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在单操作员监督控制多无人机进行搜索任务的场景下,为解决操作员的静态注意力分配问题,建立基于任务排序的静态注意力分配模型,目的是将合适的任务在合适的时间分配给操作员处理,实现任务的综合回报最大化。该模型对静态任务队列进行排序,同时设定每一个任务的处理时长和任务执行后操作员的休息时长。采用MATLAB仿真实验验证,利用动态规划和免疫算法进行求解。实验结果表明,在静态任务队列的注意力分配中此模型获得的综合回报,大于基于先进先出原则的注意力分配方法获得的综合回报。
In order to solve the problem of static attentiveness of operators under the condition of single operator supervised and controlled by multi-drone, a static attentiveness assignment model based on task sequencing is established to allocate suitable tasks at the right time To the operator to maximize the overall return on mission. The model sorts static task queues and also sets the processing time for each task and the operator’s rest time after the task is executed. Using MATLAB simulation experiments, the use of dynamic programming and immune algorithm to solve. The experimental results show that the overall return obtained by this model in the static task queue’s attention distribution is greater than the comprehensive return obtained by the first-in-first-based attention distribution method.