Youngsu Cho, Minsu Cho, Jongwoo Park, Byung-Kil Han, Young Hun Lee, Sung-Hyuk Song, Chanhun Park, Dong Il Park
{"title":"Strategic algorithm for cable wiring using dual arm with compliance control","authors":"Youngsu Cho, Minsu Cho, Jongwoo Park, Byung-Kil Han, Young Hun Lee, Sung-Hyuk Song, Chanhun Park, Dong Il Park","doi":"10.1016/j.rcim.2024.102924","DOIUrl":null,"url":null,"abstract":"A variety of electronic products are in daily use to serve a variety of needs. Electronic products require different types of cable harnesses for production. Nowadays, user preferences vary and change quickly. Therefore, a variety of small-volume products are made, and producing various kinds of complex harnesses to satisfy people’s needs is difficult. In robotic automation, the wiring harness assembly process in the manufacturing of deformable objects is challenging. Because of the characteristics of a deformable object, the manufacturing task cannot be standardized. However, relying solely on image sensors is not advisable, due to the challenges involved in recognizing complex cables with image sensors. Additionally, even when cable recognition is possible, it requires too much time. To address these issues, this paper introduces a strategic algorithm for the wiring harness assembly process. The algorithm minimizes the dependence on image sensors by enabling the use of a robotic dual-arm system. The proposed method includes techniques such as cable estimation, frictional models, and trajectory planning in the algorithms. On the basis of these methods, for a provided assembly board, the algorithm outputs a systematic process for wiring harness assembly. Experimental results validate the algorithm, demonstrating its good performance.","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"62 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.rcim.2024.102924","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A variety of electronic products are in daily use to serve a variety of needs. Electronic products require different types of cable harnesses for production. Nowadays, user preferences vary and change quickly. Therefore, a variety of small-volume products are made, and producing various kinds of complex harnesses to satisfy people’s needs is difficult. In robotic automation, the wiring harness assembly process in the manufacturing of deformable objects is challenging. Because of the characteristics of a deformable object, the manufacturing task cannot be standardized. However, relying solely on image sensors is not advisable, due to the challenges involved in recognizing complex cables with image sensors. Additionally, even when cable recognition is possible, it requires too much time. To address these issues, this paper introduces a strategic algorithm for the wiring harness assembly process. The algorithm minimizes the dependence on image sensors by enabling the use of a robotic dual-arm system. The proposed method includes techniques such as cable estimation, frictional models, and trajectory planning in the algorithms. On the basis of these methods, for a provided assembly board, the algorithm outputs a systematic process for wiring harness assembly. Experimental results validate the algorithm, demonstrating its good performance.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.