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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Multiperiod Coverage Path Planning and Scheduling for Airborne Surveillance
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Multiperiod Coverage Path Planning and Scheduling for Airborne Surveillance

机译：机载监视的多周期覆盖路径计划和调度

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摘要

In this paper, optimal surveillance mission plans are developed to cover disjoint areas of interest (AOIs) over an extended time horizon using multiple aerial vehicles. AOIs to be covered are divided into a number of cells. To promptly update information collected from AOIs and to ensure persistent surveillance, each cell is to be revisited within a time slot. Joint path planning and temporal scheduling is formulated as a combinatorial optimization with the proposal of novel objective functions: 1) maximizing the minimum number of nonrepeatedly covered cells in a sliding-window fashion and 2) maximizing the total number of covered cells in the mission plan. A multiobjective evolutionary algorithm (MOEA) with a specific chromosome representation and custom genetic operators, in which the constraint that each cell be revisited within a time slot is transformed into the third objective to handle infeasibility, is developed. The initial single-period paths are generated by solving a series of orienteering problems. The initial population is obtained by connecting these single-period paths and selecting the take-off time for each flight. Three mutation moves are proposed to enable revisiting in a single-period path and rescheduling of take-off time. The solutions converge in the MOEA and are selected by a weighted-sum model according to user preferences in decision making. Simulation results on different mission scenarios and different criteria show the superiority of the proposed algorithm. The algorithm is done offline ahead of the missions and requires modest computational resources.
机译：在本文中，制定了最佳的监视任务计划，以涵盖使用多架飞行器在较长时间范围内不相交的感兴趣区域（AOI）。要涵盖的AOI分为多个单元。为了及时更新从AOI收集的信息并确保持续监视，应在一个时隙内重新访问每个单元。联合路径规划和时间调度被公式化为组合优化，并提出了新颖的目标函数：1）以滑动窗口的方式最大化未重复覆盖的单元的最小数量； 2）在任务计划中最大化覆盖的单元的总数。开发了具有特定染色体表示和自定义遗传算子的多目标进化算法（MOEA），其中将每个细胞在一个时隙内重新访问的约束转换为处理不可行的第三个目标。初始单周期路径是通过解决一系列定向运动问题而生成的。通过连接这些单周期路径并选择每次飞行的起飞时间来获得初始人口。提出了三个突变动作，以允许在单周期路径中重新访问并重新安排起飞时间。这些解决方案在MOEA中收敛，并根据决策中的用户偏好通过加权和模型进行选择。在不同任务场景和不同标准下的仿真结果表明了该算法的优越性。该算法是在任务执行之前离线完成的，需要适度的计算资源。

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