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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.

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.

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

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.

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

We study a programmable robotic part feeder that relies on a sequence of three conveyor belts to singulate and re‐circulate parts. In industrial practice, belt speeds are set in an ad hoc fashion. Experience with real feeders reveals that throughput can suffer owing to: starvation where no parts are visible to the camera; and saturation, where too many parts are visible, which prevents identifying part pose or grasping due to obstruction by nearby parts. This motivates our search for a systematic approach to setting belt speeds. Our goal is to optimize throughput, measured in terms of how many parts per second are delivered from the robotic feeder. We describe a 1D model of the belts with a Poisson arrival process to stochastically model how belt speeds affect throughput. Initially, we study the finite case where N parts are placed into the feeder and re‐circulated until they are all delivered by the robot. Our first insight is that the vision belt should be run at maximum achievable velocity. We run simulations to empirically determine optimal buffer belt velocity as a function of lot size. Finally, we develop a theoretical model for the case where N = ∞ which approximates common usage where the buffer is replenished before it becomes empty. From this model, we derive the optimal buffer belt velocity and show that it produces throughput five times greater than that achieved with ad hoc settings.