P

robot-mcp-server

...
Created 2/27/2025byshowkeyjar

Language:

Python

Stars:

8

Forks:

0

robot-mcp-server

为大语言模型提供机器人控制能力的MCP服务器

Python Version License

功能特性

  • ✅ 支持宇树机器人运动控制
  • ✅ 支持大疆无人机起飞/降落控制
  • ✅ 基于Model Context Protocol (MCP) 的标准接口
  • 📡 实时状态监控
  • 🛑 紧急停止机制
  • 📊 完善的日志记录

安装指南

前置要求

  • Python 3.10+
  • 宇树机器人SDK2 (自动安装)
  • 大疆Tello无人机SDK (自动安装)
# 创建并激活虚拟环境
python -m venv .venv
.venv\Scripts\activate

# 安装依赖
pip install git+https://github.com/unitreerobotics/unitree_sdk2_python.git
pip install djitellopy

快速开始

from modelcontextprotocol import Client

# 连接MCP服务
client = Client.connect_stdio()

# 控制宇树机器人
client.call_tool("unitree_connect", {})
client.call_tool("unitree_move", {"velocity": 1.5})

# 控制大疆无人机
client.call_tool("dji_connect", {})
client.call_tool("dji_takeoff", {"height": 2.0})

API文档

宇树机器人工具

  • unitree_connect: 建立机器人连接
  • unitree_move(velocity: float, duration: float): 控制移动
  • unitree_stop(): 紧急停止

大疆无人机工具

  • dji_connect: 建立无人机连接
  • dji_takeoff(height: float): 起飞到指定高度
  • dji_land(): 安全降落

开发指南

项目结构:

├── src/
│   ├── main.py          # 主服务入口
│   ├── unitree_adapter.py # 宇树机器人适配器
│   └── dji_adapter.py   # 大疆无人机适配器
├── examples/            # 使用示例
├── requirements.txt     # 依赖列表
└── README.md            # 项目文档

贡献

欢迎提交Issue和PR,请遵循现有代码风格并添加适当测试。

授权协议

MIT License

Last updated: 3/2/2025

Publisher info

showkeyjar's avatar

Starved Midnight

Interesting in ML

23
followers
8
following
122
repos

More MCP servers built with Python

composer-trade-mcp

Create, backtest, and execute trades directly in one chat box. The Composer MCP Server gives LLMs the power to backtest investment ideas and execute automated trading strategies. Trade across stocks, ETFs, and crypto directly in Claude.

By https://github.com/ronnyli
slidespeak-mcp

An MCP to generate presentations with AI. Create and edit PowerPoint presentations with AI.

By https://github.com/SlideSpeak
PaddleOCR

The PaddleOCR MCP server brings enterprise-grade OCR and document parsing capabilities to AI applications. Built on PaddleOCR — a proven solution with 50,000+ GitHub stars, deeply integrated by leading projects like MinerU, RAGFlow, and OmniParser— with targeted optimizations based on the MCP concept.

By PaddlePaddle