AI-driven visual monitoring of industrial assembly tasks

Mattia Nardon1, Stefano Messelodi1, Antonio Granata2, Fabio Poiesi1, Alberto Danese2, Davide Boscaini1
1Fondazione Bruno Kessler, 2Meccanica del Sarca s.p.a.
arXiv
Teaser Image

We present ViMAT, a novel system for the real-time visual monitoring of industrial assembly tasks. Given prior knowledge on assembly instructions (top left) and synthetic CAD models of assembly components (bottom left), ViMAT integrates an AI-driven perception module, which extracts visual observations from real-world video streams (top right), with a probabilistic reasoning module that predicts the assembly state from these observations (bottom center-right).

Abstract

Visual monitoring of industrial assembly tasks is critical for preventing equipment damage due to procedural errors and ensuring worker safety. Although commercial solutions exist, they typically require rigid workspace setups or the application of visual markers to simplify the problem. We introduce ViMAT, a novel AI-driven system for real-time visual monitoring of assembly tasks that operates without these constraints. ViMAT combines a perception module that extracts visual observations from multi-view video streams with a reasoning module that infers the most likely action being performed based on the observed assembly state and prior task knowledge. We validate ViMAT on two assembly tasks, involving the replacement of \lego components and the reconfiguration of hydraulic press molds, demonstrating its effectiveness through quantitative and qualitative analysis in challenging real-world scenarios characterized by partial and uncertain visual observations.

BibTeX

@article{nardon2025vimat,
      title={AI-driven visual monitoring of industrial assembly tasks},
      author    = {Nardon, Mattia and Messelodi, Stefano and Granata, Antonio and Poiesi, Fabio and Danese, Alberto and Boscaini, Davide},
      journal={arXiv},
      year={2025}}

Acknowledgments

This work was supported by the PAT Legge 6 project NextMag. This work has been developed within a collaboration between FBK and Meccanica del Sarca s.p.a. and funded by the PAT Legge 6 project NextMag.