Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (4): 205-210.DOI: 10.3778/j.issn.1002-8331.1608-0305

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Research on genetic algorithm for trajectory optimization problem in optical detection

LIU Jinyue, LI Yang, GUO Zhihong, REN Zhibin, LIU Jiabin   

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
  • Online:2018-02-15 Published:2018-03-07


刘今越,李  洋,郭志红,任志斌,刘佳斌   

  1. 河北工业大学 机械工程学院,天津 300130

Abstract: The number of images and the track affect the detection efficiency directly in automatic optical inspection; the image stitching method is less efficient. The problem is decomposed into clustering and trajectory planning problems. A genetic algorithm based on Boolean matrix coding is developed to realize clustering. This method can not only ensure that the individual is locally optimal, but also can respond to the constraint conditions by the repair strategy. Then, the “two segment” genetic algorithm is proposed to solve the position and the trajectory of the image taking, and the optimization process of the location and trajectory of the image taking is realized. Experimental results show that the location of the image and the trajectory of the image are improved compared with the traditional algorithm, which can improve the efficiency of automatic optical detection.

Key words: automatic optical inspection, Boolean matrix coding, local optimum, restoration strategy, two stage genetic algorithm

摘要: 在自动光学检测中,取像次数及轨迹直接影响着检测效率,而图像拼接法效率较低。将该问题分解为聚类及轨迹规划问题;提出了一种基于布尔矩阵编码的遗传算法实现聚类操作,该方法不但可保证个体均为局部最优,且通过修复策略可响应各项约束条件;而后提出了“两段式”遗传算法对取像位置容差和取像顺序进行组合求解,从而实现了取像位置及轨迹的优化过程。实验验证了算法所规划的取像位置及取像轨迹较传统算法均有所提升,可有效提高自动光学检测的效率。

关键词: 自动光学检测, 布尔矩阵编码, 局部最优, 修复策略, &ldquo, 两段式&rdquo, 遗传算法