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. Breakthroughs in robotic perception and manipulation have advanced autonomous crack repair, but proposed methods lack end-to-end testing and adaptability to changing crack size. This paper presents an adaptive, autonomous system for surface crack detection and repair using robotics with advanced sensing technologies. The system uses an RGB-D camera for crack detection, a laser scanner for precise measurement, and an extruder and pump for material deposition. A novel validation procedure with 3D-printed crack specimens simulates real-world cracks and ensures testing repeatability. Our study shows that an adaptive system for crack filling is more efficient and effective than a fixed-speed approach, with experimental results confirming precision and consistency. This research paves the way for versatile, reliable robotic infrastructure maintenance.

翻译：基础设施表面裂缝若未能得到有效修复，将导致显著的结构劣化与高昂的维护成本。传统人工修复方法劳动强度大、耗时长且精度不足，难以大规模应用于广阔区域。尽管机器人感知与操控技术的突破已推动了自主裂缝修复的发展，但现有方法普遍缺乏端到端测试能力，且难以适应裂缝尺寸的动态变化。本文提出一种基于先进传感技术的自适应自主系统，用于机器人表面裂缝检测与修复。该系统采用RGB-D相机进行裂缝检测，激光扫描仪进行精确测量，并通过挤出器与泵实现材料沉积。研究引入了一种采用3D打印裂缝试件的新型验证流程，以模拟真实裂缝并确保测试的可重复性。实验结果表明，与固定速度方法相比，自适应裂缝填充系统在效率与效果上均表现更优，其精度与一致性得到了验证。本研究为开发多功能、高可靠性的机器人基础设施维护技术奠定了基础。