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On the concerted design and scheduling of multiple resources for persistent UAV operations

机译:关于持续性无人机作战的多种资源的协同设计和调度

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A fleet of unmanned aerial vehicles (UAVs) supported by logistics infrastructure, such as automated service stations, may be capable of long-term persistent operations. Typically, two key stages in the deployment of such a system are resource selection and scheduling. Here, we endeavor to conduct both of these phases in concert for persistent UAV operations. We develop a mixed integer linear program (MILP) to formally describe this joint design and scheduling problem. The MILP allows UAVs to replenish their energy resources, and then return to service, using any of a number of candidate service station locations distributed throughout the field. The UAVs provide service to known deterministic customer space-time trajectories. There may be many of these customer missions occurring simultaneously in the time horizon. Each customer mission may be addressed by several UAVs. Multiple tasks may be conducted by each UAV between visits to the service stations. The MILP jointly determines the number and locations of resources (design) and their schedules to provide service to the customers. We then develop a modified receding horizon task assignment algorithm including the design problem (RHTAd) to address the computational complexity of the MILP. Numerical experiments assess the performance of RHTAd relative to the MILP solved via CPLEX. RHTAd is substantially faster with quite acceptable loss of optimality. As such, problems of much larger size can be addressed.
机译:由后勤基础设施(例如自动服务站)支持的无人飞行器(UAV)机队可能能够长期持续运行。通常,此类系统的部署中的两个关键阶段是资源选择和调度。在这里,我们努力将这两个阶段协调进行,以实现无人机的持续运行。我们开发了一个混合整数线性程序(MILP)来正式描述此联合设计和调度问题。 MILP允许无人机使用分布在整个现场的许多候选服务站位置中的任何一个来补充其能量资源,然后恢复服务。无人机为已知的确定性客户时空轨迹提供服务。这些客户任务可能在时间范围内同时发生。每个客户任务可以由数个无人机来解决。每个UAV在访问服务站之间可以执行多个任务。 MILP共同确定资源(设计)的数量和位置以及为客户提供服务的时间表。然后,我们开发了一种包含设计问题(RHTA d )的改进后退任务分配算法,以解决MILP的计算复杂性。数值实验评估了RHTA d 相对于通过CPLEX解决的MILP的性能。 RHTA d 显着更快,但损失了最佳的可接受性。这样,可以解决更大的问题。

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