轻量无人机+边缘AI，实现实时工地避障与协同作业
EdgeAI Drone for Autonomous Construction Site Demonstrator
- 用微型控制器运行轻量目标检测模型，部署于自研无人机平台
- 在真实工地测试中实现低延迟避障与动态路径规划，支持多机协同
- 开源专用数据集，适合边缘计算与建筑自动化研究者使用
自主系统与机器人技术在建筑、物流和消防等民用领域备受关注，但其广泛应用受限于高性能计算单元的需求。边缘AI解决方案具有潜力，可实现低功耗、低成本的机器人系统，提升安全性和可持续性。本文提出一种基于边缘AI的无人机监控系统，用于建筑工地的多机器人自主作业。系统集成轻量级MCU对象检测模型于定制无人机平台，并结合5G多智能体协调架构。重点解决建筑环境中实时障碍物检测与动态路径规划问题，构建了专为MCU边缘应用设计的完整数据集。实地实验验证了系统的可行性，确定了最优运行参数，表明该方案在可扩展性与计算效率上优于现有无人机解决方案。同时讨论了自动驾驶车辆在建筑工地的现状与未来角色，以及边缘AI的有效性。数据集已公开于github.com/egirgin/storaige-b950。
原文摘要 · Abstract (English)
The fields of autonomous systems and robotics are receiving considerable attention in civil applications such as construction, logistics, and firefighting. Nevertheless, the widespread adoption of these technologies is hindered by the necessity for robust processing units to run AI models. Edge-AI solutions offer considerable promise, enabling low-power, cost-effective robotics that can automate civil services, improve safety, and enhance sustainability. This paper presents a novel Edge-AI-enabled drone-based surveillance system for autonomous multi-robot operations at construction sites. Our system integrates a lightweight MCU-based object detection model within a custom-built UAV platform and a 5G-enabled multi-agent coordination infrastructure. We specifically target the real-time obstacle detection and dynamic path planning problem in construction environments, providing a comprehensive dataset specifically created for MCU-based edge applications. Field experiments demonstrate practical viability and identify optimal operational parameters, highlighting our approach's scalability and computational efficiency advantages compared to existing UAV solutions. The present and future roles of autonomous vehicles on construction sites are also discussed, as well as the effectiveness of edge-AI solutions. We share our dataset publicly at github.com/egirgin/storaige-b950
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