Andrea Monguzzi, Tommaso Dotti, Lorenzo Fattorelli, Andrea Maria Zanchettin, Paolo Rocco
{"title":"Optimal model-based path planning for the robotic manipulation of deformable linear objects","authors":"Andrea Monguzzi, Tommaso Dotti, Lorenzo Fattorelli, Andrea Maria Zanchettin, Paolo Rocco","doi":"10.1016/j.rcim.2024.102891","DOIUrl":null,"url":null,"abstract":"<div><div>The robotic manipulation of deformable linear objects (DLOs), such as cables, is a valuable yet complex skill. In particular, to realize tasks like cable routing and wire harness assembly, it is required that two robotic arms, grasping the ends of a DLO, move it from an initial shape to a final one where cable assembly can be performed. The manipulation must be performed following a collision-free path and avoiding stretching and excessively deforming it. We address this problem by proposing an optimal model-based path planning strategy. Specifically, a hierarchical optimization strategy is defined to perform path planning, exploiting a mass–spring DLO dynamic model that we enhance to handle a generic equilibrium condition for the DLO. Furthermore, we model the interaction of the DLO with objects like clips used in assembly operations. We also deal with the estimation of the DLO stiffness to properly tune the model parameters. The effectiveness of our methodology is assessed via experimental tests, where a dual-arm robot executes the planned paths manipulating several DLOs with different mechanical properties. Finally, the method is exploited to execute a wire harness assembly task.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"92 ","pages":"Article 102891"},"PeriodicalIF":9.1000,"publicationDate":"2024-11-04","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://www.sciencedirect.com/science/article/pii/S0736584524001789","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
The robotic manipulation of deformable linear objects (DLOs), such as cables, is a valuable yet complex skill. In particular, to realize tasks like cable routing and wire harness assembly, it is required that two robotic arms, grasping the ends of a DLO, move it from an initial shape to a final one where cable assembly can be performed. The manipulation must be performed following a collision-free path and avoiding stretching and excessively deforming it. We address this problem by proposing an optimal model-based path planning strategy. Specifically, a hierarchical optimization strategy is defined to perform path planning, exploiting a mass–spring DLO dynamic model that we enhance to handle a generic equilibrium condition for the DLO. Furthermore, we model the interaction of the DLO with objects like clips used in assembly operations. We also deal with the estimation of the DLO stiffness to properly tune the model parameters. The effectiveness of our methodology is assessed via experimental tests, where a dual-arm robot executes the planned paths manipulating several DLOs with different mechanical properties. Finally, the method is exploited to execute a wire harness assembly task.
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.