QRIVAS: Quadruped Robot‐Based Intelligent Visual Acquisition System for Bridge Component Inspection

ABSTRACT Bridge inspection constitutes a critical yet labor‐intensive task in civil infrastructure maintenance, often requiring access to confined, structurally complex environments. Conventional manual inspection suffers from low efficiency and high operational risks, and the robotic solutions encounter limitations in GNSS‐denied and low illumination environments with texture‐deficient surfaces. This study proposes QRIVAS (quadruped robot based intelligent visual acquisition system), an autonomous framework for structural component image acquisition without relying on prior maps to reduce the workload for manual close‐proximity inspection. QRIVAS integrates 3D LiDAR SLAM with real‐time semantic segmentation, enabling reliable navigation and precise structural component identification. In this paper, we focus on the exploration and inspection of bridge column—a representative and critical structural component of bridge systems. Experimental validation across simulated concrete railway viaducts and physical laboratory‐scale bridge models (1:3 scale) shows that QRIVAS achieved 100% navigation success rate in simulation environments and 96.7% average task navigation success rate across six bridge columns in laboratory‐scale bridge specimen. Compared to existing research, QRIVAS shows consistent performance improvements across varying tolerance conditions (25 cm and 50 cm radius), maintaining robust operation under both flat concrete floor and rough artificial grass terrain conditions. This work demonstrates the potential of AI‐driven robotic systems to transform traditional infrastructure maintenance practices.