EdgeAI Drone for Autonomous Construction Site Demonstrator

2025-05-14

AI Summary

This paper addresses the challenges of real-time obstacle detection and dynamic path planning for autonomous collaborative multi-UAV operations in complex, dynamic construction sites. Methodologically, we design a microcontroller-unit (MCU)-optimized lightweight object detection model; construct the first publicly available edge-oriented dataset tailored to construction-site scenarios; and propose a 5G-enhanced distributed multi-agent coordination architecture integrating embedded AI, customized hardware platforms, ultra-low-latency communication, and an edge-based real-time perception-decision framework. Experimental results demonstrate millisecond-level system responsiveness, a 76% reduction in computational overhead, 2.3× extended flight endurance, and stable cooperative operation of over ten UAVs—achieving a balanced trade-off among low power consumption, cost efficiency, and high operational safety.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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

Problem

• Develops Edge-AI drone for construction site surveillance
• Solves real-time obstacle detection in dynamic environments
• Addresses multi-robot coordination with 5G and lightweight AI

Innovation

• Edge-AI-enabled drone for autonomous construction surveillance
• Lightweight MCU-based object detection model integration
• 5G-enabled multi-agent coordination infrastructure for drones