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Localization of freeform surface workpiece with particle swarm optimization algorithm

机译:基于粒子群算法的自由曲面工件定位

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A localization method for freeform surface workpiece with particle swarm optimization (PSO) algorithm is proposed in this paper. This study is the first attempt to use PSO as a matching algorithm in localization based on in situ measuring technology. The performance of the algorithm is studied by a set of simulations and optimal parameters settings are given. To test the performance of PSO and compare it with the classical Iterative Closest Point (ICP) algorithm, a blade model and a free-form surface model are used in this study. Simulation results show that PSO with the proposed parameter settings is appropriate to the localization of different freeform surface workpieces with high accuracy and not dependent on pre-localization condition. This study proves that PSO is a new effective algorithm for the localization of freeform surface workpiece because of its advantage of high global search ability over most existing algorithms.
机译:提出了一种基于粒子群算法的自由曲面工件定位方法。这项研究是首次尝试使用PSO作为基于原位测量技术的本地化匹配算法。通过一组仿真研究算法的性能,并给出最佳参数设置。为了测试PSO的性能并将其与经典的迭代最近点(ICP)算法进行比较,本文使用了叶片模型和自由曲面模型。仿真结果表明,采用所提出的参数设置的粒子群优化算法适合于高精度地定位各种自由曲面工件,并且与预定位条件无关。这项研究证明,相对于大多数现有算法,PSO具有较高的全局搜索能力,因此是一种用于自由曲面工件定位的新有效算法。

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