Industrial production today relies heavily on automated tasks that are performed by robotic assembly lines. This approach works well if the objects are fully specified industrial assembly parts and do not change in size, appearance, etc., like bin picking of predefined parts. Robots can perform such sorting and picking task nearly “blind”; with limited visual capabilities. If, however, objects originate from the natural world and vary considerably in size and appearance, this approach will fail. Hence there is a need for increased visual sensing capabilities, resulting in intelligent, “seeing” robots that can decide what they grasp by visual sensors and object learning.
Such naturally changing objects occur frequently when handling food or other flexible and deformable goods, like cloth or rubber, where traditional robot assembly will be useless and the production has to resort to manual labor. Southern Denmark and Schleswig-Holstein have a rather large food industry, which could become endangered if production costs rise. In this case it would be advantageous to upgrade production with intelligent robotic assembly, which at the same time will upgrade the qualifications and required skills of the workers. The tedious manual work can be performed by the robots while human skills will be upgraded to control robotic tasks.
