Woodot: An AI-Driven Mobile Robotic System for Sustainable Defect Repair in Custom Glulam Beams

Pierpaolo Ruttico (), Federico Bordoni and Matteo Deval
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Pierpaolo Ruttico: Indexlab, Polo di Lecco, Politecnico di Milano, 23900 Lecco, Italy
Federico Bordoni: Indexlab, Polo di Lecco, Politecnico di Milano, 23900 Lecco, Italy
Matteo Deval: Indexlab, Polo di Lecco, Politecnico di Milano, 23900 Lecco, Italy

Sustainability, 2025, vol. 17, issue 12, 1-24

Abstract: Defect repair 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 six-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 a 4.3 mm mean positional error. Full repair 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 and lowering worker exposure to repetitive tasks, Woodot offers a viable pathway to enhance the environmental, economic, and social sustainability of digital timber construction. Nevertheless, some limitations remain, such as dependency on stable lighting conditions for optimal vision performance and the need for tool calibration checks.

Keywords: mobile robots; computer vision; glulam repair; high-payload cobots; fine-tuning segmentation model; AI-based defect recognition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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