 Search algorithm of the assembly sequence of products by using past learning resultsKeijiro Watanabe and Shuhei Inada International Journal of Production Economics, 2020, vol. 226, issue C Abstract: In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model. Keywords: Reinforcement learning; Q-learning; Neural-network; Assembly sequence; Disassembly sequence; Dual-arm robot (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:226:y:2020:i:c:s0925527320300037 DOI: 10.1016/j.ijpe.2020.107615 Access Statistics for this article International Journal of Production Economics is currently edited by Stefan Minner More articles in International Journal of Production Economics from Elsevier |
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