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Adaptive Path Planning of UAVs for Delivering Delay-Sensitive Information to Ad-Hoc Nodes

机译:用于向Ad-Hoc节点传递时延敏感信息的无人机自适应路径规划

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We consider the problem of path planning using multiple UAVs as message ferries to deliver delay-sensitive information in a catastrophic disaster scenario. Our main goal is to find the optimal paths of UAVs to maximize the number of nodes that can successfully be serviced within each designated packet deadline. At the same time, we want to reduce total travel time for visiting over a virtual grid topology. We propose a distributed path planning algorithm that determines the next visit grid point based on a weighted sum of travel time and delivery deadline. Together with path planning, we incorporate a task division mechanism that collaboratively distributes the unvisited grid points with other UAVs so that the entire travel time can substantially be reduced. Simulation results demonstrate that our distributed path planning algorithm mixed with task division outperforms all baseline counterpart algorithms in terms of on-time service node rate and total travel time.
机译:我们考虑在灾难性灾难情况下使用多个UAV作为消息传递来传递延迟敏感信息的路径规划问题。我们的主要目标是找到无人飞行器的最佳路径,以最大程度地增加可以在每个指定数据包期限内成功服务的节点数量。同时,我们希望减少通过虚拟网格拓扑进行访问的总行程时间。我们提出了一种分布式路径规划算法,该算法基于旅行时间和交付期限的加权总和来确定下一个访问网格点。与路径规划一起,我们采用了一种任务划分机制,该机制与其他UAV协作地分配了未访问的网格点,从而可以大大减少整个旅行时间。仿真结果表明,在按时服务节点速率和总旅行时间方面,结合任务划分的分布式路径规划算法优于所有基准配对算法。

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