Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.13964 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918127293431808 |
|---|---|
| author | Cramer, Martijn Wu, Yanming De Schepper, David Demeester, Eric |
| author_facet | Cramer, Martijn Wu, Yanming De Schepper, David Demeester, Eric |
| contents | Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting production costs and leading to tech-skilled workforces not being used to their full potential. The COOCK+ ROBUST project aims to transform cobots into mobile and reconfigurable production assistants by integrating existing technologies, including 3D object recognition and localisation. This article explores both the opportunities and challenges of enhancing cobotic systems with these technologies in an industrial setting, outlining the key steps involved in the process. Additionally, insights from a past project, carried out by the ACRO research unit in collaboration with an industrial partner, serves as a concrete implementation example throughout. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augmenting cobots for sheet-metal SMEs with 3D object recognition and localisation Cramer, Martijn Wu, Yanming De Schepper, David Demeester, Eric Robotics Computer Vision and Pattern Recognition Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting production costs and leading to tech-skilled workforces not being used to their full potential. The COOCK+ ROBUST project aims to transform cobots into mobile and reconfigurable production assistants by integrating existing technologies, including 3D object recognition and localisation. This article explores both the opportunities and challenges of enhancing cobotic systems with these technologies in an industrial setting, outlining the key steps involved in the process. Additionally, insights from a past project, carried out by the ACRO research unit in collaboration with an industrial partner, serves as a concrete implementation example throughout. |
| title | Augmenting cobots for sheet-metal SMEs with 3D object recognition and localisation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13964 |