低延迟远程操控机器人系统，适用于智慧城市运维。
A Modular AIoT Framework for Low-Latency Real-Time Robotic Teleoperation in Smart Cities
- 模块化设计，结合Flutter、MQTT与WebRTC实现跨平台实时控制。
- 端到端延迟低于1.2秒，远端响应最快仅0.2秒。
- 适合城市巡检、设备维护等需实时感知与反馈的场景。
本文提出一种面向智慧城市的AIoT远程机器人操控系统，支持实时远程操作与智能视觉监控。前端采用Flutter跨平台移动端界面，通过MQTT传输控制信号，利用LiveKit框架实现WebRTC视频流传输。部署轻量级YOLOv11-nano模型进行实时目标检测，并将标注视觉信息回传至用户界面。控制指令经由MQTT发送至基于ESP8266的执行节点，由Arduino Mega2560控制器协调多轴机械臂运动。后端部署于DigitalOcean，保障云调度可扩展性与全球通信稳定性。在本地及跨国VPN测试（涵盖香港、日本、比利时）下，执行器响应时间最低达0.2秒，整体视频延迟低于1.2秒，即使在高延迟网络中仍保持稳定性能。该双协议低延迟设计支持闭环交互与分布式环境下的鲁棒运行。相比传统平台，本系统强调模块化部署、实时AI感知与自适应通信策略，适用于远程基础设施巡检、公共设备维护与城市自动化等场景。未来工作将聚焦边缘部署、自适应路由与城市级物联网网络集成，以提升系统韧性与扩展性。
原文摘要 · Abstract (English)
This paper presents an AI-driven IoT robotic teleoperation system designed for real-time remote manipulation and intelligent visual monitoring, tailored for smart city applications. The architecture integrates a Flutter-based cross-platform mobile interface with MQTT-based control signaling and WebRTC video streaming via the LiveKit framework. A YOLOv11-nano model is deployed for lightweight object detection, enabling real-time perception with annotated visual overlays delivered to the user interface. Control commands are transmitted via MQTT to an ESP8266-based actuator node, which coordinates multi-axis robotic arm motion through an Arduino Mega2560 controller. The backend infrastructure is hosted on DigitalOcean, ensuring scalable cloud orchestration and stable global communication. Latency evaluations conducted under both local and international VPN scenarios (including Hong Kong, Japan, and Belgium) demonstrate actuator response times as low as 0.2 seconds and total video latency under 1.2 seconds, even across high-latency networks. This low-latency dual-protocol design ensures responsive closed-loop interaction and robust performance in distributed environments. Unlike conventional teleoperation platforms, the proposed system emphasizes modular deployment, real-time AI sensing, and adaptable communication strategies, making it well-suited for smart city scenarios such as remote infrastructure inspection, public equipment servicing, and urban automation. Future enhancements will focus on edge-device deployment, adaptive routing, and integration with city-scale IoT networks to enhance resilience and scalability.
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