Robotic Waste Sorting Technology: Toward a Vision-Based Categorization System for the Industrial Robotic Separation of Recyclable Waste

Maria Koskinopoulou, Fredy Raptopoulos, George Papadopoulos, Nikitas Mavrakis, Michail Maniadakis

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

The use of robots in waste processing plants can significantly improve the processing of recyclables. Such robots need sophisticated visual and manipulation skills to be able to work in the extremely heterogeneous, complex, and unpredictable waste sorting industrial environment. This article considers the implementation of an autonomous robotic system for the categorization and physical sorting of recyclables according to material types. In particular, it focuses on the development of a low-cost computer vision module based on deep learning technologies to identify and sort items. To facilitate further research endeavors, the data set of recyclable images and a group of image processing scripts for object identification, masking, and synthetic placement against multiple backgrounds are available in an open source GitHub repository (https://github.com/kskmar/ReSort-IT.git). The deep-trained computer vision module is integrated with a robotic system that undertakes the physical separation of recyclables. The composite system is deployed in a waste processing plant, where it is successfully assessed in recyclable sorting under difficult and demanding industrial conditions.

Original languageEnglish
Pages (from-to)50-60
Number of pages11
JournalIEEE Robotics and Automation Magazine
Volume28
Issue number2
Early online date6 Apr 2021
DOIs
Publication statusPublished - Jun 2021

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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