Gepubliceerd in Mikroniek, nr. 6 2018
In manufacturing environments where collaborative robots are employed,
conventional computer vision algorithms have trouble in the robust localisation
and detection of products due to changing illumination conditions and shadows
caused by a human sharing the workspace with the robotic system. In order to
enhance the robustness of vision applications, machine learning with neural
networks is explored. The performance of machine-learning algorithms versus
conventional computer vision algorithms is studied by observing a generic
user scenario for the manufacturing process: the assembly of a product
by localisation, identification and manipulation of building blocks.