A Data-driven Approach for General Visual Quality Control in a Robotic Workcell

Abstract

Setting up computer vision quality control tasks in a robot workcell is expensive, time consuming, and often requires expert knowledge. In this work, a highly adaptable approach to mitigate this issue is introduced. First, an ontology of atomic building blocks is defined, where each block represents one computer vision algorithm. Second, these blocks are used to generate a complex graph structure. The generation follows a set of rules, which can be shown to the inexperienced worker as a list of simple questions. Afterwards, this graph can be refined. Finally, the graph is integrated into the robotic assembly sequence, and the visual quality control procedure can be executed. Two industrial use cases show the feasibility of this approach. Thus, this work leverages computer vision aided quality control tasks in robot workcells.