基于蚁群算法的植保无人机任务分配优化研究  

Research on optimization of plant protection drone task assignment based on ant colony algorithm

作　　者：杜芳芳[1] 巨玉祥 李卓 王苗苗[1] 常娜娜[1] DU Fangfang;JU Yuxiang;LI Zhuo;WANG Miaomiao;CHANG Nana(Lanzhou Resources&Environment Voc-Tech College,Lanzhou 730000,China;Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州资源环境职业技术学院,甘肃兰州730000 [2]兰州交通大学,甘肃兰州730070

出　　处：《交通科技与经济》2020年第5期17-20,44,共5页Technology & Economy in Areas of Communications

基　　金：甘肃省陇原青年创新创业人才项目(2017-121-23)。

摘　　要：针对植保无人机在多个小面积农产品种植区域农药喷洒任务分配优化问题,考虑农药喷洒时无人机任务分配对工作效率的影响,建立基于农药喷洒总收益最大的优化模型。采用蚁群算法求解上述模型,并设置算法中的编码、交叉和变异方式。通过算例分析,设置试验条件,试验结果表明,文中所构建的模型及算法对于无人机农药喷洒任务分配优化决策具有很好的参考价值。求解得到的植保无人机作业次序缩短了植保无人机的航行距离,提高了农药喷洒的工作效率,节省了人力物力,使喷洒作业更加方便高效。An optimization model based on pesticide spraying with the greatest total return is established to optimize the assignment of pesticide spraying tasks for plant protection drones in multiple small agricultural product growing areas by considering the impact of drone task allocation on work efficiency during pesticide spraying.Therefore,this model is used to solve ant colony algorithm,set the encoding and crossover and mutation methods in the algorithm.The test result shows that the model and algorithm constructed in this paper provide a good reference value for the optimal decision-making of the drone pesticide spraying task assignment through analyzing examples and setting test conditions.The solved plant protection drone operation sequence shortens the plant protection drone's navigation distance,maximizes the efficiency of pesticide spraying,and saves manpower and material resources,which enables the operation to be more convenient and efficient.

关 键 词：蚁群算法 植保无人机 农药喷洒 任务收益 

分 类 号：S252.3[农业科学—农业机械化工程]