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RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots

Authors: Benjamin Alt; Florian Stöckl; Silvan Müller; Christopher Braun; Julian Raible; Saad Alhasan; Oliver Rettig; +6 Authors

RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots

Abstract

Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.

Comment: 7 pages, 6 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

Related Organizations
Keywords

I.2.2, Computer Science - Robotics, Computer Science - Artificial Intelligence, 68T40, I.2.6, I.2.9

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
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