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Research on Multi-UAV Loading Multi-type Sensors Cooperative Reconnaissance Task Planning Based on Genetic Algorithm

机译:基于遗传算法的多无人机装载多传感器协同侦察任务计划研究

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Unmanned Aerial Vehicle (UAV) has been playing an increasingly important role in modern military fields recently. The multi-UAV cooperative reconnaissance mission planning is one of the task allocation and resource scheduling problems in the field of multi-UAV co-operative control, which is full of challenges. In this paper, a multi-base, multi-target, multi-load and multi-UAV cooperative task model is established. Taking the actual battlefield situation into account, this paper built a confrontation scenario between the UAVs and radars. The objective function of the established model is the shortest route length of UAVs staying in the detection range of radars. This paper presented an improved genetic algorithm to address the problem scenario. The solving procedure consists of two steps. First of all, the route of UAVs that traverse targets within target group is considered as a Traveling Salesman Problem (TSP). Second, the route of UAVs that fly between different target groups is regarded as a Multiple Depot Vehicle Routing Problem (MDVRP). In addition, the working patterns of different sensors carried by UAVs are concerned. As a consequence, a more optimized route of UAVs is acquired. Finally, A simulated case is designed to verify the feasibility of our proposed algorithm.
机译:近来,无人机在现代军事领域中发挥着越来越重要的作用。多无人机协同侦察任务计划是多无人机协同控制领域的任务分配和资源调度问题之一,充满挑战。本文建立了一种多基地,多目标,多负荷,多无人机协同任务模型。考虑到战场的实际情况,本文建立了无人机与雷达的对抗方案。建立的模型的目标函数是无人机在雷达探测范围内的最短航程。本文提出了一种改进的遗传算法来解决问题场景。解决过程包括两个步骤。首先,在目标群内穿越目标的无人机的路线被视为旅行商问题(TSP)。其次,在不同目标群之间飞行的无人机的路线被视为多仓库车辆路由问题(MDVRP)。另外,还涉及无人机携带的不同传感器的工作模式。结果,获得了无人机的更优化的路线。最后,设计了一个仿真案例以验证我们提出的算法的可行性。

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