Abstract

The construction industry faces persistent challenges related to labor shortages, safety hazards, and productivity inefficiencies. AI-assisted autonomous construction robots offer a promising solution by integrating advanced robotics, computer vision, and machine learning to perform complex tasks with minimal human intervention. This paper presents a framework for AI-driven autonomous construction systems capable of site navigation, material handling, bricklaying, and quality inspection. The proposed system employs deep learning for real-time environment perception, reinforcement learning for task planning and adaptation, and multi-agent coordination for collaborative operations. Sensor fusion from LiDAR, cameras, and IoT devices enables robust obstacle detection and dynamic decision-making in unstructured construction environments. Case studies demonstrate improvements in operational efficiency by up to 25%, reduction in human labor requirements, and enhanced safety outcomes. The integration of AI with robotic platforms facilitates predictive maintenance, workflow optimization, and adherence to project timelines. Findings suggest that AI-assisted autonomous construction robots can revolutionize modern construction practices by increasing precision, minimizing risks, and enabling sustainable and cost-effective building processes, supporting the broader adoption of Industry 4.0 technologies in the construction sector.