Optimizing the path of seedling low-density transplanting by using greedy genetic algorithm
Optimizing the path of seedling low-density transplanting by using greedy genetic algorithm
复制标题
DOI：
10.1016/j.compag.2017.09.017
复制
发表时间：
2017-11-01
影响因子：
8.3
通讯作者：
Rao, Xiuqin
中科院分区：
文献类型：
--
作者：
Tong, Junhua;Wu, Chuanyu;Rao, Xiuqin
自动化移栽机在温室内进行重复的低密度移栽和重新插苗，以解决劳动力短缺问题并持续生产苗木。通过优化末端执行器的分插路径，可以提高分插器的工作效率。在这项研究中，贪婪遗传算法（GGA）的路径优化。GGA结合了贪婪算法（GRA）和遗传算法（GA）的特点。比较了GGA、GRA、GA和共同序列法（CSM）在苗木低密度移栽路径规划中的优化效果和计算时间。分析了稀疏（32和50孔）和密集（72和128孔）苗盘的平均移栽路径，其中5%-20%的空孔随机定位。与CSM的平均优化率相比，GA、GGA和GRA对稀疏塔板的平均优化率分别为10%、8.7%和5.1%，而对密集塔板的平均优化率分别为13.9%、13.4%和11.8%。对于密相塔板，GGA和GA的标准差在不同的空孔处重叠。平均路径短的合适方法的性能排名的顺序是GA，GGA，和GRA。随着空孔数和塔板孔数的增加，GA相对于GGA的优势逐渐减小。路径规划的计算必须满足插秧机的实时操作要求。GA，GGA，和GRA消耗9.61，2.82，和0.02秒，分别为密集托盘的路径规划。与遗传算法和遗传算法相比，GGA具有路径寻优率高、计算时间短等综合性能，能有效地进行苗木低密度移栽路径规划。这种组合优化算法可以有类似的农业应用。(c)2017 Elsevier B. V.版权所有。
Automated transplanters perform repetitive low-density transplanting and replugging of seedlings in greenhouses to resolve the labor shortage problem and consistently produce seedlings. The work efficiency of transplanters can be improved by optimizing the transplanting paths of end effectors. In this study, a greedy genetic algorithm (GGA) was developed for path optimization. GGA combines the characteristics of a greedy algorithm (GRA) and a genetic algorithm (GA). The performances of GGA, GRA, GA, and the common sequence method (CSM) in the path planning for seedling low-density transplanting were compared in terms of their optimization effects and computation time. Average transplanting paths were analyzed for sparse (32 and 50 holes) and dense (72 and 128 holes) seedling trays with 5%-20% randomly located vacant holes. Compared with the average optimization ratio of CSM, those of GA, GGA and GRA were 10%, 8.7%, and 5.1%, respectively, for sparse trays, whereas 13.9%, 13.4%, and 11.8%, respectively, for dense trays. The standard deviations of GGA and GA overlapped in different vacant holes for the dense trays. The performance ranking of the suitable methods with short average paths was in the order of GA, GGA, and GRA. The superiority of GA over GGA gradually decreased with the increasing number of vacant and tray holes. The computation of path planning must satisfy the real-time operating requirement of transplanters. GA, GGA, and GRA consumed 9.61, 2.82, and 0.02 s, respectively, for the path planning for the dense trays. Compared with GA and GRA, GGA performed effectively in the path planning of seedling low-density transplanting due to its comprehensive performance derived from its path optimization ratio and low computation time cost. This combined optimization algorithm could have similar agricultural applications. (c) 2017 Elsevier B.V. All rights reserved.