Problem
Research questions and friction points this paper is trying to address.
robotic disassembly
material flow analysis
critical raw materials
real-time data acquisition
vision-based detection
Vision-Based Robotic Disassembly Combined with Real-Time MFA Data Acquisition
🤖 AI Summary
This study addresses the challenge of external dependency on critical raw materials by proposing an adaptive disassembly method for end-of-life desktop computers that integrates learning-driven vision with real-time material flow analysis. Leveraging a neural network-based object detection model deployed on edge devices, the system identifies geometrically uncertain and damaged components and guides robotic end-effectors to perform precise disassembly. Concurrently, it captures high-granularity material flow data across spatial scales—from local to wide-area—using the synchromaterials framework. This work represents the first integration of disassembly operations with material flow analysis (MFA) for concurrent data generation, significantly enhancing both recycling efficiency and the timeliness and accuracy of MFA.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
vision-based robotic disassembly
real-time MFA
edge computing
synchromaterials
neural object detection
🔎 Similar Papers
No similar papers found.
