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首页> 外文会议>International Design Engineering Technical Conferences;Computers and Information in Engineering Conference;IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications >AFFINE ICP FOR FINE LOCALIZATION OF SMART-AGVS IN SMART FACTORIES
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AFFINE ICP FOR FINE LOCALIZATION OF SMART-AGVS IN SMART FACTORIES

机译：仿射ICP用于智能工厂中智能AGVS的精细定位

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摘要

With the emergence of the concept of Industry 4.0, smart factories have started to be planned in which the production paradigm will change. Automated Guided Vehicles, abbreviated as AGV, that will perform load carrying and similar tasks in smart factories, Smart-AGVs, will try to reach their destinations on their own route instead of predetermined routes like in today's factories. Moreover, since they will not reach their targets in a single way, they have to dock a target with their fine localization algorithms. In this paper, an affine Iterative Closest Point, abbreviated as ICP, based fine localization method is proposed, and applied on Smart-AGV docking problem in smart factories. ICP is a point set registration method but it is also used for localization applications due to its high precision. Affine ICP is an ICP variant which finds affine transformation between two point sets. In general, the objective junction of ICP is constructed based on least square metric. In this study, we use affine ICP with corren-tropy metric. Correntropy is a similarity measure between two random variables, and affine ICP with correntropy tries to maximize the similarity between two point sets. Affine ICP has never been utilized in fine localization problem. We make an update on affine ICP by means of polar decomposition to reach transformation between two point sets in terms of rotation matrix and translation vector. The performance of the algorithm proposed is validated in simulation and the efficiency of it is demonstrated on MATLAB by comparing with the docking performance of the traditional ICP.
机译：随着工业4.0概念的出现，已经开始计划生产将改变生产模式的智能工厂。自动导引车，简称AGV，将在智能工厂Smart-AGV中执行负载和类似任务，将尝试以自己的路线到达目的地，而不是像今天的工厂那样以预定路线到达目的地。而且，由于它们不会以单一方式达到目标，因此他们必须将目标与精细的定位算法对接。本文提出了一种仿射迭代最近点，简称为ICP，基于精细定位方法，并将其应用于智能工厂中的Smart-AGV对接问题。 ICP是一种点集注册方法，但由于其精度高，也可用于本地化应用。仿射ICP是一种ICP变体，可在两个点集之间找到仿射变换。通常，ICP的目标结是基于最小平方度量构建的。在这项研究中，我们使用仿射ICP和Corren-tropy度量。熵是两个随机变量之间的相似性度量，并且具有熵的仿射ICP试图最大化两个点集之间的相似性。仿射ICP从未用于精细定位问题。我们通过极坐标分解对仿射ICP进行更新，以实现旋转矩阵和平移矢量两个点集之间的变换。通过与传统ICP的对接性能进行比较，在仿真中验证了所提出算法的性能，并在MATLAB上证明了该算法的效率。

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