用树莓派搭建可语音交互的实时监控机器人，手机就能远程操控。
A Surveillance Based Interactive Robot
- 双树莓派架构：前端摄录+中央处理，视频用FFmpeg流传输。
- YOLOv3在CPU上实现实时物体检测，语音指令识别准确率高。
- 开源硬件+开放软件，适合教学或快速原型开发。
我们构建了一款移动式监控机器人，能实时传输视频并响应语音指令，用户可通过手机或浏览器远程监控与操控。系统采用两块树莓派4：前端部署于差速驱动底盘，配备摄像头、麦克风和扬声器；中央单元负责视频服务与感知计算。视频通过FFmpeg传输。场景中的物体使用YOLOv3检测，以支持导航与事件感知。语音交互方面，利用Python语音识别、多语言翻译与文本转语音库，实现指令接收与多语种回复。深度相机Kinect提供彩色与深度图像，辅助障碍物感知。室内测试表明，该机器人可在不依赖人工控制的情况下，在CPU上以交互帧率检测常见物体，可靠识别语音命令并执行动作。设计基于现成硬件与开源软件，易于复现。文中还讨论了传感器融合（如超声波测距）、GPU加速及增加人脸识别与文本识别等扩展方向。
原文摘要 · Abstract (English)
We build a mobile surveillance robot that streams video in real time and responds to speech so a user can monitor and steer it from a phone or browser. The system uses two Raspberry Pi 4 units: a front unit on a differential drive base with camera, mic, and speaker, and a central unit that serves the live feed and runs perception. Video is sent with FFmpeg. Objects in the scene are detected using YOLOv3 to support navigation and event awareness. For voice interaction, we use Python libraries for speech recognition, multilingual translation, and text-to-speech, so the robot can take spoken commands and read back responses in the requested language. A Kinect RGB-D sensor provides visual input and obstacle cues. In indoor tests the robot detects common objects at interactive frame rates on CPU, recognises commands reliably, and translates them to actions without manual control. The design relies on off-the-shelf hardware and open software, making it easy to reproduce. We discuss limits and practical extensions, including sensor fusion with ultrasonic range data, GPU acceleration, and adding face and text recognition.
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