Optimal model-based path planning for the robotic manipulation of deformable linear objects

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Andrea Monguzzi, Tommaso Dotti, Lorenzo Fattorelli, Andrea Maria Zanchettin, Paolo Rocco
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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.
基于模型的机器人操纵可变形线性物体的最优路径规划
机器人操纵电缆等可变形线性物体(DLO)是一项宝贵而复杂的技能。特别是,要完成电缆布线和线束组装等任务,需要两个机械臂抓住 DLO 的两端,将其从初始形状移动到最终形状,以便进行电缆组装。操作必须按照无碰撞路径进行,并避免拉伸和过度变形。针对这一问题,我们提出了一种基于模型的最优路径规划策略。具体来说,我们定义了一种分层优化策略来执行路径规划,该策略利用了质量-弹簧 DLO 动态模型,我们对该模型进行了改进,以处理 DLO 的通用平衡条件。此外,我们还模拟了 DLO 与装配操作中使用的夹子等物体之间的相互作用。我们还对 DLO 的刚度进行了估计,以适当调整模型参数。我们通过实验测试评估了我们方法的有效性,在实验测试中,双臂机器人通过操纵具有不同机械性能的多个 DLO 执行计划路径。最后,我们利用该方法执行了一项线束装配任务。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
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.
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