Michael, who oversees digital initiatives in BMW plants, explains BMW’s trials of Hexagon’s AEON robot with a BMWBLOG host in Munich.
AEON uses wheels, interchangeable hands and tools, cameras, and thermal sensors. Michael says it could take on unpleasant or repetitive jobs, freeing workers for other tasks. BMW chose a wheeled robot because factory floors suit it and it can move faster than a walking robot.
Michael describes using robots to pick parts from irregular bins and place them into welding cells, a task that conventional automation has not made cost-effective. Workers can teach AEON through teleoperation or virtual training, then refine its performance in the plant. A learned skill can be shared across a fleet.
BMW is testing AEON with a standard Hexagon scanner to measure body-in-white panels, including gaps. The robot sends measurements to BMW’s system, and verbal instructions can tell it which part to scan. Michael says robots could make visual checks for scratches and dents more consistent because they do not tire.
Michael says robots can learn motions through repeated demonstrations, with training sometimes taking 20, 50, or 100 repetitions. BMW expects robots to specialize by production area while sharing knowledge of models and components. Coordinated robot teams may become useful later, but Michael says that stage is still far off.
Michael identifies limited robot availability and lengthy training as the main barriers. BMW’s example timeline takes about six months from pilot to production for one use case. He expects virtual training and teleoperation to shorten that process.
Briefs summarise talks and interviews; timestamps open the video at that moment. Claims in a talk are the speaker's own.
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