WOODOT: An AI-Driven Mobile Robotic System for Sustainable Defect Remediation in Custom Glulam Beams

Defect remediation on custom-curved glulam beams is still performed manually because knots are irregular, numerous, and located on elements that cannot pass through linear production lines, limiting the scalability of timber-based architecture. This study presents Woodot, an autonomous mobile robotic platform that combines an omnidirectional rover, a 6-dof collaborative arm, and a fine-tuned Segment Anything computer-vision pipeline to identify, mill, and plug surface knots on geometrically variable beams. The perception model was trained on a purpose-built micro-dataset and reached an F1 score of 0.69 on independent test images, while the integrated system located defects with 4.3 mm mean positional error. Full remediation cycles averaged 74 s per knot, reducing processing time by more than 60 % compared with skilled manual operations, and achieved flush plug placement in 87 % of trials. These outcomes demonstrate that a lightweight AI model coupled with mobile manipulation can deliver reliable, shop-floor automation for low-volume, high-variation timber production. By shortening cycle times, lowering worker exposure to repetitive tasks, and minimising material waste, Woodot offers a vi-able pathway to enhance the environmental, economic, and social sustainability of digital timber construction.