e2m2e — Earth to Moon, Moon to Earth

e2m2e 是面向地月空间任务规划的算法工具集基础设施。在“LLM+Agent”式自主任务规划系统中，大模型负责理解任务意图、分解与编排子任务，e2m2e 负责提供精确可靠的轨道计算工具：建立地月空间的动力学模型，生成周期轨道族，设计轨道之间的转移路径，并把结果画出来检查。

安装

uv（推荐）

uv 是本项目推荐的包管理器，速度快、依赖解析可靠。

uv pip install e2m2e

在自己的项目中使用：

uv init my-project && cd my-project
uv add e2m2e

从源码开发（需要 Rust 工具链，用于构建积分器内核）：

git clone https://github.com/cislunarspace/e2m2e.git
cd e2m2e
uv sync

conda

e2m2e 没有 conda-forge 包，conda 用来创建和管理 Python 环境，环境内仍用 pip 安装：

conda create -n e2m2e python=3.12
conda activate e2m2e
pip install e2m2e

pip

pip install e2m2e

可选依赖（Hamiltonian 正规化流水线）：

pip install e2m2e[normal-form]

SPICE 内核

星历动力学需要 NASA SPICE 内核文件，放置在 kernels/ 目录或 $SPICE_KERNEL_DIR 指定的路径。

国内用户推荐从项目的 GitHub Release 下载：kernels-v1 中打包了全部必需内核（行星星历、地球自转、月球姿态、闰秒与行星常数），下载后放入 kernels/ 目录即可。官方来源（网络可达时）：NASA NAIF。

快速开始

创建地月 CR3BP 系统，生成一个 DRO 轨道族：

from e2m2e.core import CR3BP_System, Orbit, CR3BP_Dynamics
from e2m2e.algorithms import DifferentialCorrection, Continuation

system = CR3BP_System(mu=0.01215, primary="earth", secondary="moon")
dynamics = CR3BP_Dynamics(system=system)

# 种子轨道
initial_state = [0.79188556619742, 0.0, 0.0, 0.0, 0.53682, 0.0]
seed_orbit = Orbit(states=[initial_state], times=[0])

# 微分修正收敛到周期轨道
corrector = DifferentialCorrection(dynamic=dynamics)
corrector.setup_2D_symmetric_x_fixed_x0(x0=initial_state[0])
seed_dro = corrector.iterate_correction(initial_guess=seed_orbit)

# 延拓生成轨道族
continuation = Continuation(corrector=corrector)
family = continuation.natural_continuation(
    seed_orbit=seed_dro,
    param_range=(0.14, 0.9),
    step_size=0.005,
)

更多示例（星历动力学、多重打靶、转移设计、可视化）见在线文档和 examples/ 目录。

使命与进度

航天任务规划正从“人在回路”走向自主规划。传统模式依赖地面人员逐级分解任务、调配资源，决策链条冗长，单次在轨服务任务的地面支持普遍在十余个小时。李胤慷等在《基于“LLM+Agent”的在轨服务自主任务规划技术》（上海航天，2026）中提出了新架构：大模型负责语义理解、任务分解与编排，专业算法工具负责精确计算，两者通过 MCP 协议协同，分钟级生成全流程任务规划方案。

“LLM+Agent”协同推理决策架构（李胤慷等，2026）

这套架构能否落地，关键在算法工具集。大模型是概率生成模型，自身做不了精确轨道计算，它输出的方案是否可信，取决于所调用工具的精度与可靠性。没有标准化、经过验证、可被调用的算法工具集，自主规划就是空中楼阁。

这里有一个容易产生的误解需要说清。用户把任务需求和相关信息输入大模型，大模型调用工具求解问题，最终呈现的结果并非大模型自身生成，而是工具计算得出的。大模型做的只是把工具的输出整理成便于人阅读和理解的形式。看起来像是模型直接根据输入生成了答案，实际上这些结果既不是大模型算出来的，也不是它推理出来的，而是工具的运算输出。因此，工具集的质量决定了整个系统的质量，这正是 e2m2e 要守住的地方。

算法工具集整体架构（李胤慷等，2026）

地月空间是这套范式最重要的应用场景之一，空间站、导航与通信星座、月面往返都从这里起步。e2m2e 的使命是建设地月空间方向的算法工具集基础设施，把轨道建模、轨道生成、转移设计等算法做成精确、标准化、可被上层规划系统调用的开源工具库。上图中算法封装层与接口协议层的能力，正是 e2m2e 已经建成和正在补齐的部分。

已经建成：

两套动力学模型：简化三体模型（CR3BP，用于快速设计）和高精度星历模型（基于 SPICE，用于精确外推），以及两者之间的转换
周期轨道族生成：DRO、Halo、Lyapunov、Lissajous、Butterfly、Dragonfly、共振轨道（RO）等，配微分修正、多重打靶、延拓等数值算法
高精度力模型：点质量与第三体引力、球谐重力场（含固体潮）、大气阻力、太阳光压、连续推力，传播精度已与 GMAT、DFH 对齐到亚百米级
转移轨道设计：网格搜索 + 非线性规划的两步法
坐标系转换（J2000/ITRF 等，与 GMAT 兼容）和 2D/3D 可视化

正在进行和计划中的：

更多轨道族：DPO、三角平动点附近的 SPO/LPO、Horseshoe 等
小推力转移与 LEO/GEO 端点条件
Hamiltonian 正规化流水线的进一步完善（把平动点附近轨道化简为少数表征参数）
面向上层规划系统的服务化封装。下一步把 e2m2e 封装为 MCP 服务器，接入下图所示的异构模型交互框架，让大模型可以像调用 Lambert、C-W 工具一样调用地月轨道算法

基于 MCP 的异构模型交互框架（李胤慷等，2026）

详细的能力清单与 API 文档见在线文档；逐版本变更见 CHANGELOG.md。

文档

在线文档：https://cislunarspace.github.io/e2m2e/

本地构建：

uv sync --group docs
uv run sphinx-build -b html docs docs/_build/html

测试与代码规范

uv run pytest tests/
uv run ruff check .

贡献

Fork 本仓库
创建功能分支 (git checkout -b feature/amazing-feature)
提交更改 (git commit -m 'Add amazing feature')
推送到分支 (git push origin feature/amazing-feature)
开启 Pull Request

引用

@software{e2m2e,
  title = {e2m2e: Earth to Moon, Moon to Earth Transfer Orbit Design Library},
  author = {ouyangjiahong},
  email = {ouyangjiahong22@nudt.edu.cn},
  url = {https://github.com/cislunarspace/e2m2e},
  version = {5.3.0},
  year = {2026},
}

使命叙述依据的文献：

李胤慷, 王浩, 袁容昊, 等. 基于“LLM+Agent”的在轨服务自主任务规划技术[J]. 上海航天（中英文）, 2026, 43(1): 169-179. DOI: 10.19328/j.cnki.2096-8655.2026.01.016.

License

Apache 2.0

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