Optimizing robotic part feeder throughput with queueing theory

Abstract

Purpose

This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.

Design/methodology/approach

Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.

Findings

The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Practical implications

Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.

Originality/value

This paper advances the scientific understanding of automation and part feeding.

Citation

Gudmundsson, D. and Goldberg, K. (2007), "Optimizing robotic part feeder throughput with queueing theory", Assembly Automation, Vol. 27 No. 2, pp. 134-140. https://doi.org/10.1108/01445150710733360