An Assembly Sequence Planning Framework for Complex Data using General Voronoi Diagram

S. Dorn, Nicola Wolpert, E. Schomer
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引用次数: 2

Abstract

We present the first realization of an assembly sequence planning framework for large-scale and complex 3D real-world CAD scenarios. Other than in academic benchmark data sets, in our scenario each assembled part is allowed to contain flexible fastening elements and the number of assembled parts is quite high. With our framework we are able to derive a meaningful assembly priority graph for the parts. Our framework divides the disassembly motion of each part into a NEAR- and a subsequent FAR planning phase and uses existing specialized motion planners for each phase. To reduce the number of unsuccessful motion planning requests we use a general Voronoi diagram graph and a novel collision perceiving method which significantly speed up our framework. At the end, we create an assembly priority graph to indicate which parts must be disassembled before others. In our experiments, we show that our framework is the first one which is able to generate a priority graph for a representative data set from the automotive industry. Moreover, the reported disassembly motions for the individual parts are shorter and can be computed faster than with other state-of-the-art frameworks.
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基于通用Voronoi图的复杂数据装配序列规划框架
我们提出了一个装配序列规划框架的首次实现大规模和复杂的三维现实世界的CAD场景。与学术基准数据集不同,在我们的场景中,每个装配部件都允许包含柔性紧固元件,并且装配部件的数量相当高。有了我们的框架,我们就能够为零件推导出一个有意义的装配优先级图。我们的框架将每个部件的拆卸运动分为近距离和随后的远距离规划阶段,并为每个阶段使用现有的专门运动规划器。为了减少不成功的运动规划请求的数量,我们使用了通用的Voronoi图和一种新的碰撞感知方法,这大大提高了框架的速度。最后,我们创建了一个装配优先级图,以指示哪些部件必须在其他部件之前拆卸。在我们的实验中,我们证明了我们的框架是第一个能够为汽车行业的代表性数据集生成优先级图的框架。此外,与其他先进的框架相比,单个部件的报告拆卸运动更短,可以更快地计算。
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