Vision-Based Robotic Disassembly Combined with Real-Time MFA Data Acquisition

TL;DR

This paper develops a vision-based robotic disassembly system for PC desktops that enables real-time material flow analysis, improving resource recovery and supply chain resilience.

Contribution

It introduces a real-time visual detection method on edge devices for robotic disassembly and integrates synchromaterials for synchronized MFA data collection.

Findings

Successful detection of PC components with neural networks.

Robotic disassembly adaptable to damaged items.

Real-time MFA data generation using synchromaterials.

Abstract

Stable and reliable supplies of rare-Earth minerals and critical raw materials (CRMs) are essential for the development of the European Union. Since a large share of these materials enters the Union from outside, a valid option for CRMs supply resilience and security is to recover them from end-of-use products. Hence, in this paper we present the preliminary phases of the development of real-time visual detection of PC desktop components running on edge devices to simultaneously achieve two goals. The first goal is to perform robotic disassembly of PC desktops, where the adaptivity of learning-based vision can enable the processing of items with unpredictable geometry caused by accidental damages. We also discuss the robot end-effectors for different PC components with the object contact points derivable from neural detector bounding boxes. The second goal is to provide in an autonomous, highly-granular, and timely fashion, the data needed to perform material flow analysis (MFA) since, to date, MFA often lacks of the data needed to accurately study material stocks and flows. The second goal is achievable thanks to the recently-proposed synchromaterials, which can generate both local and wide-area (e.g., national) material mass information in a real-time and synchronized fashion.