Task assignment is a key problem in multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. In order to realize the management of multiple agricultural machinery cooperation, studies on task assignment based on the improved ant colony algorithm have been conducted under the farmland operation environment. First, a task assignment model of multiple agricultural machinery cooperation was established by combining dynamic and static task assignments. Then, according to the task assignment model, the task assignment process based on the improved ant colony algorithm was established while considering the match between supply and demand, the operation capacity of the agricultural machinery, and the operation cycle and path cost. Finally, the dynamic and static task assignments of multiple agricultural machinery cooperation based on the improved ant colony algorithm were simulated on MATLAB. Taking the crop harvesting experiment as an example, according to the actual farmland location information of the Zhuozhou Experimental Farm, the different (agricultural machinery, task) combinations were set, and the task assignment results were compared and analyzed. Results showed that the path costs of harvester and grain transporters were reduced by 51.27% and 22.00% respectively, When the quantities of tasks were set to 11, indicating that the improved ant colony algorithm can effectively reduce the path cost. When the quantities of tasks were set to 5, 11, 16 and 22, the average operation cycles were shortened by 67.32%, 37.50%, 55.95%, and 56.37% respectively. The problem of “nearby” in the task assignment was solved to a certain extent, the overload of some agricultural machinery and the idle of other agricultural machinery were avoided, and the operation cycle was shortened. At the same time, based on the static task assignment, the dynamic task assignment was realized in the two scenarios of new tasks and malfunctioning harvesters, thus laying a foundation for further solving the scheduling management problem of multiple agricultural machinery cooperation under a complex farmland operation environment.

任务分配是多机协作导航中的关键问题。在区域性农田经营的背景下，多个农业机械通常需要一起完成多个任务。为了实现多种农机合作的管理，在农田作业环境下，基于改进的蚁群算法进行任务分配研究。首先，结合动态和静态任务分配，建立了多种农机合作的任务分配模型。然后，根据任务分配模型，在考虑供需匹配，农业机械的运行能力以及运行周期和路径成本的基础上，建立了基于改进蚁群算法的任务分配过程。最后，在MATLAB上对基于改进蚁群算法的多种农机合作动态和静态任务分配进行了仿真。以农作物收成实验为例，根据the州实验农场的实际耕地位置信息，设置了不同的（农机，任务）组合，并对任务分配结果进行了比较分析。结果表明，当任务数量设置为11时，收割机和谷物运输机的路径成本分别降低了51.27％和22.00％，表明改进的蚁群算法可以有效地降低路径成本。将任务数量设置为5、11、16和22时，平均操作周期分别缩短了67.32％，37.50％，55.95％和56.37％。任务分配中的“附近”问题得到了一定程度的解决，避免了某些农机超载和其他农机闲置的情况，缩短了作业周期。同时，在静态任务分配的基础上，在新任务和收割机故障两种情况下实现了动态任务分配，为进一步解决复杂农田经营下多种农机合作的调度管理问题奠定了基础。环境。