Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair (2407.16874v2)

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.

• The paper introduces an integrated robotic system that combines RGB-D imaging and laser scanning for adaptive, precise surface crack repair.
• It details a novel computer vision pipeline that optimizes crack detection and path planning to minimize material waste and repair time.
• Experimental validation using 3D-printed specimens shows that adaptive speed control significantly reduces fill error compared to fixed-speed methods.

Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair

This paper introduces an adaptive robotic system for the autonomous detection and repair of surface cracks in infrastructure. The work leverages advanced computer vision and robotics to address the costly inefficiencies of manual crack repair methods, offering an automated alternative that promises higher precision and adaptability to various crack sizes.

Key Contributions

The authors outline three primary contributions:

1. Integrated Robotic System: The system uses a RGB-D camera for initial crack detection and a laser scanner for precision measurements. This integration helps achieve accurate localization and evaluation of cracks, facilitating precise repair operations.
2. Novel Computer Vision Pipeline: A combination of RGB-D imaging and laser scanning is employed to deliver crack detection with high precision. This pipeline enables adaptive filling by adjusting the robot's operation to the specific characteristics of each crack.
3. Validation with 3D-printed Specimens: The validation process involved using 3D-printed specimens, allowing for controlled experimentation and repeatability. The experimental findings affirm the efficiency of the adaptive approach over a constant-speed methodology.

Methodology

The paper describes a thorough step-by-step methodology for the proposed system:

• Calibration: Determination of extrusion rates at varying speeds ensures precise material deposition. This is particularly crucial for maintaining material consistency and reducing waste.
• Crack Detection and Measurement: The process begins with crack detection using a segmentation model applied to RGB images, followed by transforming the coordinates for robot processing.
• Path Optimization: Optimized path planning minimizes travel distance, enhancing repair efficiency and accuracy.
• Material Deposition and Validation: The repair system adapts its speed according to the crack’s profile, confirmed via laser-based post-repair validation to ensure adequate material deposition.

Experimental and Simulation Results

The research includes both simulation via RoboDK and practical experiments, demonstrating the system’s efficiency. Key results showcase a significant increase in repair accuracy with adaptive speed control as compared to traditional methods, highlighting reductions in both material waste and repair time.

Quantitative results from the calibration stage reveal an inverse relationship between robotic speed and material extrusion, critical for optimizing the filling process based on crack dimensions. Notably, adaptive speed control yielded a mean fill error of 0.305, outperforming fixed speeds.

Implications and Future Directions

The findings suggest that adaptive robotic systems could significantly enhance infrastructure maintenance, offering automated solutions that improve safety, reduce labor costs, and potentially extend the lifespan of critical structures. The integration of vision-based robotic systems into civil engineering could pave the way for more sophisticated, reliable maintenance technologies in various infrastructure domains.

Future work could investigate broader applications, such as scaling the system for different types of infrastructure or integrating more sophisticated computer vision techniques to further enhance crack detection and repair precision.

In conclusion, this paper presents a comprehensive robotic solution for surface crack detection and repair, with clear contributions to the field of infrastructure maintenance. The work lays foundational elements for further exploration in autonomous maintenance technologies, offering promising advancements for current infrastructure management challenges.