An Integrated System for WEEE Sorting Employing X-ray Imaging, AI-based Object Detection and Segmentation, and Delta Robot Manipulation

TL;DR

This paper presents an integrated autonomous system combining X-ray imaging, AI-based detection, and robotic manipulation to improve battery sorting in WEEE recycling, enhancing safety and efficiency.

Contribution

It introduces a novel system that combines dual-energy X-ray imaging, AI detection models, and a Delta robot for fully autonomous battery sorting in WEEE recycling.

Findings

High detection accuracy of batteries in diverse WEEE types

Effective differentiation of dense and thin materials using dual-energy X-ray imaging

Successful validation in simulation and real-world setup

Abstract

Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources. Moreover, as battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities. Numerous systems have been proposed for battery detection and removal from WEEE recycling lines, including X-ray and RGB-based visual inspection methods, typically driven by AI-powered object detection models (e.g., Mask R-CNN, YOLO, ResNets). Despite advances in optimizing detection techniques and model modifications, a fully autonomous solution capable of accurately identifying and sorting batteries across diverse WEEEs types has yet to be realized. In response to these challenges, we present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms, enabling high-contrast image reconstruction for effective differentiation of dense and thin materials in WEEE. Devices move along a conveyor belt through a high-resolution X-ray imaging system, where YOLO and U-Net models precisely detect and segment battery-containing items. An intelligent tracking and position estimation algorithm then guides a Delta robot equipped with a suction gripper to selectively extract and properly discard the targeted devices. The approach is validated in a photorealistic simulation environment developed in NVIDIA Isaac Sim and on the real setup.