菌落挑取机器人视觉定位与路径优化
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摘要
针对菌落挑取机器人挑取目标菌落的工艺流程和特点,结合视觉定位的基本原理,建立了菌落挑取机器人的菌落二维视觉定位相机模型,提出了菌落二维视觉定位方法与流程,实现了目标菌落在世界坐标系下的定位。针对菌落挑取实验待挑取菌落数量多、挑取路径不能进行合理规划而导致挑取效率低等问题,分别运用基本蚁群算法和改进蚁群算法对菌落挑取路径进行优化,并给出了菌落挑取路径优化图。模拟实验表明,菌落定位准确,菌落挑取效率得到显著提高。
According to the technological process and characteristics of colony picking robot selecting target colony as well as the basic principle of visual positioning,the model of colony picking robot's two-dimensional visual positioning camera was established,then the method and process of colony two-dimensional visual positioning were put forward,and the positioning of target colony in the world coordinate system was realized.In terms of the problems such as the large number of colonies to be selected in the colony picking experiment and the low picking efficiency caused by the unreasonable planning of the picking path,the basic and improved ant colony algorithms were respectively used to optimize the picking path of the colony,and the optimization diagram of the picking path of the colony was given.The simulation experiment shows that the colony positioning is accurate and the efficiency of colony picking is improved significantly.
According to the technological process and characteristics of colony picking robot selecting target colony as well as the basic principle of visual positioning,the model of colony picking robot's two-dimensional visual positioning camera was established,then the method and process of colony two-dimensional visual positioning were put forward,and the positioning of target colony in the world coordinate system was realized.In terms of the problems such as the large number of colonies to be selected in the colony picking experiment and the low picking efficiency caused by the unreasonable planning of the picking path,the basic and improved ant colony algorithms were respectively used to optimize the picking path of the colony,and the optimization diagram of the picking path of the colony was given.The simulation experiment shows that the colony positioning is accurate and the efficiency of colony picking is improved significantly.
引文
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[2]UBER D C,JAKLEVIC J M,THEIL E H,et al.Application of robotics and image processing to automated colony picking and arraying[J].Biotechniques,1991,11(5):642-647.
[3]徐鹏.基于机器视觉的机械零件二维尺寸检测系统研究[D].广州:华南理工大学,2015.
[4]代娜.基于机器视觉的小型规则零件二维尺寸测量研究[D].武汉:湖北工业大学,2008.
[5]谈高超.基于机器视觉的批量零件自动检测系统的研制与工程应用[D].上海:东华大学,2014.
[6]胡银厚.求解TSP算法的研究与改进[D].郑州:郑州大学,2012.
[7]邱实,程金光,张荣福.基于蚁群算法的单克隆菌落挑选仪路径规划[J].光学仪器,2015,37(3):264-267.
[8]陈灵佳.蚁群算法在解决TSP问题中的应用[J].电子技术与软件工程,2017(10):145.
[9]段海滨.蚁群算法原理及其应用[M].北京:科学出版社,2005.
[10]STUTZLE T,HOOS H.MAX-MIN ant system and local search for the traveling salesman problem[C]//Proceedings of 1997IEEE International Conference on Evolutionary Computation.Indianapolis:IEEE,1997:309-314.
[11]GAMBARDELLA L M,DORIGO M.Solving symmetric and asymmetric TSPs by ant colonies[C]//Proceedings of 1996IEEE Conference on Evolutionary Computation.Nagoya:IEEE,1996:622-627.