{"title":"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","authors":"Jinhua Xiao, Jiaxu Gao, N. Anwer, B. Eynard","doi":"10.1115/1.4062235","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-agent Reinforcement Learning Method for Disassembly Sequential Task Optimization Based on Human-Robot Collaborative Disassembly in Electric Vehicle Battery Recycling\",\"authors\":\"Jinhua Xiao, Jiaxu Gao, N. Anwer, B. Eynard\",\"doi\":\"10.1115/1.4062235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4062235\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062235","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
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.
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