Digital Twin-driven Robotic Disassembly Sequence Dynamic Planning under Uncertain Missing Condition
Disassembly is an inevitable process of recycling end-of-life products and robotic disassembly sequence planning could improve disassembly efficiency. However, missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution shou...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2023-12, Vol.19 (12), p.1-9 |
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| creator | Liu, Jiayi Xu, Zhenlu Xiong, Heng Lin, Qiwen Xu, Wenjun Zhou, Zude |
| description | Disassembly is an inevitable process of recycling end-of-life products and robotic disassembly sequence planning could improve disassembly efficiency. However, missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution should be dynamically generated according to the recognized condition during disassembly process. In this paper, digital twin is utilized to solve robotic disassembly sequence dynamic planning under uncertain missing condition. First, the framework of the proposed method is studied and digital twin of robotic disassembly process is established. Afterwards, deep Q-learning network is utilized to solve the proposed problem. Finally, case studies are carried out to verify the effectiveness of proposed method. The results show the converged DQN model could dynamically find the optimal solutions after missing condition of component is recognized during disassembly process using less running time, compared with the other meta-heuristics algorithms. |
| doi_str_mv | 10.1109/TII.2023.3253187 |
| format | Article |
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However, missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution should be dynamically generated according to the recognized condition during disassembly process. In this paper, digital twin is utilized to solve robotic disassembly sequence dynamic planning under uncertain missing condition. First, the framework of the proposed method is studied and digital twin of robotic disassembly process is established. Afterwards, deep Q-learning network is utilized to solve the proposed problem. Finally, case studies are carried out to verify the effectiveness of proposed method. 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However, missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution should be dynamically generated according to the recognized condition during disassembly process. In this paper, digital twin is utilized to solve robotic disassembly sequence dynamic planning under uncertain missing condition. First, the framework of the proposed method is studied and digital twin of robotic disassembly process is established. Afterwards, deep Q-learning network is utilized to solve the proposed problem. Finally, case studies are carried out to verify the effectiveness of proposed method. 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| subjects | Algorithms Behavioral sciences Digital twin Digital twins Disassembly sequences Dismantling End of life Kinematics Learning Planning Robot kinematics robotic disassembly sequence planning Service robots uncertainties Uncertainty |
| title | Digital Twin-driven Robotic Disassembly Sequence Dynamic Planning under Uncertain Missing Condition |
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