基于人机协同拆卸的电动汽车电池回收中拆卸顺序任务优化的多智能体强化学习方法

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Jinhua Xiao, Jiaxu Gao, N. Anwer, B. Eynard
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引用次数: 4

摘要

随着新型电动汽车电池在各个工业领域的广泛应用,建立一个系统的智能电池回收系统来找出退役电动汽车电池的资源浪费和环境影响具有重要意义。通过将电动汽车电池回收在再制造领域的拆解和梯次利用相结合,人机协作拆解方法可以解决退役电动汽车电池循环利用效率和安全性方面的许多巨大挑战。为了找出电动汽车电池回收过程中人机协同拆卸过程中的常见问题,提出了一种基于多智能体强化学习算法的动态拆卸过程优化方法。此外,有必要在二维平面中基于人机协同拆卸任务对电动汽车电池拆卸任务轨迹进行拆卸,结合Q学习算法可以获得同一拆卸平面中的最优拆卸路径。拆卸任务序列可以通过标准轨迹匹配来完成。最后通过对具体电池模块壳体的拆卸操作验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-agent Reinforcement Learning Method for Disassembly Sequential Task Optimization Based on Human-Robot Collaborative Disassembly in Electric Vehicle Battery Recycling
With the wide application of new electric vehicle (EV) battery in various industrial fields, it is important to establish a systematic intelligent battery recycling system that can be used to find out the resource wastes and environmental impacts for the retired EV battery. By combining the disassembly and echelon utilization of EV battery recycling in the re-manufacturing fields, human-robot collaboration (HRC) disassembly method can be used to solve many huge challenges about the efficiency and safety of retired EV battery recycling. In order to find out the common problems in the human-robot collaboration disassembly process of EV battery recycling, a dynamic disassembly process optimization method based on Multi-Agent Reinforcement Learning (MARL) algorithm is proposed. Furthermore, it is necessary to disassemble the EV battery disassembly task trajectory based on human-robot collaboration disassembly task in 2D planar, which can be used to acquire the optimal disassembly paths in the same disassembly planar combining the Q-learning algorithm. The disassembly task sequence can be completed through standard trajectory matching. Finally the feasibility of the method is verified by disassembly operations for a specific battery module case.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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