生成联系起来。视觉约束验证和场景参数化进一步支持了在广泛的应用场景中直观的轨迹设计环境。在对涵盖10种约束类型的50个自然语言交会任务提示的评估中，PLUTO为每个提示生成了轨迹，其中94%的生成轨迹满足任务意图的定量和定性要求。最终，这项工作探索了智能体AI在弥合先进优化算法与工程师导向的轨迹设计工作流程之间差距的潜力。
英文摘要
Modern space missions require trajectory design methods capable of efficiently adapting to diverse mission objectives and operational constraints. Despite the maturity of non-convex trajectory optimization techniques, their broader adoption remains limited by the substantial domain expertise required to tailor problem formulations across varying mission scenarios. Recent advances in large language model (LLM) reasoning and agentic AI coding capabilities offer a promising pathway to improving the accessibility of advanced trajectory optimization techniques. This paper introduces PLUTO (Plain Language Understanding for Trajectory Optimization), an interactive trajectory design framework that integrates agentic AI coding tools within a sequential convex programming (SCP) architecture. The proposed system maps high-level semantic inputs to well-defined mathematical constraints while preserving a structured representation suitable for SCP. These constraints are processed through an auto-convexification pipeline and incorporated directly into executable optimal control formulations, thereby systematically connecting user intent and trajectory generation. Visual constraint verification and scenario parameterization further support an intuitive environment for trajectory design across a wide range of application scenarios. Within an evaluation of 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt, and 94% of the resulting trajectories satisfied both the quantitative and qualitative requirements of the mission intent. Ultimately, this work explores the potential of agentic AI to bridge the gap between advanced optimization algorithms and engineer-focused trajectory design workflows.
Comments10 pages, 4 figures, 2 tables. Submitted to the 2027 IEEE Aerospace Conference
