Vision-Based Robotic Disassembly Combined with Real-Time MFA Data Acquisition
This addresses supply resilience for the European Union by enabling efficient recycling of critical materials from electronic waste, though it appears incremental as it builds on existing vision and synchromaterials concepts.
The paper tackles the problem of recovering critical raw materials from end-of-use PC desktops by developing a vision-based robotic disassembly system that uses real-time visual detection on edge devices to handle unpredictable geometries and simultaneously acquire data for material flow analysis, enabling autonomous and granular tracking of material stocks and flows.
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.