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A knowledge transfer method for human-robot collaborative disassembly of end-of-life power batteries based on augmented reality

Published online by Cambridge University Press:  13 September 2024

Jie Li ,
Liangliang Duan Open the ORCID record for Liangliang Duan [Opens in a new window] ,
Weibin Qu Open the ORCID record for Weibin Qu [Opens in a new window]  and
Hangbin Zheng
Jie Li
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Liangliang Duan*
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Weibin Qu
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Hangbin Zheng
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
*
Corresponding author: Liangliang Duan; Email: [email protected]
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Abstract

The disassembly of power batteries poses significant challenges due to their complex sources, diverse types, variations in design and manufacturing processes, and diverse service conditions. Human memory capacity and robot cognitive and understanding capabilities are limited when faced with different dismantling tasks for end-of-life power batteries. Insufficient human-computer interaction capabilities greatly hinder the efficiency of human-robot collaboration (HRC) operations. The existing HRC relies heavily on the experience of operators, while the existing disassembly system fails to update new disassembly strategies in real time when facing new battery varieties. Therefore, this paper proposes an augmented reality-assisted human-robot collaboration (AR-HRC) power battery dismantling system based on transfer learning. It consists of three modules: AR-HRC knowledge modeling, dismantling subgraph similarity assessment, and strategy transfer update. The AR-HRC knowledge modeling module aims to establish an intelligent mapping from tasks to collaborative strategies based on part features. Based on the evaluation of task similarity, the mobility assessment model divides subtasks into similar and dissimilar classes. For similar subtasks, the original dismantling strategy can be applied to the current task. However, for different subtasks, operators can issue instructions to the AR-HRC system through the human-computer interaction function of AR and develop new collaborative strategies based on actual conditions. Finally, a case study of power battery dismantling is conducted, and the results show that compared to traditional pre-programmed assembly, this system can improve dismantling efficiency and reduce cognitive burden.

Keywords

ARHRCAR-HRCKTtransfer learningstrategy transfer updatepower battery
Type
Research Article
Information
AI EDAM , Volume 38 , 2024 , e9
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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