摘要
为提高AOI过程中图像采集的效率,将AOI路径规划问题分解为检测窗划分和CCD移动路径规划问题相结合的组合优化问题。首先使用改进的迭代自组织聚类算法划分检测窗,然后使用粒子群与蚁群混合算法优化CCD移动路径,充分发挥粒子群算法前期快速的全局收敛性能和蚁群算法的局部寻优能力,同时考虑了检测窗的可移动范围从而缩短了路径长度。最后通过Matlab仿真实验证明,与标准蚁群算法相比本文的AOI路径规划方案能够得到更短的规划路径和更快的收敛速度,提高了AOI设备的工作效率。
In order to improve the efficiency of image acquisition in the AOI process,this paper decomposes the AOI path planning problem into a combination optimization problem by combining detection window division and CCD moving path planning problem.First the improved iterative self-organizing clustering algorithm is used to divide the detection window,and then the particle swarm ant colony fusion algorithm is used to optimize the CCD moving path,which fully utilizes the local optimization ability of the ant colony algorithm and the rapid convergence performance of the particle swarm algorithm in the early stage Considering the movable range of the detection window,the path length is shortened.Finally,simulation experiments prove that the AOI path planning scheme in this paper can obtain shorter planned paths and faster convergence speed compared with standard ant colony optimization algorithm,thereby improving the working efficiency of AOI equipment.
作者
东苗
DONG Miao(Department of Information Technology and Electrical Engineering, Shanghai Xingjian College, Shanghai 200070, China)
出处
《微型电脑应用》
2021年第6期125-128,共4页
Microcomputer Applications
