Computer vision and fuzzy rules applied to an industrial desktop robot

Abstract

Purpose – Desktop robots are suitable for production line systems in industrial applications. Despite their capabilities to meet diverse requirements, they have to be programmed off-line using waypoints and path information. Misalignments in the workspace location during loading cause injuries in the workpiece and tool. Further, in flexible industrial production, machinery must adapt to changing product demands, both to the simultaneous production of different types of workpieces and to product styles with short life cycles. In this paper, visual data processing concepts on the basis of fuzzy logic are applied to enable an industrial desktop robot to raise its flexibility and address these problems that limit the production rate of small industries. Design/methodology/approach – In this paper, a desktop robot performing dispensing tasks is equipped with a computer vision system. Visual information is used to autonomously change previously off-line stored robot programs for known workpieces or to call worker’s attention for unknown/ misclassified workpieces. A fuzzy inference classifier based on a fuzzy grammar, is used to describe/identify workpieces. Fuzzy rules are automatically generated from features extracted from the workpiece under analysis. Findings – Different types of workpieces were tested and a good rate performance, higher than 90 per cent, was achieved. The obtained results illustrate both the flexibility and robustness of the proposed solution as well as its capabilities for good classification of workpieces. Practical implications – The overall system is being implemented in an industrial environment. Originality/value – The paper reports a piece of solid work which indicates clearly that the work is suitable for industrial utilization.