First Lessons Learned of an Artificial Intelligence Robotic System for Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods

TL;DR

This paper explores an AI robotic system that uses multispectral imaging to improve the autonomous sorting of coarse waste, aiming to recover more recyclable materials efficiently.

Contribution

It introduces a multispectral imaging approach for material classification and investigates AI-based autonomous control of hydraulic machinery for waste sorting.

Findings

Multispectral imaging enables better material classification in damaged waste.

AI controllers are being developed for autonomous hydraulic machinery.

The system aims to reduce manual sorting and recover more recyclables.

Abstract

Current disposal facilities for coarse-grained waste perform manual sorting of materials with heavy machinery. Large quantities of recyclable materials are lost to coarse waste, so more effective sorting processes must be developed to recover them. Two key aspects to automate the sorting process are object detection with material classification in mixed piles of waste, and autonomous control of hydraulic machinery. Because most objects in those accumulations of waste are damaged or destroyed, object detection alone is not feasible in the majority of cases. To address these challenges, we propose a classification of materials with multispectral images of ultraviolet (UV), visual (VIS), near infrared (NIR), and short-wave infrared (SWIR) spectrums. Solution for autonomous control of hydraulic heavy machines for sorting of bulky waste is being investigated using cost-effective cameras and artificial intelligence-based controllers.