EdgeAI Drone for Autonomous Construction Site Demonstrator

TL;DR

This paper introduces an Edge-AI drone system for autonomous construction site monitoring, combining lightweight object detection, multi-robot coordination, and real-time obstacle avoidance to improve safety and efficiency.

Contribution

It presents a novel UAV platform with integrated lightweight AI models and a multi-agent infrastructure tailored for construction environments, advancing edge-AI applications in civil robotics.

Findings

Demonstrated real-time obstacle detection and path planning in construction sites

Showed scalability and computational efficiency of the proposed system

Provided a new dataset for MCU-based edge applications

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