Distributed Adaptive Control: An ideal Cognitive Architecture candidate for managing a robotic recycling plant

TL;DR

This paper advocates for Distributed Adaptive Control as a cognitive architecture to manage complex, uncertain recycling plants within Industry 4.0, supported by a recursive implementation and a prototype micro-recycling plant.

Contribution

It introduces a recursive implementation of DAC for large-scale CPS management in recycling plants and presents a prototype micro-recycling plant as a benchmark.

Findings

Recursive DAC effectively manages open, uncertain environments.

The micro-recycling plant prototype provides a realistic benchmark.

DAC supports adaptive collaboration in cyber-physical systems.

Abstract

In the past decade, society has experienced notable growth in a variety of technological areas. However, the Fourth Industrial Revolution has not been embraced yet. Industry 4.0 imposes several challenges which include the necessity of new architectural models to tackle the uncertainty that open environments represent to cyber-physical systems (CPS). Waste Electrical and Electronic Equipment (WEEE) recycling plants stand for one of such open environments. Here, CPSs must work harmoniously in a changing environment, interacting with similar and not so similar CPSs, and adaptively collaborating with human workers. In this paper, we support the Distributed Adaptive Control (DAC) theory as a suitable Cognitive Architecture for managing a recycling plant. Specifically, a recursive implementation of DAC (between both single-agent and large-scale levels) is proposed to meet the expected demands of the European Project HR-Recycler. Additionally, with the aim of having a realistic benchmark for future implementations of the recursive DAC, a micro-recycling plant prototype is presented.