Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair

Abstract

Surface cracks in infrastructure can lead to severe deterioration and expensive maintenance if not efficiently repaired. Manual repair methods are labor-intensive, time-consuming, and imprecise. While advancements in robotic perception and manipulation have progressed autonomous crack repair, three key challenges remain: accurate localization in the robot's coordinate frame, adaptability to varying crack sizes, and realistic validation of repairs. We present an adaptive, autonomous robotic system for surface crack detection and repair using advanced sensing technologies to enhance precision and safety for humans. A laser scanner is used to refine crack coordinates for accurate localization. Furthermore, our adaptive crack filling approach outperforms fixed speed techniques in efficiency and consistency. We validate our method using 3D printed cracks under realistic conditions, demonstrating repeatable testing. This research contributes to the field of human-robot interaction by reducing manual labor, improving safety, and streamlining maintenance operations, ultimately paving the way for more sophisticated and integrated construction robotics.