2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2025)

Structural health monitoring (SHM) of indoor environments is essential for early crack detection, signaling potential structural failures. Manual inspections are labor-intensive, error-prone, and hazardous, driving the need for automation. This paper introduces a compact end-to-end autonomous robotic system for indoor crack monitoring, integrating online mapping, waypoint navigation, vision-based detection, large language model (LLM)-assisted screening, coordinate transformation, and evidence collection. The system uses a mobile platform to create a 2D occupancy grid map via simultaneous localization and mapping (SLAM). At predefined waypoints, a fine-tuned YOLOv5 detector identifies cracks from camera feeds, with outputs screened by a lightweight LLM to minimize false positives through contextual reasoning. Verified coordinates are transformed from camera to global map using time-stamped transformations. For multi-view coverage, the robot executes two 90° counterclockwise rotations per waypoint. It then navigates to cracks for high-resolution close-up imaging. In the real case study, YOLOv5 shows stable loss curves, confusion matrix with precision (0.8707) and recall (0.7840), and mAP@0.5 of 0.8262 at optimal epoch. Results are visualized on a map overlaying crack positions and evidence images for intuitive review. This enhances SHM accuracy, efficiency, and safety, with broader infrastructure applications.

Crack,Robot,Indoor Positioning,Monitoring,LLM,Autonomous Vehicle