Autonomous AI-enabled Industrial Sorting Pipeline for Advanced Textile Recycling

TL;DR

This paper presents an autonomous AI-enabled textile sorting system that combines robotics, spectral imaging, and digital twin technology to improve recycling efficiency and sustainability in the textile industry.

Contribution

It introduces a novel integrated framework for textile sorting using AI, robotics, spectral imaging, and digital twins, enhancing accuracy and scalability over traditional methods.

Findings

Improved sorting accuracy demonstrated in preliminary tests.

Enhanced system scalability and efficiency.

Potential environmental benefits through better recycling processes.

Abstract

The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry. This paper addresses the inefficiencies of traditional textile sorting methods by introducing an autonomous textile analysis pipeline. Utilising robotics, spectral imaging, and AI-driven classification, our system enhances the accuracy, efficiency, and scalability of textile sorting processes, contributing to a more sustainable and circular approach to waste management. The integration of a Digital Twin system further allows critical evaluation of technical and economic feasibility, providing valuable insights into the sorting system's accuracy and reliability. The proposed framework, inspired by Industry 4.0 principles, comprises five interconnected layers facilitating seamless data exchange and coordination within the system. Preliminary results highlight the potential of our holistic approach to mitigate environmental impact and foster a positive shift towards recycling in the textile industry.