Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair
This work addresses the labor-intensive and imprecise nature of manual crack repair in infrastructure maintenance, though it appears incremental by building on existing robotic perception and manipulation advancements.
The paper tackles the problem of autonomous surface crack repair by developing an adaptive robotic system that uses a laser scanner for accurate localization and an adaptive filling approach, demonstrating improved efficiency and consistency in validation tests with 3D printed cracks.
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.