多机协同作业全覆盖路径规划  

Full Coverage Path Planning for Multi Machine Collaborative Work

作　　者：金宝龙 夏长高[1] 韩江义[1] Jin Baolong;Xia Changgao;Han Jiangyi(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江2120133

出　　处：《农机化研究》2024年第12期28-33,共6页Journal of Agricultural Mechanization Research

基　　金：苏北科技专项-先导性项目(SZ-YC202165);江苏省重点研发计划项目(BE2018343-1)。

摘　　要：多机协同作业能够提升作业效率,节约作业时间,减少了农作物因收获不及时导致的粮食浪费。为此,以总的非工作距离和最长单车行驶距离为目标函数,建立虚拟的农田模型,将多机协同作业全覆盖路径规划问题转化为VRP问题,并使用改进的蚁群算法求解。仿真结果表明:改进后的算法有效;对比遗传算法和传统的蚁群算法,改进后的算法平均目标函数值分别降低了25.0%和11.25%;对比模拟退火算法,改进后的算法平均目标函数值降低了1.5%,算法稳定性更好,适用于更大规模农田的多机协同作业全覆盖路径规划问题求解。Multi machine collaborative operation can improve operation efficiency,save operation time and reduce food waste caused by untimely harvest of crops.In this paper,we take the total non working distance and the longest single vehicle traveling distance as the objective function,and transform the multi machine collaborative full coverage path planning problem into a VRP problem,which is solved using an improved ant colony algorithm.The simulation results show that the improved algorithm is effective.Compared with genetic algorithm and traditional ant colony algorithm,our improved algorithm reduces the average objective function value by 24.81%and 12.22%respectively;Compared with simulated annealing algorithm,our improved algorithm reduces the average objective function value by 2.0%,moreover,the improved algorithm has better stability and is suitable for solving the full coverage path planning problem of multi machine cooperative operation in larger farmland.

关 键 词：多机协同 全覆盖路径规划 蚁群算法 优化算法 

分 类 号：S232.5[农业科学—农业机械化工程]