Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

中文翻译：

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使用GA优化器对无人高空伪卫星进行总体任务计划

无人驾驶飞机（UAV）在制图和监视地面活动方面越来越受青睐，部分原因是成本效益和轻型高分辨率成像传感器的可用性。太阳能高空伪卫星（HAPS）的最新进展拓宽了从广阔地面较低平流层开始的多种此类无人机的长距离遥感的未来使用范围。但是，为了提高任务的成功率和安全性，必须在任务规划期间考虑风对平台动力学的影响以及云覆盖对图像质量的影响。因此，本文提出了一种新的计划程序，该计划程序考虑了天气情况，确定了多个HAPS任务的时间层次分解。该计划程序由多目标进化算法（MOEA）支持，该算法根据精心定义的不同目标来确定可行的高级计划的最佳Pareto前沿，以考虑环境随时间变化的条件所带来的不确定性。同时，通过在MOEA中集成约束处理技术来确保计划的可行性。利用历史天气数据和现实的任务设置，我们分析了规划器在不同情况下的性能，并得出结论，该规划器能够确定不同条件下的总体良好解决方案。通过在MOEA中集成约束处理技术，可以确保计划的可行性。利用历史天气数据和现实的任务设置，我们分析了规划器在不同情况下的性能，并得出结论，该规划器能够确定不同条件下的总体良好解决方案。通过在MOEA中集成约束处理技术，可以确保计划的可行性。利用历史天气数据和现实的任务设置，我们分析了规划器在不同情况下的性能，并得出结论，该规划器能够确定不同条件下的总体良好解决方案。