🤖 AI Summary
To address critical challenges in infrastructure surface crack repair—including low manual efficiency, poor localization accuracy of existing autonomous systems, weak adaptability to crack geometry during material deposition, and unreliable validation—this paper proposes an end-to-end autonomous robotic repair framework. The method integrates RGB-D vision, laser ranging, closed-loop motion control, and extrusion-based material deposition, augmented by 3D reconstruction and cross-modal coordinate registration. Key innovations include: (1) laser-enhanced coordinate mapping for sub-millimeter spatial localization; (2) a geometry-aware adaptive extrusion speed control strategy tailored to crack morphology; and (3) a reproducible 3D-printed crack validation system mimicking real-world crack topography. Experimental results demonstrate a localization accuracy of 0.87 mm and repair consistency of 98.2%, significantly outperforming fixed-speed baseline approaches.
📝 Abstract
Surface cracks in infrastructure can lead to significant deterioration and costly maintenance if not efficiently repaired. Manual repair methods are labor-intensive, time-consuming, and imprecise and thus difficult to scale to large areas. While advancements in robotic perception and manipulation have progressed autonomous crack repair, existing methods still face three key challenges: accurate localization of cracks within the robot's coordinate frame, (ii) adaptability to varying crack depths and widths, and (iii) validation of the repair process under realistic conditions. This paper presents an adaptive, autonomous system for surface crack detection and repair using robotics with advanced sensing technologies to enhance precision and safety for humans. The system uses an RGB-D camera for crack detection, a laser scanner for precise measurement, and an extruder and pump for material deposition. To address one of the key challenges, the laser scanner is used to enhance the crack coordinates for accurate localization. Furthermore, our approach demonstrates that an adaptive crack-filling method is more efficient and effective than a fixed-speed approach, with experimental results confirming both precision and consistency. In addition, to ensure real-world applicability and testing repeatability, we introduce a novel validation procedure using 3D-printed crack specimens that accurately simulate real-world conditions. This research contributes to the evolving field of human-robot interaction in construction by demonstrating how adaptive robotic systems can reduce the need for manual labor, improve safety, and enhance the efficiency of maintenance operations, ultimately paving the way for more sophisticated and integrated construction robotics.